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Commitment or discretion? An empirical investigation of monetary policy preferences in China
Journal Pre-proof Commitment or discretion? An empirical investigation of monetary policy preferences in China Ding Liu, Yue Zhang, Weihong Sun PII:
S0264-9993(19)30966-6
DOI:
https://doi.org/10.1016/j.econmod.2019.11.022
Reference:
ECMODE 5080
To appear in:
Economic Modelling
Received Date: 2 July 2019 Revised Date:
19 November 2019
Accepted Date: 21 November 2019
Please cite this article as: Liu, D., Zhang, Y., Sun, W., Commitment or discretion? An empirical investigation of monetary policy preferences in China, Economic Modelling (2019), doi: https:// doi.org/10.1016/j.econmod.2019.11.022. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
Commitment or Discretion? An Empirical Investigation of Monetary Policy Preferences in China Ding Liua, Yue Zhangb, Weihong Sunc,∗ aSchool
of Economics, Room 1001, Gezhi Building, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, P. R. China. bSchool of Finance, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, P. R. China. cThe West Center for Economics Research, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, P. R. China.
Abstract This paper offers a first attempt to estimate the policy preferences of China’s central bank by confronting a small-scale microfounded New Keynesian model in which monetary policy is described by commitment or discretion with the Chinese macroeconomic data over the period from 1992Q2 to 2017Q4. Bayesian model comparison reveals that the data favor discretionary monetary policy. Estimates of the loss function weights under both cases show that the leading policy goal is price stability, followed by output stability and then interest rate smoothing. Finally, through counterfactual analyses we assess how macroeconomic outcomes might improve, had the Chinese central bank been able to commit. These findings shed new light on the opaque Chinese monetary policy, and are robust to subsample analysis. Keywords: Commitment, Discretion, New Keynesian Model, Optimal Monetary Policy, China JEL: E52, E58, E61 We thank Gregory Givens, Juan Medina Guzman and Junior Maih for constructive discussions about optimal monetary policy estimation problem, and thank the editor Sushanta Mallick and two anonymous referees for helpful suggestions. In particular, Junior Maih kindly offers some guidance on using the RISE toolbox to estimate the optimal policy problem. We also thank the participants of 2019 IEF Conference held in Nanjing Audit University for comments. However, all errors remain our own. Ding Liu would like to gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 71601160) and from the CICFDSC at SWUFE (Grant No. JRXT201905). ∗ Corresponding author
Email addresses: [email protected] (Ding Liu), [email protected] (Yue Zhang), [email protected] (Weihong Sun)
1. Introduction Although China has become a major contributor to the world economy, its monetary policy governed by the People’s Bank of China (PBoC) is still opaque to outside observers. The former governor Xiaochuan Zhou explains that the PBoC has multiple policy objectives, ranging from short-term to long-term goals (Zhou, 2016). In the short run, the PBoC aims to achieve price stability, economic growth, full employment, and broadly balanced payments, while in the longer run it also considers implementing financial reforms and opening up, in order to develop domestic financial markets. These objectives are embodied in “the Law of the People’s Republic of China on the PBoC” passed in 1995 and amended in 2003. However, the PBoC has never explicitly articulated the relative importance of these objectives. This vagueness leads to distinct interpretations. For example, Chen et al. (2016) argues that output growth is the overarching goal, while the former governor in a written speech prepared for the 2012 Per Jacobsson Lecture points out that the PBoC puts price stability in the most important position. It is imperative to figure out the PBoC’s objective function, since Chinese monetary policy has spillover effects on both developing and developed economies, see Vespignani (2015), Kang et al. (2016), Lombardi et al. (2018) and Chiang et al. (2019) for examples. Meanwhile, a growing number of researches have used New Keynesian dynamic stochastic general equilibrium (DSGE) models to analyze Chinese monetary policy in recent years. This line of literature typically assumes a simple Taylor (1993) or McCallum (1988)type rule to characterize monetary policy, see Zhang (2009), Chang et al. (2015), Li and Liu (2017) and among others. This practice, however, is debatable. The reason is that monetary policy instrument rules build on the premise that the monetary authority is able to make credible commitments, while the institutional constraints make it difficult for the PBoC to commit. At the beginning of each year, China’s central government specifies targets for GDP growth, inflation rate and other economic indicators. All government agencies including the 2
PBoC are supposed to accomplish these overarching targets. In fact, the State Council has legal authority in guiding the PBoC to formulate and implement monetary policy, since achieving the multiple objectives requires the PBoC to coordinate and work with other government units. This implies that it is hard for the PBoC to make independent monetary policy decisions. Hence, the conduct of monetary policy in China is distinct from the standard practice in advanced economies, which make researchers wonder which of the two optimal policy concepts - commitment and discretion - better describes the PBoC’s monetary policy preferences consistent with the data.1 What can the data tell us about the PBoC’s preferences and its policy objective function? How does the PBoC trade off the different policy goals in response to economic shocks? This paper attempts to shed some light on these issues. For this purpose, we jointly estimate the parameters describing the PBoC’s objective and its policy preferences by confronting a small-scale microfounded New Keynesian DSGE model with the Chinese macroeconomic data. This paper assumes that the PBoC sets monetary policy optimally under commitment or discretion, and then uses Bayesian methods with quarterly Chinese data to simultaneously estimate the parameters related to the objective function which determines how the PBoC trades off alternative policy goals when responding to exogenous shocks, and the parameters in the structural equations. Finally, through counterfactual analyses we assess how macroeconomic outcomes might improve had the Chinese central bank been able to commit. Three key findings emerge. First, Bayesian model comparison reveals that the policy preferences of PBoC is best characterized as discretion. This result is consistent with the fact that the PBoC does not enjoy either goal or instrument independence. As mentioned earlier, the policy goals and key monetary policy instruments (say the benchmark lending and deposit rates, and the required reserve ratio) are set by the State Council. To achieve the multiple mandates, it is difficult for the PBoC to follow any prescribed plans when
1
See Mallick and Sousa (2012), Sun (2015) and among others for related discussions about monetary policy features in China and other emerging economies.
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responding to economic shocks. Policy decisions, instead, are made by solving a sequential optimization problem, without taking into account past policy promises. Hence, actions taken by the PBoC to fulfill its multiple objectives barely affect private-sector expectation in any credible way. Second, estimates of the weights associated with the loss function under both commitment and discretion show that the PBoC cares more about inflation stability than stabilizing output and smoothing interest rate. This finding supports the former governor’s view about the PBoC’s policy preferences. The impulse response functions also reveal that the PBoC gives the highest priority to price stability. Third, using counterfactual simulations, we show that had the PBoC been able to commit, the Chinese economy would have enjoyed substantially lower inflation volatility: the predicted standard deviation of inflation is 0.89% compared to the actual value of 5.50%. The volatility of nominal interest rate, nevertheless, increases significantly from the actual value of 3.64% to the predicted value of 5.40%. Our paper contributes to the literature in two respects. First, our investigation is a useful addition to the empirical optimal monetary policy literature. As the largest emerging economy, China is becoming more of a major influence on the world economy, hence it is important to better understand its monetary policy. There is a small but growing empirical literature about optimal monetary policy, but we believe that the current paper is the first work studying China’s monetary policy preferences from this perspective. The majority of that literature has focused either on commitment or discretion, without comparing the two modes of optimal monetary policy. For example, Dennis (2004), Söderström et al. (2005) and Castelnuovo (2006) empirically study New Keynesian models under discretion, while Salemi (2006), Ilbas (2010), and Ilbas (2012) estimate New Keynesian models under commitment for the US economy. There are a few exceptions. Givens (2012) uses maximum likelihood method to estimate a reduced-form New Keynesian model under both commitment and discretion. He concludes that discretionary policy better fits the US data during 1982Q1-2008Q4. Coroneo et al. (2018) find the same result employing the method of moment inequalities. Estimating 4
a simple New Keynesian model for UK with Bayesian methods, Kirsanova and le Rouxb (2013) uncover evidences in support of discretionary monetary and fiscal policy. For the US economy, Matthes (2015) estimates a simple reduced-form New Keynesian model where private sectors update their beliefs about whether the monetary authority acts under discretion or commitment, without modeling the central bank’s actual behavior. In contrast, Debortoli and Lakdawala (2016) perform Bayesian estimation based on a medium-scale New Keynesian model allowing for regime-switching deviations from commitment policies. For the US economy during 1966Q1-2012Q2, the data do not support either commitment or discretion, but are in favor of a generalized case called loose commitment. Applying the same estimation method to a small-scale New Keynesian model augmented with regime-switching volatility and policy parameters, however, Chen et al. (2017b) find that the US monetary policy during 1961Q1-2008Q3 is best characterized as being discretionary. Chen et al. (2017a) apply the same methodology to the Euro area and conclude that the data prefer discretion. For emerging economies, Gómez et al. (2019) use Bayesian methods and small open New Keynesian models to uncover the monetary policy objectives of four Latin American economies under both commitment and discretion. They find that the central banks in these four countries act discretionarily and have high preferences for stabilizing inflation as well as smoothing interest rate. Our results are largely consistent with their findings. Relative to these papers, this study is the first to identify the central bank’s preferences for China. Second, our examination of the data of the ultimate target variables like output and inflation can help us understand the PBoC’s systematic behavior. Discussions on simple or optimal policy rules a la Taylor (1993) or McCallum (1988) in China are not likely to be informative without knowledge about whether the central bank acts under commitment at all. Our empirical analysis suggests that ongoing Chinese monetary policy reforms should grant operational independence to the PBoC and limit the number of policy objectives. In this way, the PBoC’s credibility can be enhanced, which is not just good for domestic
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macroeconomic stability, but also is helpful for trading partners to deal with possible policy spillover from China. We structure the paper into six sections. Section 2 gives a sketch of the theoretical framework. Section 3 specifies the central bank’s objective function and alternative descriptions of optimal monetary policy. Section 4 describes the empirical strategy, including solution methods, data, estimation methods and priors. Section 5 presents our empirical findings. Section 6 concludes. 2. The linearized model The economy consists of households, a perfectly competitive final good sector, a monopolistically competitive intermediate good sector, and the government. To ensure data coherence, the model is obtained by augmenting the prototypical New Keynesian model with propagation mechanisms including consumption habits and price indexation. However, we deliberately keep it relatively simple so that the investigation is transparent. We summarize the linearized model here, and relegate the detailed microfounded model in the online Appendix C. Before outlining the log-linearized model, we define hatted variable ≡ /̅ as the log deviation of variable, , from its steady state, ̅ . The representative household’s optimization gives rise to the consumption Euler equation, 1 = − − − − + #1
and the labor supply decision, σ + = "! − #̂ #2
where = 1 − & ' − & ' is habits-adjusted consumption, denotes nominal
interest rate, stands for inflation, represents a technology shock, is a preference shock, denotes output, "! is real marginal cost, and #̂ = (̅(̂ /1 − (̅ performs a costpush shock generated by variations in the labor income tax rate. The parameter 1/σ 6
denotes the intertemporal elasticity of substitution, ϕ is the inverse of the Frisch elasticity of labor supply, and η measures the degree of external consumption habits. When η = 0, equation (1) contains no backward-looking components and degrades to the standard consumption Euler equation. With external habits, both past and expected future consumption affects current consumption (which equals to output here). As a result, the elasticity of consumption with respect to real interest depends not only on σ, but also on η. Given σ, the effect of real interest rate on consumption tends to be lower for higher η. The firms’ optimization decisions give rise to a hybrid New Keynesian Phillips curve (NKPC), =
) * + + + "! #3
1 + )* 1 + )* '
where the parameter β denotes the discount factor of households, γ measures the degree of price indexation, α represents the Calvo (1983) probability of fixed price, and + ≡
1 − -) 1 − - ⁄-1 + )* .
When γ = 0, equation (3) contains no backward-looking components and degrades to the standard NKPC. With partial price indexation, past inflation along with its expected future counterpart and current real marginal cost determine the present inflation. In this way, we introduce a backward looking component into the inflation process. The technology, preference and cost-push shocks follow AR(1) processes: = /0 ' + 10, #4
= /5 ' + 15, #5
#̂ = /7 #̂ ' + 17, #6
where 10, , 15, and 17, are independent and identically distributed N0, 0; , N0, 5; and N0, 7; , respectively.
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The central bank treats as the policy constraints the equations given above, and then chooses the nominal interest rate as its policy instrument to minimize a loss function considered in the following section. 3. The central bank’s problem This section describes the central bank’s objective function and its optimal policy problem. 3.1. The central bank’s objective function To estimate optimal policy regimes, we need to define the central bank’s objective function. An obvious benchmark would be the microfounded welfare function which is a second-order approximation to the expected discounted utility of the representative households populating our economy, as shown in Appendix D. However, it is too restrictive, see Chen et al. (2017a, b). Therefore, we adopt a simpler specification as Debortoli and Lakdawala (2016) do.2 We consider the following objective function for estimation, D
; L = = > ) ?; + @A ; + @B − ' C #7
E=
where , and represent the log deviations of inflation, output and nominal interest rate from their steady states, respectively. We normalize the weight on inflation stabilization to be unity, so the parameters @A and @B capture the relative concerns for stabilizing output and smoothing interest rates. This form of loss function reflects the PBoC’s multiple objectives including price stability, economic growth and financial stability. We include an interest rate smoothing term to capture financial stability concerns, see Ilbas (2010), Givens (2012), Debortoli and Lakdawala (2016) and Chen et al. (2017a) for examples.
2
As a robustness check, we consider the case with the microfounded welfare function, but freely estimate the coefficients on the various quadratic terms. The main results are the same under this alternative specification. The details of this exercise are available upon request.
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3.2. Optimal monetary policy We study two standard cases of optimal monetary policy, that is, timeless commitment and discretion. Under both cases, the central bank chooses interest rates as its monetary policy instrument to minimize the loss function (7), subject to the private sector behavior equations (1)-(3), and the law of motion for the exogenous shocks (4)-(6). However, they differ considerably in the extent to which the monetary authority has the ability to affect the private sector’s expectations, since timeless commitment gives rise to some historydependent components in monetary policy, which is absent under discretion. Under timeless commitment as first proposed by Woodford (1999), the monetary authority is able to make credible commitments in advance about future policy actions. In D
current context, the monetary authority can choose a complete policy plan, G HE= , in order to minimize the loss function (7), subject to the sequence of equilibrium constraints (1)-(6), and never re-optimizes. The commitment solution is characterized by equation (9) and (10). When solving for the optimal interest rate path, the monetary authority will take into consideration the effects of its policy plans on forward-looking variables so that it can influence the private-sector expectations in a manner that enhances the output-inflation trade-off. In contrast, a discretionary monetary authority acts sequentially without committing itself to any policy plans. Equations (11) and (12) describe the discretionary solution. When solving the sequential optimization problem, the central bank adjusts policy only based on the present and future state of the economy, without considering any past commitments. This implies that it takes future variables as given in sequential reoptimization. As a result, the central bank cannot harness private-sector expectations to achieve its policy goals.
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4. Empirical strategy This section first sketches out the solution methods, then describes the estimation strategy including data, measurement equations, Bayesian methods and the specification of priors. 4.1. Solution Methods To compute optimal monetary policies, we can stack the constraints (1)-(6) into the following compact form, I
J K J M = K I M + K; + I N M 1 #8
L K= L 0;× S
where J ≡ Q , , #̂ , ' , ' , ' R are the state variables in period T, L ≡ U , VS S
are the forward-looking variables in period t, and 1 ≡ Q10, , 15, , 17, R are the innovations realized in period t with covariance matrix Σ. Matrices A0, A1, A2 and A3 consist of the structural parameters and their combinations. We can follow the methods in Söderlind (1999) or Dennis (2007) to compute the optimal commitment policy in the timeless perspective. The solution under commitment has the following form I and
J J X1 M = X I M + I ; M #9
0;× W W L J I M = XN I M #10
W
where W are the Lagrange multipliers associated with the forward-looking constraints in the lower block of (8). These multipliers capture the effect of past and current commitments on the private-sector expectations, which suggests that timeless commitment imports a history-dependence into monetary policy. The elements in matrices C1, C2 and C3 are given by the structural parameters and their combinations. The algorithm proposed by Söderlind (1999) or Dennis (2007) can be used to find the optimal discretionary policy. We can show that the solution under discretion is characterized by 10
J = Z J + Z; 1 #11
and
L I M = ZN J #12
which says that the discretionary policy is solely determined by the current and future state of the economy. The structural parameters and their combinations determine the elements in matrices D1, D2 and D3. Comparing the solution under commitment (9) -(10) and the counterpart under discretion (11)-(12), we should note an important difference that the former is historydependent, while the latter is purely forward-looking. This distinction will help us identify which mode of optimal policy fit better the data, since different cross-equation restrictions are imposed by commitment and discretion. The solution derived for commitment or discretion described above gives the state transition equation of a state-space model. The measurement equations are specified in the following subsection. 4.2. Data and Estimation Methods The estimation adopts three quarterly Chinese series as observables, including growth rate of real output (∆RGDPt), annualized nominal interest rate (INTt) and annualized inflation rate (INFt).3 The sample covers the period from 1992:Q2 to 2017:Q4.4 All data except interest rate are seasonally adjusted. The main data sources are the quarterly data constructed by Chang et al. (2016) and the CEIC database. Figure B.1 plots the series for output growth, inflation, and nominal interest rate for the sample period. Accordingly, the measurement equations are specified as:
3
The difference of logged real GDP, scaled by 100, returns the growth rate of real output. Annualized nominal interest rate is the quarterly average of the 7-day China interbank offered rate. Annualized inflation rate is the difference of logged GDP deflator, multiplied by 400. 4 As a robustness check, we also conduct a subsample estimation in the online appendix. Our main results described below are still valid.
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∆\Z] = − ' + + ^ _`a = 4 + 4 ^ _`b = 4 + 4^ + 4 ^ + 4c ^ where ^ ≡ 100d − 1 , ^ ≡ 100d − 1 and c ^ ≡ 1001⁄) − 1 represent quarterly growth rate of real GDP, inflation rate and real interest rates in the steady state, respectively. The measurement equations described here, together with the state transition equation corresponding to the solution under commitment or discretion, yield a statespace model to be estimated. Given the distributions of shocks, we can use the Kalman filter to calculate the likelihood of the model solution, and then estimate the model parameters using Bayesian methods.5More specifically, we adopt a Markov chain Monte Carlo (MCMC) algorithm to estimate posterior distributions. We first find the posterior modes numerically using some optimization routine. To sample from the posterior distribution, we employ a random-walk Metropolis-Hastings (RWMH) algorithm. A target acceptance rate of about 30% is achieved by scaling the variance of the proposal density. We run two separate MCMC chains with alternative starting values. For each chain, we make 0.5 million draws but burn in the first 0.1 million draws. To save space, we thin the MCMC chains by keeping every 10th draw from the remaining draws. We adopt the potential scale reduction factor (PSRF) by Gelman et al. (1992) to assess convergence of the posterior simulation. To compare model fit, we adopt the modified harmonic mean estimator proposed by Geweke (1999) to estimate the log marginal data density.6
5
Please refer to An and Schorfheide (2007), Herbst and Schorfheide (2015) and among others for detailed instructions on implementing the Bayesian methods in estimating DSGE models. 6 We conduct the Bayesian estimation using the RISE toolbox written by Junior Maih. RISE can be accessed via https://github.com/jmaih/RISE_toolbox.
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4.3. Prior Distribution Table A.1 presents the prior distributions. We follow Smets and Wouters (2007) and Chen et al. (2017b) to set priors for the structural parameters. The relative weights associated with the central bank’s objective function, @A and @B , are assumed to be distributed following beta distributions with mean 0.5 and standard deviation 0.15. We set the prior means of real output growth ^ , inflation πQ and real interest rates c ^ in the steady state to be largely consistent with their sample averages. The discount factor, β = 1 + c ^ ⁄100 ' , is determined by c ^ . Finally, we assume that the persistence of the shocks has a beta distribution with mean 0.5 and standard deviation 0.15, and that the standard deviation of these shocks follows a diffuse prior, specified as an inverse gamma distribution with mean 1 and standard deviation 5. 5. Empirical results This section reports the empirical results. To begin with, we present the posterior estimates of the central bank preferences and the model structural parameters. Then we compare the two optimal monetary policy regimes and assess commitment or discretion fitting best the data. Finally, we conduct some counterfactual exercises. 5.1. Posterior estimates Table A.2 summarizes the posterior means and the 90% confidence intervals. The first column gives the results under discretion which is the best-fitting model. The posterior means are largely consistent with the literature: an elasticity of intertemporal substitution, σ = 2.993; a sizeable estimate of habit persistence, η = 0.950; a labor supply elasticity, ' = 1⁄2.186; an estimate of price stickiness, α = 0.848, suggesting that the duration of price contracts is about one and a half years; and a rather limited degree of price indexation, γ = 0.149. Moving from discretion to timeless commitment, the structural parameter estimates differ to various degrees. The parameter estimates of labor supply elasticity and price 13
stickiness remain largely the same, while the intertemporal elasticity of substitution, habit persistence and the degree of price indexation falls to σ = 2.609, η = 0.802 and γ = 0.101, respectively. The estimates of standard deviation and persistence for the productivity shock under discretion and commitment are almost identical, but the estimates for the preference and cost-push shocks differ significantly. Under discretion, the estimated standard deviation and persistence of the preference shock are 5 = 2.766 and /5 = 0.905 , respectively, while under commitment these estimates rises substantially to 5 = 4.534 and /5 = 0.938. These differences are more evident for the cost-push shock #̂ . Moving from discretion to commitment, its estimated standard deviation σµ jumps up from 4.470 to 6.978, and while its persistence ρµ rises from 0.823 to 0.969. As explained in Chen et al. (2017a, b), the differences in the parameter estimates across optimal policy regimes reveal the need to create a relevant trade-off to account for the observed fluctuations in output, inflation and interest rates. In the standard New Keynesian model, the monetary authority faces an output-inflation trade-off only when cost-push shocks hit the economy, see Woodford (2003). As in Leith et al. (2012), the presence of external habits in our model breaks this “divine coincidence”, which implies that meaningful policy trade-offs also arise from technology and preference shocks. Under discretion, history-dependence is absent due to the inability to commit, hence inflation inertia carries more weight in fitting the data. When the central bank can commit, monetary policy does not suffer from the stabilization bias problem, and as a result it allows higher variance and persistence of cost-push shocks. More volatile and persistent cost-push shocks help the commitment model generate a significant policy trade-off which in turn explains the actual output and inflation dynamics. As we make it clear later, however, the data reject commitment, since it excessively stabilizes the economy, especially in terms of inflation volatility. Regarding the loss function, estimates of weights @A and @B are substantial, but higher under timeless commitment, relative to those under discretion. As we explain in detail 14
below, higher weights on interest rate smoothing and output stabilization are needed for timeless commitment to fit the data better. Under both cases, the dominant policy objective is inflation stability, followed by output stability and then interest rate smoothing. These estimation results help us understand the targets and preferences of the Chinese monetary policy, since the PBoC has never explicitly articulated the relative importance of its objectives. To further understand the differences of parameter estimates under discretion and commitment, Figure B.2 and B.3 compare the impulse responses under the two versions of optimal monetary policy. Figure B.2 graphs the responses of nominal interest rate , inflation and output gap to productivity shock (left column), preference shock
(middle column) and cost-push shock #̂ (right column). The counterfactual takes the
estimated parameter values under discretion, but assumes the monetary authority has access to a timeless commitment technology. Two findings emerge. First, all three shocks produce hump-shaped responses in output gap and inflation, due to the presence of habits externality. Second, other things being equal, switching from discretion to commitment the central bank has an incentive to react more aggressively to the shocks, since a relatively small increase in interest rate volatility can trade for a big decrease in inflation volatility. By committing to sustain higher interest rates, the policy maker can successfully reduce current alteration in output gap and inflation, via effectively depressing their expected future values. The response of output to a cost-push shock is the only exception, since high interest rates produce a prolonged negative output gap. One implication of these results is that the timeless commitment involves a data-inconsistent level of interest rate volatility. A higher weight on interest rate smoothing is needed to alleviate this inconsistency for empirical fit, as shown in Table A.2. Figure B.3 plots impulse responses for the estimated models under commitment and discretionary monetary policy. We should note that now policy regimes and variations in other estimated parameters together drive responses. Due to a larger estimate of @B under commitment as shown in Table A.2, the policy responses to the preference (or demand) 15
and cost-push (or supply) shocks of the same size strive to follow those under discretion. Stronger interest rate smoothing incentive, however, causes more volatile output and inflation. For example, the peak response of inflation to the cost-push shock is twice as large for commitment relative to the corresponding result in Figure B.2. Another message from Figure B.3 is that timeless commitment relies heavily on the preference and cost-push shocks to match the data, particularly inflation volatility. For example, the response of output gap to these shocks are deeper and more persistent under commitment, which is in line with the estimated shock parameters as shown in Table A.2. 5.2. Model comparison We compare the empirical fit of the two modes of optimal monetary policy based on two criteria in this subsection. First, we calculate the log marginal data densities using the harmonic mean estimator proposed by Geweke (1999). Second, we simulate the two models and assess which model specification can produce standard deviations close to the data moments. The last row of Table A.2 presents the log marginal data densities and the associated Bayes factor for discretion and timeless commitment. These two model selection devices suggest that data decisively favor discretion over commitment. For this reason, we can conclude that the PBoC does not attempt to commit in advance to future monetary policy actions. As a second comparison, Table A.3 reports the standard deviations of inflation, output growth and nominal interest rate based on the data and the estimated models. Discretion performs better in reproducing the business cycle features. In particular, the simulated inflation series are almost as volatile as the actual inflation rates. Commitment, in contrast, substantially exaggerates the volatility of nominal interest rates. As explained earlier, this big discrepancy is required to match inflation volatility, since otherwise the commitment policy implies much stable inflation rates.
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5.3. Counterfactual analysis The data select discretion rather than commitment to describe the PBoC’s behavior in making monetary policy. This prompts us to undertake the following counterfactual exercise: what the macroeconomic outcomes would have realized if the PBoC had operated under timeless commitment? To this purpose, we carry out a counterfactual which takes the estimated parameter values under discretion, but assumes the PBoC can make timeless commitment. The last column of Table A.3 reports the results from this counterfactual analysis. We can see that inflation volatility falls drastically from 5.47% to 0.89%, which is much lower compared to the actual value of 5.50% in the data. This big reduction in inflation volatility is obtained at the cost of a relatively smaller increase in interest rate volatility. Moreover, output fluctuates less. Taking stock, macroeconomic outcomes should improve substantially if the PBoC had set policy according to commitment rather than discretion.
6. Conclusions Chinese monetary policy making has been obscure to outside observers, even though it is an important factor in shaping the world economy. To shed some light on the PBoC’s policy preferences, in this paper we confront a small-scale New Keynesian model in which monetary policy is described by commitment or discretion with the Chinese macroeconomic data. Our estimation results decisively choose discretion as the best-fit model. Put differently, the data prefer the idea that the PBoC in charge of Chinese monetary policy probably operates under discretion. Furthermore, the leading policy goal is price stability, followed by output stability and then interest rate smoothing. Finally, we undertake some counterfactual simulations which suggest that macroeconomic outcomes should have improved substantially if the PBoC had set policy according to commitment rather than discretion. The model for estimation in this paper is deliberately small-scale for the sake of transparency and identification of parameters. We hope our paper can prompt more 17
researches on monetary policy preferences in China. For example, future work can consider regimes-witching volatility and policy parameters, or larger scale models when data allow.
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Givens, G. E. (2012). Estimating central bank preferences under commitment and discretion. Journal of Money, credit and Banking 44(6), 1033–1061. Gómez, M., J. P. Medina, and G. Valenzuela (2019). Unveiling the objectives of central banks: Tales of four Latin American countries. Economic Modelling 76, 81–100. Herbst, E. P. and F. Schorfheide (2015). Bayesian estimation of DSGE models. Princeton University Press. Ilbas, P. (2010). Estimation of monetary policy preferences in a forward-looking model: A Bayesian approach. International Journal of Central Banking 6(3), 169–209. Ilbas, P. (2012). Revealing the preferences of the us federal reserve. Journal of Applied Econometrics 27(3), 440–473. Kang, W., R. A. Ratti, and J. L. Vespignani (2016). Chinese liquidity increases and the US economy. Economic Modelling 52, 764–771. Kirsanova, T. and S. le Rouxb (2013). Commitment vs. discretion in the UK: An empirical investigation of the monetary and fiscal policy regime. International Journal of Central Banking 9(4), 99–151. Leith, C., I. Moldovan, and R. Rossi (2012). Optimal monetary policy in a new Keynesian model with habits in consumption. Review of Economic Dynamics 15(3), 416–435. Li, B. and Q. Liu (2017). On the choice of monetary policy rules for china: A Bayesian DSGE approach. China Economic Review 44, 166–185. Lombardi, D., P. L. Siklos, and X. Xie (2018). Monetary policy transmission in systemically important economies and China’s impact. Journal of Asian Economics 59, 61–79. Mallick, S. K. and R. M. Sousa (2012). Real effects of monetary policy in large emerging economies. Macroeconomic Dynamics 16(S2), 190–212. Matthes, C. (2015). Figuring out the fed—beliefs about policymakers and gains from transparency. Journal of Money, credit and Banking 47(1), 1–29. McCallum, B. T. (1988). Robustness properties of a rule for monetary policy. CarnegieRochester conference series on public policy 29, 173–203. Salemi, M. K. (2006). Econometric policy evaluation and inverse control. Journal of Money, Credit, and Banking 38(7), 1737–1764. Smets, F. and R. Wouters (2007). Shocks and frictions in us business cycles: A Bayesian DSGE approach. American Economic Review 97(3), 586–606. Söderlind, P. (1999). Solution and estimation of re macromodels with optimal policy. European Economic Review 43(4-6), 813–823.
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So¨derstr¨om, U., P. Söderlind, and A. Vredin (2005). New-Keynesian models and monetary policy: A re-examination of the stylized facts. Scandinavian Journal of Economics 107(3), 521–546. Sun, R. (2015). What measures Chinese monetary policy? Journal of International Money and Finance 59, 263–286. Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester conference series on public policy 39, 195–214. Vespignani, J. L. (2015). International transmission of monetary shocks to the Euro area: Evidence from the US, Japan and China. Economic Modelling 44, 131–141. Woodford, M. (1999). Optimal monetary policy inertia. The Manchester School 67, 1–35. Woodford, M. (2003). Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press, Princeton, NJ. Zhang, W. (2009). China’s monetary policy: Quantity versus price rules. Journal of Macroeconomics 31(3), 473–484. Zhou, X. (2016). Managing multi-objective monetary policy: From the perspective of transitioning Chinese economy. Michel Camdessus Central Banking Lecture at International Monetary Fund.
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Appendix A. Tables
Table A.1: Prior distributions of the parameters Parameter
Description
σ η
Risk aversion parameter Habit persistence Inverse of Frisch elasticity
α γ Weights on objectives
Range
Density
Mean St. Dev.
[0,1)
Normal Beta Normal
2.50 0.60 2.50
0.30 0.30 0.50
Calvo parameter Inflation inertia
[0,1) [0,1)
Beta Beta
0.75 0.50
0.04 0.15
@A @B
output stabilization term interest rate smoothing term
[0,1) [0,1)
Beta Beta
0.50 0.50
0.15 0.15
^ ^ c^
Steady state output growth Steady state Inflation rate Steady state interest rate
Normal Gamma Gamma
1.30 1.07 0.12
0.09 0.11 0.02
/0 /5 /7
Persistence of TFP shocks Persistence of taste shock Persistence of cost-push shock
[0,1) [0,1) [0,1)
Beta Beta Beta
0.50 0.50 0.50
0.15 0.15 0.15
0 5 7
TFP shock Preference shock Cost-push shock
Inv. Gamma Inv. Gamma Inv. Gamma
1 1 1
5 5 5
φ
Steady state values
Persistence of shocks
Standard deviation of shocks
Note: The mean and standard deviation of gamma distributions are determined by the shape and the scale parameters. Here, the shape and scale parameters are indirectly determined by setting the mean and standard deviation instead, and the same applies to inverse-gamma distributions and beta distributions.
21
Table A.2: Estimation results for the parameters Parameters
Discretion
Timeless commitment
σ
2.993 [2.515,3.455]
2.609 [2.151,3.072]
η
0.950 [0.942,0.959]
0.802 [0.739,0.857]
φ
2.186 [1.742,2.707]
2.191 [1.716,2.650]
α
0.848 [0.827,0.869]
0.847 [0.818,0.875]
γ
0.149 [0.073,0.230]
0.101 [0.036,0.184]
@A
0.415 [0.282,0.545]
0.530 [0.406,0.656]
@B
0.315 [0.185,0.454]
0.401 [0.261,0.547]
^
1.441 [1.298,1.581]
1.465 [1.326,1.605]
^
1.036 [0.883,1.208]
1.133 [0.975,1.300]
c^
0.121 [0.089,0.159]
0.122 [0.090,0.157]
/0
0.257 [0.154,0.361]
0.248 [0.150,0.350]
/5
0.905 [0.865,0.927]
0.938 [0.911,0.961]
/7
0.823 [0.769,0.871]
0.969 [0.937,0.992]
0
1.434 [1.297,1.578]
1.427 [1.289,1.578]
5
2.766 [2.023,3.568]
4.534 [3.253,6.319]
7
4.470 [3.367,5.823]
6.978 [5.636,8.540]
Structural parameters
Weights on objectives
Steady state values
Shock
processes
Log marginal data densities and Bayes factors -558.71 -574.60 (7.96e + 06) (1.00) Note: The posterior distribution of each model parameter is characterized by its mean and [5th, 95th] percentiles in square brackets. The numbers in parentheses are Bayes factors for marginal data densities. Geweke’s harmonic mean estimator
22
Table A.3: Standard deviation
Variables Data Discretion Timeless Commitment Timeless commitment (counterfactual) Inflation 5.498 5.468 5.121 0.889 Output growth 1.350 1.481 1.493 1.470 Nominal interest rate 3.643 4.690 7.126 5.395 Note: This table reports the standard deviation of inflation, real output growth and nominal interest rate in the data, the estimated model under discretion and commitment, and a commitment counterfactual, respectively. The counterfactual takes the estimated parameter values under discretion, but assumes the central bank can make timeless commitment. The numbers are in percent.
23
Appendix B. Figures Output Growth (%)
4 3 2 1 0 1995
2000
2005
2010
2015
2010
2015
2010
2015
Annualized Inflation Rate (%)
30 20 10 0 -10 1995
2000
2005 Annualized Interest Rate (%)
15 10 5 0 1995
2000
2005
Figure B.1: The data for estimation Note: This figure plots output growth, annualized inflation and annualized nominal interest rate (1992:Q22017:Q4) in percentage points.
24
Figure B.2: Impulse responses under discretion and the corresponding commitment counterfactual , inflation rate and output gap Note: This figure graphs the impulse responses of nominal interest rate
to a 1% productivity shock (left column), preference shock (middle column) and cost-push shock #̂
(right column) for the estimated model under discretion (x-marked line) and the corresponding commitment counterfactual (circled line), respectively. The counterfactual takes the estimated parameter values under discretion, but assumes the monetary authority has access to a timeless commitment technology. The responses are percent deviations from steady state.
25
Figure B.3: Impulse responses under discretion and commitment
, inflation rate and output gap Note: This figure graphs the impulse responses of nominal interest rate to a 1% productivity shock (left column), preference shock (middle column) and cost-push shock #̂
(right column) for the estimated model under discretion (x-marked line) and commitment (circled line), respectively. The responses are percent deviations from steady state.
Highlights:
• This paper offers a first estimation of Chinese central bank’s policy preferences. • Monetary policy preferences are described by commitment or discretion. • The data favor discretionary monetary policy. • Price stability weights more than output stabilization and interest rate smoothing. • Macroeconomic outcomes could have greatly improved, had commitment prevailed.
27
S0264-9993(19)30966-6
DOI:
https://doi.org/10.1016/j.econmod.2019.11.022
Reference:
ECMODE 5080
To appear in:
Economic Modelling
Received Date: 2 July 2019 Revised Date:
19 November 2019
Accepted Date: 21 November 2019
Please cite this article as: Liu, D., Zhang, Y., Sun, W., Commitment or discretion? An empirical investigation of monetary policy preferences in China, Economic Modelling (2019), doi: https:// doi.org/10.1016/j.econmod.2019.11.022. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.
Commitment or Discretion? An Empirical Investigation of Monetary Policy Preferences in China Ding Liua, Yue Zhangb, Weihong Sunc,∗ aSchool
of Economics, Room 1001, Gezhi Building, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, P. R. China. bSchool of Finance, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, P. R. China. cThe West Center for Economics Research, Southwestern University of Finance and Economics, 555, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, 611130, P. R. China.
Abstract This paper offers a first attempt to estimate the policy preferences of China’s central bank by confronting a small-scale microfounded New Keynesian model in which monetary policy is described by commitment or discretion with the Chinese macroeconomic data over the period from 1992Q2 to 2017Q4. Bayesian model comparison reveals that the data favor discretionary monetary policy. Estimates of the loss function weights under both cases show that the leading policy goal is price stability, followed by output stability and then interest rate smoothing. Finally, through counterfactual analyses we assess how macroeconomic outcomes might improve, had the Chinese central bank been able to commit. These findings shed new light on the opaque Chinese monetary policy, and are robust to subsample analysis. Keywords: Commitment, Discretion, New Keynesian Model, Optimal Monetary Policy, China JEL: E52, E58, E61 We thank Gregory Givens, Juan Medina Guzman and Junior Maih for constructive discussions about optimal monetary policy estimation problem, and thank the editor Sushanta Mallick and two anonymous referees for helpful suggestions. In particular, Junior Maih kindly offers some guidance on using the RISE toolbox to estimate the optimal policy problem. We also thank the participants of 2019 IEF Conference held in Nanjing Audit University for comments. However, all errors remain our own. Ding Liu would like to gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 71601160) and from the CICFDSC at SWUFE (Grant No. JRXT201905). ∗ Corresponding author
Email addresses: [email protected] (Ding Liu), [email protected] (Yue Zhang), [email protected] (Weihong Sun)
1. Introduction Although China has become a major contributor to the world economy, its monetary policy governed by the People’s Bank of China (PBoC) is still opaque to outside observers. The former governor Xiaochuan Zhou explains that the PBoC has multiple policy objectives, ranging from short-term to long-term goals (Zhou, 2016). In the short run, the PBoC aims to achieve price stability, economic growth, full employment, and broadly balanced payments, while in the longer run it also considers implementing financial reforms and opening up, in order to develop domestic financial markets. These objectives are embodied in “the Law of the People’s Republic of China on the PBoC” passed in 1995 and amended in 2003. However, the PBoC has never explicitly articulated the relative importance of these objectives. This vagueness leads to distinct interpretations. For example, Chen et al. (2016) argues that output growth is the overarching goal, while the former governor in a written speech prepared for the 2012 Per Jacobsson Lecture points out that the PBoC puts price stability in the most important position. It is imperative to figure out the PBoC’s objective function, since Chinese monetary policy has spillover effects on both developing and developed economies, see Vespignani (2015), Kang et al. (2016), Lombardi et al. (2018) and Chiang et al. (2019) for examples. Meanwhile, a growing number of researches have used New Keynesian dynamic stochastic general equilibrium (DSGE) models to analyze Chinese monetary policy in recent years. This line of literature typically assumes a simple Taylor (1993) or McCallum (1988)type rule to characterize monetary policy, see Zhang (2009), Chang et al. (2015), Li and Liu (2017) and among others. This practice, however, is debatable. The reason is that monetary policy instrument rules build on the premise that the monetary authority is able to make credible commitments, while the institutional constraints make it difficult for the PBoC to commit. At the beginning of each year, China’s central government specifies targets for GDP growth, inflation rate and other economic indicators. All government agencies including the 2
PBoC are supposed to accomplish these overarching targets. In fact, the State Council has legal authority in guiding the PBoC to formulate and implement monetary policy, since achieving the multiple objectives requires the PBoC to coordinate and work with other government units. This implies that it is hard for the PBoC to make independent monetary policy decisions. Hence, the conduct of monetary policy in China is distinct from the standard practice in advanced economies, which make researchers wonder which of the two optimal policy concepts - commitment and discretion - better describes the PBoC’s monetary policy preferences consistent with the data.1 What can the data tell us about the PBoC’s preferences and its policy objective function? How does the PBoC trade off the different policy goals in response to economic shocks? This paper attempts to shed some light on these issues. For this purpose, we jointly estimate the parameters describing the PBoC’s objective and its policy preferences by confronting a small-scale microfounded New Keynesian DSGE model with the Chinese macroeconomic data. This paper assumes that the PBoC sets monetary policy optimally under commitment or discretion, and then uses Bayesian methods with quarterly Chinese data to simultaneously estimate the parameters related to the objective function which determines how the PBoC trades off alternative policy goals when responding to exogenous shocks, and the parameters in the structural equations. Finally, through counterfactual analyses we assess how macroeconomic outcomes might improve had the Chinese central bank been able to commit. Three key findings emerge. First, Bayesian model comparison reveals that the policy preferences of PBoC is best characterized as discretion. This result is consistent with the fact that the PBoC does not enjoy either goal or instrument independence. As mentioned earlier, the policy goals and key monetary policy instruments (say the benchmark lending and deposit rates, and the required reserve ratio) are set by the State Council. To achieve the multiple mandates, it is difficult for the PBoC to follow any prescribed plans when
1
See Mallick and Sousa (2012), Sun (2015) and among others for related discussions about monetary policy features in China and other emerging economies.
3
responding to economic shocks. Policy decisions, instead, are made by solving a sequential optimization problem, without taking into account past policy promises. Hence, actions taken by the PBoC to fulfill its multiple objectives barely affect private-sector expectation in any credible way. Second, estimates of the weights associated with the loss function under both commitment and discretion show that the PBoC cares more about inflation stability than stabilizing output and smoothing interest rate. This finding supports the former governor’s view about the PBoC’s policy preferences. The impulse response functions also reveal that the PBoC gives the highest priority to price stability. Third, using counterfactual simulations, we show that had the PBoC been able to commit, the Chinese economy would have enjoyed substantially lower inflation volatility: the predicted standard deviation of inflation is 0.89% compared to the actual value of 5.50%. The volatility of nominal interest rate, nevertheless, increases significantly from the actual value of 3.64% to the predicted value of 5.40%. Our paper contributes to the literature in two respects. First, our investigation is a useful addition to the empirical optimal monetary policy literature. As the largest emerging economy, China is becoming more of a major influence on the world economy, hence it is important to better understand its monetary policy. There is a small but growing empirical literature about optimal monetary policy, but we believe that the current paper is the first work studying China’s monetary policy preferences from this perspective. The majority of that literature has focused either on commitment or discretion, without comparing the two modes of optimal monetary policy. For example, Dennis (2004), Söderström et al. (2005) and Castelnuovo (2006) empirically study New Keynesian models under discretion, while Salemi (2006), Ilbas (2010), and Ilbas (2012) estimate New Keynesian models under commitment for the US economy. There are a few exceptions. Givens (2012) uses maximum likelihood method to estimate a reduced-form New Keynesian model under both commitment and discretion. He concludes that discretionary policy better fits the US data during 1982Q1-2008Q4. Coroneo et al. (2018) find the same result employing the method of moment inequalities. Estimating 4
a simple New Keynesian model for UK with Bayesian methods, Kirsanova and le Rouxb (2013) uncover evidences in support of discretionary monetary and fiscal policy. For the US economy, Matthes (2015) estimates a simple reduced-form New Keynesian model where private sectors update their beliefs about whether the monetary authority acts under discretion or commitment, without modeling the central bank’s actual behavior. In contrast, Debortoli and Lakdawala (2016) perform Bayesian estimation based on a medium-scale New Keynesian model allowing for regime-switching deviations from commitment policies. For the US economy during 1966Q1-2012Q2, the data do not support either commitment or discretion, but are in favor of a generalized case called loose commitment. Applying the same estimation method to a small-scale New Keynesian model augmented with regime-switching volatility and policy parameters, however, Chen et al. (2017b) find that the US monetary policy during 1961Q1-2008Q3 is best characterized as being discretionary. Chen et al. (2017a) apply the same methodology to the Euro area and conclude that the data prefer discretion. For emerging economies, Gómez et al. (2019) use Bayesian methods and small open New Keynesian models to uncover the monetary policy objectives of four Latin American economies under both commitment and discretion. They find that the central banks in these four countries act discretionarily and have high preferences for stabilizing inflation as well as smoothing interest rate. Our results are largely consistent with their findings. Relative to these papers, this study is the first to identify the central bank’s preferences for China. Second, our examination of the data of the ultimate target variables like output and inflation can help us understand the PBoC’s systematic behavior. Discussions on simple or optimal policy rules a la Taylor (1993) or McCallum (1988) in China are not likely to be informative without knowledge about whether the central bank acts under commitment at all. Our empirical analysis suggests that ongoing Chinese monetary policy reforms should grant operational independence to the PBoC and limit the number of policy objectives. In this way, the PBoC’s credibility can be enhanced, which is not just good for domestic
5
macroeconomic stability, but also is helpful for trading partners to deal with possible policy spillover from China. We structure the paper into six sections. Section 2 gives a sketch of the theoretical framework. Section 3 specifies the central bank’s objective function and alternative descriptions of optimal monetary policy. Section 4 describes the empirical strategy, including solution methods, data, estimation methods and priors. Section 5 presents our empirical findings. Section 6 concludes. 2. The linearized model The economy consists of households, a perfectly competitive final good sector, a monopolistically competitive intermediate good sector, and the government. To ensure data coherence, the model is obtained by augmenting the prototypical New Keynesian model with propagation mechanisms including consumption habits and price indexation. However, we deliberately keep it relatively simple so that the investigation is transparent. We summarize the linearized model here, and relegate the detailed microfounded model in the online Appendix C. Before outlining the log-linearized model, we define hatted variable ≡ /̅ as the log deviation of variable, , from its steady state, ̅ . The representative household’s optimization gives rise to the consumption Euler equation, 1 = − − − − + #1
and the labor supply decision, σ + = "! − #̂ #2
where = 1 − & ' − & ' is habits-adjusted consumption, denotes nominal
interest rate, stands for inflation, represents a technology shock, is a preference shock, denotes output, "! is real marginal cost, and #̂ = (̅(̂ /1 − (̅ performs a costpush shock generated by variations in the labor income tax rate. The parameter 1/σ 6
denotes the intertemporal elasticity of substitution, ϕ is the inverse of the Frisch elasticity of labor supply, and η measures the degree of external consumption habits. When η = 0, equation (1) contains no backward-looking components and degrades to the standard consumption Euler equation. With external habits, both past and expected future consumption affects current consumption (which equals to output here). As a result, the elasticity of consumption with respect to real interest depends not only on σ, but also on η. Given σ, the effect of real interest rate on consumption tends to be lower for higher η. The firms’ optimization decisions give rise to a hybrid New Keynesian Phillips curve (NKPC), =
) * + + + "! #3
1 + )* 1 + )* '
where the parameter β denotes the discount factor of households, γ measures the degree of price indexation, α represents the Calvo (1983) probability of fixed price, and + ≡
1 − -) 1 − - ⁄-1 + )* .
When γ = 0, equation (3) contains no backward-looking components and degrades to the standard NKPC. With partial price indexation, past inflation along with its expected future counterpart and current real marginal cost determine the present inflation. In this way, we introduce a backward looking component into the inflation process. The technology, preference and cost-push shocks follow AR(1) processes: = /0 ' + 10, #4
= /5 ' + 15, #5
#̂ = /7 #̂ ' + 17, #6
where 10, , 15, and 17, are independent and identically distributed N0, 0; , N0, 5; and N0, 7; , respectively.
7
The central bank treats as the policy constraints the equations given above, and then chooses the nominal interest rate as its policy instrument to minimize a loss function considered in the following section. 3. The central bank’s problem This section describes the central bank’s objective function and its optimal policy problem. 3.1. The central bank’s objective function To estimate optimal policy regimes, we need to define the central bank’s objective function. An obvious benchmark would be the microfounded welfare function which is a second-order approximation to the expected discounted utility of the representative households populating our economy, as shown in Appendix D. However, it is too restrictive, see Chen et al. (2017a, b). Therefore, we adopt a simpler specification as Debortoli and Lakdawala (2016) do.2 We consider the following objective function for estimation, D
; L = = > ) ?; + @A ; + @B − ' C #7
E=
where , and represent the log deviations of inflation, output and nominal interest rate from their steady states, respectively. We normalize the weight on inflation stabilization to be unity, so the parameters @A and @B capture the relative concerns for stabilizing output and smoothing interest rates. This form of loss function reflects the PBoC’s multiple objectives including price stability, economic growth and financial stability. We include an interest rate smoothing term to capture financial stability concerns, see Ilbas (2010), Givens (2012), Debortoli and Lakdawala (2016) and Chen et al. (2017a) for examples.
2
As a robustness check, we consider the case with the microfounded welfare function, but freely estimate the coefficients on the various quadratic terms. The main results are the same under this alternative specification. The details of this exercise are available upon request.
8
3.2. Optimal monetary policy We study two standard cases of optimal monetary policy, that is, timeless commitment and discretion. Under both cases, the central bank chooses interest rates as its monetary policy instrument to minimize the loss function (7), subject to the private sector behavior equations (1)-(3), and the law of motion for the exogenous shocks (4)-(6). However, they differ considerably in the extent to which the monetary authority has the ability to affect the private sector’s expectations, since timeless commitment gives rise to some historydependent components in monetary policy, which is absent under discretion. Under timeless commitment as first proposed by Woodford (1999), the monetary authority is able to make credible commitments in advance about future policy actions. In D
current context, the monetary authority can choose a complete policy plan, G HE= , in order to minimize the loss function (7), subject to the sequence of equilibrium constraints (1)-(6), and never re-optimizes. The commitment solution is characterized by equation (9) and (10). When solving for the optimal interest rate path, the monetary authority will take into consideration the effects of its policy plans on forward-looking variables so that it can influence the private-sector expectations in a manner that enhances the output-inflation trade-off. In contrast, a discretionary monetary authority acts sequentially without committing itself to any policy plans. Equations (11) and (12) describe the discretionary solution. When solving the sequential optimization problem, the central bank adjusts policy only based on the present and future state of the economy, without considering any past commitments. This implies that it takes future variables as given in sequential reoptimization. As a result, the central bank cannot harness private-sector expectations to achieve its policy goals.
9
4. Empirical strategy This section first sketches out the solution methods, then describes the estimation strategy including data, measurement equations, Bayesian methods and the specification of priors. 4.1. Solution Methods To compute optimal monetary policies, we can stack the constraints (1)-(6) into the following compact form, I
J K J M = K I M + K; + I N M 1 #8
L K= L 0;× S
where J ≡ Q , , #̂ , ' , ' , ' R are the state variables in period T, L ≡ U , VS S
are the forward-looking variables in period t, and 1 ≡ Q10, , 15, , 17, R are the innovations realized in period t with covariance matrix Σ. Matrices A0, A1, A2 and A3 consist of the structural parameters and their combinations. We can follow the methods in Söderlind (1999) or Dennis (2007) to compute the optimal commitment policy in the timeless perspective. The solution under commitment has the following form I and
J J X1 M = X I M + I ; M #9
0;× W W L J I M = XN I M #10
W
where W are the Lagrange multipliers associated with the forward-looking constraints in the lower block of (8). These multipliers capture the effect of past and current commitments on the private-sector expectations, which suggests that timeless commitment imports a history-dependence into monetary policy. The elements in matrices C1, C2 and C3 are given by the structural parameters and their combinations. The algorithm proposed by Söderlind (1999) or Dennis (2007) can be used to find the optimal discretionary policy. We can show that the solution under discretion is characterized by 10
J = Z J + Z; 1 #11
and
L I M = ZN J #12
which says that the discretionary policy is solely determined by the current and future state of the economy. The structural parameters and their combinations determine the elements in matrices D1, D2 and D3. Comparing the solution under commitment (9) -(10) and the counterpart under discretion (11)-(12), we should note an important difference that the former is historydependent, while the latter is purely forward-looking. This distinction will help us identify which mode of optimal policy fit better the data, since different cross-equation restrictions are imposed by commitment and discretion. The solution derived for commitment or discretion described above gives the state transition equation of a state-space model. The measurement equations are specified in the following subsection. 4.2. Data and Estimation Methods The estimation adopts three quarterly Chinese series as observables, including growth rate of real output (∆RGDPt), annualized nominal interest rate (INTt) and annualized inflation rate (INFt).3 The sample covers the period from 1992:Q2 to 2017:Q4.4 All data except interest rate are seasonally adjusted. The main data sources are the quarterly data constructed by Chang et al. (2016) and the CEIC database. Figure B.1 plots the series for output growth, inflation, and nominal interest rate for the sample period. Accordingly, the measurement equations are specified as:
3
The difference of logged real GDP, scaled by 100, returns the growth rate of real output. Annualized nominal interest rate is the quarterly average of the 7-day China interbank offered rate. Annualized inflation rate is the difference of logged GDP deflator, multiplied by 400. 4 As a robustness check, we also conduct a subsample estimation in the online appendix. Our main results described below are still valid.
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∆\Z] = − ' + + ^ _`a = 4 + 4 ^ _`b = 4 + 4^ + 4 ^ + 4c ^ where ^ ≡ 100d − 1 , ^ ≡ 100d − 1 and c ^ ≡ 1001⁄) − 1 represent quarterly growth rate of real GDP, inflation rate and real interest rates in the steady state, respectively. The measurement equations described here, together with the state transition equation corresponding to the solution under commitment or discretion, yield a statespace model to be estimated. Given the distributions of shocks, we can use the Kalman filter to calculate the likelihood of the model solution, and then estimate the model parameters using Bayesian methods.5More specifically, we adopt a Markov chain Monte Carlo (MCMC) algorithm to estimate posterior distributions. We first find the posterior modes numerically using some optimization routine. To sample from the posterior distribution, we employ a random-walk Metropolis-Hastings (RWMH) algorithm. A target acceptance rate of about 30% is achieved by scaling the variance of the proposal density. We run two separate MCMC chains with alternative starting values. For each chain, we make 0.5 million draws but burn in the first 0.1 million draws. To save space, we thin the MCMC chains by keeping every 10th draw from the remaining draws. We adopt the potential scale reduction factor (PSRF) by Gelman et al. (1992) to assess convergence of the posterior simulation. To compare model fit, we adopt the modified harmonic mean estimator proposed by Geweke (1999) to estimate the log marginal data density.6
5
Please refer to An and Schorfheide (2007), Herbst and Schorfheide (2015) and among others for detailed instructions on implementing the Bayesian methods in estimating DSGE models. 6 We conduct the Bayesian estimation using the RISE toolbox written by Junior Maih. RISE can be accessed via https://github.com/jmaih/RISE_toolbox.
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4.3. Prior Distribution Table A.1 presents the prior distributions. We follow Smets and Wouters (2007) and Chen et al. (2017b) to set priors for the structural parameters. The relative weights associated with the central bank’s objective function, @A and @B , are assumed to be distributed following beta distributions with mean 0.5 and standard deviation 0.15. We set the prior means of real output growth ^ , inflation πQ and real interest rates c ^ in the steady state to be largely consistent with their sample averages. The discount factor, β = 1 + c ^ ⁄100 ' , is determined by c ^ . Finally, we assume that the persistence of the shocks has a beta distribution with mean 0.5 and standard deviation 0.15, and that the standard deviation of these shocks follows a diffuse prior, specified as an inverse gamma distribution with mean 1 and standard deviation 5. 5. Empirical results This section reports the empirical results. To begin with, we present the posterior estimates of the central bank preferences and the model structural parameters. Then we compare the two optimal monetary policy regimes and assess commitment or discretion fitting best the data. Finally, we conduct some counterfactual exercises. 5.1. Posterior estimates Table A.2 summarizes the posterior means and the 90% confidence intervals. The first column gives the results under discretion which is the best-fitting model. The posterior means are largely consistent with the literature: an elasticity of intertemporal substitution, σ = 2.993; a sizeable estimate of habit persistence, η = 0.950; a labor supply elasticity, ' = 1⁄2.186; an estimate of price stickiness, α = 0.848, suggesting that the duration of price contracts is about one and a half years; and a rather limited degree of price indexation, γ = 0.149. Moving from discretion to timeless commitment, the structural parameter estimates differ to various degrees. The parameter estimates of labor supply elasticity and price 13
stickiness remain largely the same, while the intertemporal elasticity of substitution, habit persistence and the degree of price indexation falls to σ = 2.609, η = 0.802 and γ = 0.101, respectively. The estimates of standard deviation and persistence for the productivity shock under discretion and commitment are almost identical, but the estimates for the preference and cost-push shocks differ significantly. Under discretion, the estimated standard deviation and persistence of the preference shock are 5 = 2.766 and /5 = 0.905 , respectively, while under commitment these estimates rises substantially to 5 = 4.534 and /5 = 0.938. These differences are more evident for the cost-push shock #̂ . Moving from discretion to commitment, its estimated standard deviation σµ jumps up from 4.470 to 6.978, and while its persistence ρµ rises from 0.823 to 0.969. As explained in Chen et al. (2017a, b), the differences in the parameter estimates across optimal policy regimes reveal the need to create a relevant trade-off to account for the observed fluctuations in output, inflation and interest rates. In the standard New Keynesian model, the monetary authority faces an output-inflation trade-off only when cost-push shocks hit the economy, see Woodford (2003). As in Leith et al. (2012), the presence of external habits in our model breaks this “divine coincidence”, which implies that meaningful policy trade-offs also arise from technology and preference shocks. Under discretion, history-dependence is absent due to the inability to commit, hence inflation inertia carries more weight in fitting the data. When the central bank can commit, monetary policy does not suffer from the stabilization bias problem, and as a result it allows higher variance and persistence of cost-push shocks. More volatile and persistent cost-push shocks help the commitment model generate a significant policy trade-off which in turn explains the actual output and inflation dynamics. As we make it clear later, however, the data reject commitment, since it excessively stabilizes the economy, especially in terms of inflation volatility. Regarding the loss function, estimates of weights @A and @B are substantial, but higher under timeless commitment, relative to those under discretion. As we explain in detail 14
below, higher weights on interest rate smoothing and output stabilization are needed for timeless commitment to fit the data better. Under both cases, the dominant policy objective is inflation stability, followed by output stability and then interest rate smoothing. These estimation results help us understand the targets and preferences of the Chinese monetary policy, since the PBoC has never explicitly articulated the relative importance of its objectives. To further understand the differences of parameter estimates under discretion and commitment, Figure B.2 and B.3 compare the impulse responses under the two versions of optimal monetary policy. Figure B.2 graphs the responses of nominal interest rate , inflation and output gap to productivity shock (left column), preference shock
(middle column) and cost-push shock #̂ (right column). The counterfactual takes the
estimated parameter values under discretion, but assumes the monetary authority has access to a timeless commitment technology. Two findings emerge. First, all three shocks produce hump-shaped responses in output gap and inflation, due to the presence of habits externality. Second, other things being equal, switching from discretion to commitment the central bank has an incentive to react more aggressively to the shocks, since a relatively small increase in interest rate volatility can trade for a big decrease in inflation volatility. By committing to sustain higher interest rates, the policy maker can successfully reduce current alteration in output gap and inflation, via effectively depressing their expected future values. The response of output to a cost-push shock is the only exception, since high interest rates produce a prolonged negative output gap. One implication of these results is that the timeless commitment involves a data-inconsistent level of interest rate volatility. A higher weight on interest rate smoothing is needed to alleviate this inconsistency for empirical fit, as shown in Table A.2. Figure B.3 plots impulse responses for the estimated models under commitment and discretionary monetary policy. We should note that now policy regimes and variations in other estimated parameters together drive responses. Due to a larger estimate of @B under commitment as shown in Table A.2, the policy responses to the preference (or demand) 15
and cost-push (or supply) shocks of the same size strive to follow those under discretion. Stronger interest rate smoothing incentive, however, causes more volatile output and inflation. For example, the peak response of inflation to the cost-push shock is twice as large for commitment relative to the corresponding result in Figure B.2. Another message from Figure B.3 is that timeless commitment relies heavily on the preference and cost-push shocks to match the data, particularly inflation volatility. For example, the response of output gap to these shocks are deeper and more persistent under commitment, which is in line with the estimated shock parameters as shown in Table A.2. 5.2. Model comparison We compare the empirical fit of the two modes of optimal monetary policy based on two criteria in this subsection. First, we calculate the log marginal data densities using the harmonic mean estimator proposed by Geweke (1999). Second, we simulate the two models and assess which model specification can produce standard deviations close to the data moments. The last row of Table A.2 presents the log marginal data densities and the associated Bayes factor for discretion and timeless commitment. These two model selection devices suggest that data decisively favor discretion over commitment. For this reason, we can conclude that the PBoC does not attempt to commit in advance to future monetary policy actions. As a second comparison, Table A.3 reports the standard deviations of inflation, output growth and nominal interest rate based on the data and the estimated models. Discretion performs better in reproducing the business cycle features. In particular, the simulated inflation series are almost as volatile as the actual inflation rates. Commitment, in contrast, substantially exaggerates the volatility of nominal interest rates. As explained earlier, this big discrepancy is required to match inflation volatility, since otherwise the commitment policy implies much stable inflation rates.
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5.3. Counterfactual analysis The data select discretion rather than commitment to describe the PBoC’s behavior in making monetary policy. This prompts us to undertake the following counterfactual exercise: what the macroeconomic outcomes would have realized if the PBoC had operated under timeless commitment? To this purpose, we carry out a counterfactual which takes the estimated parameter values under discretion, but assumes the PBoC can make timeless commitment. The last column of Table A.3 reports the results from this counterfactual analysis. We can see that inflation volatility falls drastically from 5.47% to 0.89%, which is much lower compared to the actual value of 5.50% in the data. This big reduction in inflation volatility is obtained at the cost of a relatively smaller increase in interest rate volatility. Moreover, output fluctuates less. Taking stock, macroeconomic outcomes should improve substantially if the PBoC had set policy according to commitment rather than discretion.
6. Conclusions Chinese monetary policy making has been obscure to outside observers, even though it is an important factor in shaping the world economy. To shed some light on the PBoC’s policy preferences, in this paper we confront a small-scale New Keynesian model in which monetary policy is described by commitment or discretion with the Chinese macroeconomic data. Our estimation results decisively choose discretion as the best-fit model. Put differently, the data prefer the idea that the PBoC in charge of Chinese monetary policy probably operates under discretion. Furthermore, the leading policy goal is price stability, followed by output stability and then interest rate smoothing. Finally, we undertake some counterfactual simulations which suggest that macroeconomic outcomes should have improved substantially if the PBoC had set policy according to commitment rather than discretion. The model for estimation in this paper is deliberately small-scale for the sake of transparency and identification of parameters. We hope our paper can prompt more 17
researches on monetary policy preferences in China. For example, future work can consider regimes-witching volatility and policy parameters, or larger scale models when data allow.
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Appendix A. Tables
Table A.1: Prior distributions of the parameters Parameter
Description
σ η
Risk aversion parameter Habit persistence Inverse of Frisch elasticity
α γ Weights on objectives
Range
Density
Mean St. Dev.
[0,1)
Normal Beta Normal
2.50 0.60 2.50
0.30 0.30 0.50
Calvo parameter Inflation inertia
[0,1) [0,1)
Beta Beta
0.75 0.50
0.04 0.15
@A @B
output stabilization term interest rate smoothing term
[0,1) [0,1)
Beta Beta
0.50 0.50
0.15 0.15
^ ^ c^
Steady state output growth Steady state Inflation rate Steady state interest rate
Normal Gamma Gamma
1.30 1.07 0.12
0.09 0.11 0.02
/0 /5 /7
Persistence of TFP shocks Persistence of taste shock Persistence of cost-push shock
[0,1) [0,1) [0,1)
Beta Beta Beta
0.50 0.50 0.50
0.15 0.15 0.15
0 5 7
TFP shock Preference shock Cost-push shock
Inv. Gamma Inv. Gamma Inv. Gamma
1 1 1
5 5 5
φ
Steady state values
Persistence of shocks
Standard deviation of shocks
Note: The mean and standard deviation of gamma distributions are determined by the shape and the scale parameters. Here, the shape and scale parameters are indirectly determined by setting the mean and standard deviation instead, and the same applies to inverse-gamma distributions and beta distributions.
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Table A.2: Estimation results for the parameters Parameters
Discretion
Timeless commitment
σ
2.993 [2.515,3.455]
2.609 [2.151,3.072]
η
0.950 [0.942,0.959]
0.802 [0.739,0.857]
φ
2.186 [1.742,2.707]
2.191 [1.716,2.650]
α
0.848 [0.827,0.869]
0.847 [0.818,0.875]
γ
0.149 [0.073,0.230]
0.101 [0.036,0.184]
@A
0.415 [0.282,0.545]
0.530 [0.406,0.656]
@B
0.315 [0.185,0.454]
0.401 [0.261,0.547]
^
1.441 [1.298,1.581]
1.465 [1.326,1.605]
^
1.036 [0.883,1.208]
1.133 [0.975,1.300]
c^
0.121 [0.089,0.159]
0.122 [0.090,0.157]
/0
0.257 [0.154,0.361]
0.248 [0.150,0.350]
/5
0.905 [0.865,0.927]
0.938 [0.911,0.961]
/7
0.823 [0.769,0.871]
0.969 [0.937,0.992]
0
1.434 [1.297,1.578]
1.427 [1.289,1.578]
5
2.766 [2.023,3.568]
4.534 [3.253,6.319]
7
4.470 [3.367,5.823]
6.978 [5.636,8.540]
Structural parameters
Weights on objectives
Steady state values
Shock
processes
Log marginal data densities and Bayes factors -558.71 -574.60 (7.96e + 06) (1.00) Note: The posterior distribution of each model parameter is characterized by its mean and [5th, 95th] percentiles in square brackets. The numbers in parentheses are Bayes factors for marginal data densities. Geweke’s harmonic mean estimator
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Table A.3: Standard deviation
Variables Data Discretion Timeless Commitment Timeless commitment (counterfactual) Inflation 5.498 5.468 5.121 0.889 Output growth 1.350 1.481 1.493 1.470 Nominal interest rate 3.643 4.690 7.126 5.395 Note: This table reports the standard deviation of inflation, real output growth and nominal interest rate in the data, the estimated model under discretion and commitment, and a commitment counterfactual, respectively. The counterfactual takes the estimated parameter values under discretion, but assumes the central bank can make timeless commitment. The numbers are in percent.
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Appendix B. Figures Output Growth (%)
4 3 2 1 0 1995
2000
2005
2010
2015
2010
2015
2010
2015
Annualized Inflation Rate (%)
30 20 10 0 -10 1995
2000
2005 Annualized Interest Rate (%)
15 10 5 0 1995
2000
2005
Figure B.1: The data for estimation Note: This figure plots output growth, annualized inflation and annualized nominal interest rate (1992:Q22017:Q4) in percentage points.
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Figure B.2: Impulse responses under discretion and the corresponding commitment counterfactual , inflation rate and output gap Note: This figure graphs the impulse responses of nominal interest rate
to a 1% productivity shock (left column), preference shock (middle column) and cost-push shock #̂
(right column) for the estimated model under discretion (x-marked line) and the corresponding commitment counterfactual (circled line), respectively. The counterfactual takes the estimated parameter values under discretion, but assumes the monetary authority has access to a timeless commitment technology. The responses are percent deviations from steady state.
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Figure B.3: Impulse responses under discretion and commitment
, inflation rate and output gap Note: This figure graphs the impulse responses of nominal interest rate to a 1% productivity shock (left column), preference shock (middle column) and cost-push shock #̂
(right column) for the estimated model under discretion (x-marked line) and commitment (circled line), respectively. The responses are percent deviations from steady state.
Highlights:
• This paper offers a first estimation of Chinese central bank’s policy preferences. • Monetary policy preferences are described by commitment or discretion. • The data favor discretionary monetary policy. • Price stability weights more than output stabilization and interest rate smoothing. • Macroeconomic outcomes could have greatly improved, had commitment prevailed.
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