- Email: [email protected]
Has the FED Fallen behind the Curve? Evidence from VAR models
Accepted Manuscript Has the fed fallen behind the curve? Evidence from VAR models Antonio M. Conti
PII: DOI: Reference:
S0165-1765(17)30238-0 http://dx.doi.org/10.1016/j.econlet.2017.06.011 ECOLET 7655
To appear in:
Economics Letters
Received date : 16 January 2017 Revised date : 2 May 2017 Accepted date : 9 June 2017 Please cite this article as: Conti, A.M., Has the fed fallen behind the curve? Evidence from VAR models. Economics Letters (2017), http://dx.doi.org/10.1016/j.econlet.2017.06.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
Has the Fed Fallen Behind the Curve? Evidence from VAR models Antonio M. Conti ‡,♦ ‡
Banca d’Italia, Economic Outlook and Monetary Policy Directorate, Rome, Italy. ♦ ECARES, UlB, Bruxelles, Belgium. [email protected]
Abstract We evaluate the role of US monetary policy in shaping inflation and economic activity by means of a medium–scale Bayesian VAR model, where the ZLB and the unconventional measures are addressed by using a shadow interest rate. The historical decomposition and a conditional forecast scenario show that the FED’s stance is in line with GDP and inflation dynamics. Moreover, core inflation will likely lie below its target in 2017. Keywords: monetary policy; inflation; Bayesian VAR; sign restrictions; conditional forecasting.
Preprint submitted to Economics Letters
May 2, 2017
1. Introduction The Federal Reserve (FED) launched an extraordinary and prolonged monetary easing program to contrast the effects of the Great Recession of 2008–09. As the US economy rebounded, the debate spread on whether and when the FED should have raised the policy rate, emphasizing the negative consequences of waiting “too long” in terms of inflationary pressures. However, contrary to forecasters’ and even to some FED policymakers’ calls, the first tightening occurred in December 2015, and the second followed last November. We study whether the FED has “fallen behind the curve” (Nechio and Rudebusch, 2016), both in a structural and reduced–form VAR framework. In particular, we first assess the contribution of monetary policy shocks to inflation dynamics, identified by sign restrictions implemented following the algorithm by RubioRam´ırez et al. (2010) in a medium–scale Bayesian VAR model which includes domestic, global, real and financial variables. To capture unconventional monetary policies we use a shadow interest rate, which helps for modelling monetary stance when policy rates hit the zero lower bound (ZLB), as suggested by Krippner (2013) and Wu and Xia (2016) among others. In order to do so, we borrow their estimated shadow rates available on their respective websites1 . Next, we use the same Bayesian VAR model to forecast US PCE core inflation over 2017–18, both unconditionally and conditionally on possible future paths of the Fed Funds rate (FFR hereafter) and oil prices, relying on the methodology pioneered by Waggoner and Zha (1999) and then refined by recent contributions on conditional forecasts in large Bayesian VAR models by Giannone et al. (2014) and Banbura et al. (2015). Furthermore, we conduct a scenario analysis which compares a counterfactual FFR consistent with business cycle and labor markets dynamics to the observed FFR dynamics. This evaluation is informative on the adequacy of FED’s monetary stance. Our results are the following. First, the Structural VAR (SVAR) model reveals that monetary policy 1 See
the on line Appendix to this paper for details.
Preprint submitted to Economics Letters
shocks had a moderately expansionary effect on inflation in 2016, after being barely contractionary in the last quarter of 2015. Accordingly, they were moderately supportive of economic activity and employment conditions. Second, the results of the forecasting analysis are reassuring on the appropriateness of the FED’s stance. Indeed, the model foresees a slowing of PCE inflation, at around 1.4%, under the assumption of a range of between 4 and 6 hikes within the period 2017–2018. The forecast for PCE core inflation is below the estimates released by the FED in December 2016 and those surveyed in the SPF, but it is very similar to the prediction by the NY FED Large DSGE model (Del Negro et al., 2013). Finally, a conditional forecasting scenario deems the actual FFR dynamics consistent with the one predictable by the FED had the future business cycle pattern been fully available at the end of 2014. This suggests that the FED has not been too cautious towards inflation. In Section 2 we outline the framework, present the data and report the findings of the structural analysis. We describe the conterfactuals and their results in Section 3, whereas in Section 4 we briefly conclude.
2. Bayesian VAR framework To tackle our research question, we adopt a Bayesian VAR model, a flexible tool which provides a realistic empirical representation of data. By contrast, its main limitation lies in its linearity, which makes it somewhat ill-suited to deal with potential non–linear effects of shocks at the ZLB. To some extent, however, this limitation may be dampened by the use of a shadow interest rate to capture negative monetary policy rates at the ZLB (see, in particular, Krippner, 2013; Wu and Xia, 2016). Furthermore, the shadow rate is clearly linked to the size of the FED’s balance sheet, commonly adopted by studies on unconventional monetary policies: the correlation between the two variables is equal to -0.8 (Figure 1). June 17, 2017
Figure 1: FED monetary policy stance
evidence in Laseen and Sanjani, 2016; Abbate et al., 2016). Log transformation is applied, except for unemployment, interest rates and the EBP.3 Identification of A0 relies on the sign restrictions approach proposed by Rubio-Ram´ırez et al. (2010), imposed on impact, which allow us to disentangle five structural shocks: other than monetary policy, we also identify shocks to oil supply, global demand, domestic aggregate demand and supply (see Table 1). Table 1: Sign restrictions Shock Variable ot wyt pt yt S Rt qt wt urt EBPt
Notes: Green shaded area: FED Total Assets. Interest rates are plotted on the right-hand scale. Blue lines: FFR (straight); shadow interest rate by Wu and Xia (dashed); shadow interest rate by Krippner (circled).
2.1. Model Let yt be an n × 1 vector of endogenous macroeconomic variables. Their joint dynamics is described by the following system of equations: p X 0 0 0 0 iid (1) yt− j A j + εt , εt ∼ N(0, In ) y t A0 = c +
Oil Supply
Global Demand
– + – +
– – – – – +
Aggregate Demand
Monetary Policy
Aggregate Supply +
– – – – – +
– – + +
– +
+ +
Notes: A “+” (or “–” ) for the exchange rate implies that the structural shock leads to an appreciation (depreciation) of the US dollar vis-`a-vis its main trade partners. Blank entries: no restrictions.
The restrictions are sufficient to disentangle the shocks, and, together with the other estimation details, are extensively discussed in the on–line Appendix.4
j=1
where t = 1, 2, . . . , T , c is a n × 1 vector of constants, εt is a n × 1 vector of exogenous shocks A j is a n × n matrix of parameters for 0 ≤ j ≤ p with A0 invertible, p is the lag length, and T is the sample size. The vector εt conditional on past information and initial conditions y0 ..y1−p is gaussian with mean zero and variance covariance matrix In .
2.3. Findings of the Structural analysis Figure 2 plots the historical decomposition of the variables of interest for the FED, focusing on the recent period of low inflation (2013–2016). The five identified shocks explain the bulk of the deviation of inflation from its VAR baseline, validating our identification (Figure 1a). The unexplained part widens in 2016, as inflation increases. This suggests that other shocks are the main drivers of inflation in 2016. The more obvious sources seem to originate from the labor market, such as labor demand or wage bargaining shocks. Although we do not identify these shocks, we stress how high the deviation of wage growth from its baseline is in 2015:Q1,
2.2. Data and Identification of the Structural Shocks The sample includes seasonally adjusted quarterly observations for the period 1987:Q1 to 2016:Q3. The baseline specification includes the real oil price (ot ), world demand (wyt ), PCE price index net of food and energy (pt ), real GDP (yt ), the FFR (replaced by the shadow rate at the ZLB; S Rt ), the nominal effective exchange rate of the dollar against major US partners (qt ), wages (wt , in real terms in the baseline specification),2 the unemployment rate (urt ) and the EBP by Gilchrist and Zakrajsek (2012) (EBPt ), which measures credit markets stress and is important in explaining US inflation dynamics (in this regard, see, among others, VAR
3 Our baseline specification improves on the structural analysis carried out by Conti et al. (2015) and Bobeica and Jarocinski (2017) by incorporating labor and financial variables. 4 Sign restrictions follow theoretical and empirical prescriptions (Kilian, 2009; Rubio-Ram´ırez et al., 2010). We leave to further research the implementation of the influential contribution recently made by Baumeister and Hamilton (2015) on the informativeness of allegedly uninformative priors.
2 Considering nominal wages does not alter the results. See the Appendix for more details.
2
Figure 2: Historical decomposition
same time span the role of other shocks (i.e., not identified, depicted by the orange bars) turns predominant in explaining unemployment dynamics. The broad picture looks consistent with our findings of the five identified shocks accounting for around 30 and 60% of wage growth and unemployment(Figure 3c–d). We now turn to monetary shocks, as their contribution to inflation dynamics may help for understanding whether, and to what extent, the FED is driving inflation upwards. Figure1a shows that their effect is not negligible: the contribution of monetary policy shocks identified by means of the shadow rate is, on average, modest. In fact, it is positive from late 2013 until the fourth quarter of 2015, when it turns barely negative as the FED raised the FFR for the first time since 2007. In 2016 monetary policy contribution was slightly positive, however marginal with respect to the contribution of other shocks. Additionally, monetary policy shocks are not significant in shaping the deviation of real GDP growth from its baseline (Figure 1b). Thus, the SVAR model demonstrates the modest although expansionary - role played by monetary policy shocks in shaping inflation in 2016. This seems to contrast the idea that FED’s actions could spark a burst in inflation in the coming years. However, since the most recent widening in the deviation from core inflation baseline does not allow for a precise answer to our question of interest, we turn to the second part of the empirical analysis. 3. Conditional forecasts and scenario analysis We complement the structural analysis of monetary shocks by running a forecasting analysis. We estimate model (1) on the full sample and then use it to forecast inflation, both unconditionally and conditionally. As for the latter, we follow an approach similar to the one used by Giannone et al. (2014) in designing two different exercises. In the first exercise, we ask what would happen to PCE core inflation (and other variables such as economic activity) should the policy rate be raised by 1 percentage point in 2017, i.e. the four hikes under current debate.5 We then examine the obtained unconditional and conditional forecasts to detect possible overshooting of PCE core inflation with respect to the target. To be as realistic as possible we include the most recent rebound of oil prices, as we impose this exogenous path among the conditions as well.6
Notes: Black line: deviation of the actual variable from its VAR-baseline; bars: shocks’ contribution. From the top to the bottom: inflation, real GDP growth, wages growth, unemployment rate. Sample: 1987:Q1 - 2016:Q3.
before reverting to more moderate levels. To the extent that wage dynamics produces inflationary pressures, one may argue that labor market shocks translated into PCE increase in recent quarters. Accordingly, in the
5 We
also condition on two hikes by 25 basis points each in 2018. January 2017 Oil prices were envisioned at around 58$ in 2018Q4. They are now floating around 56$ at the same horizon (see 6 In
3
Figure 3: Forecasts
We subsequently ask a related question: what would the FFR implied by model (1) have been had the FED observed business cycle dynamics in 2015-2016? A counterfactual FF higher than the actual would signal that the FED is behind the curve. 3.1. Findings of the counterfactuals Figure 3 shows the results of the first simulation. The unconditional forecast shows PCE inflation growing to 1.7% in the fourth quarter of 2016, before gradually falling in 2017, then stabilizing at around 1.2%. This reflects the FFR path, foreseen by the model around 1.2% at the end of 2017, implying two hikes of 0.25 basis points each, and a rise in oil prices (Figure 3c-d). As for economic activity, in spite of the less expansionary monetary stance, real GDP experiments a growth by 2.5%, subsequently slowing at 2.1%. Conditional forecasts describe a similar picture: inflation is somewhat higher because of the higher oil prices. In fact, differences between the imposed path of the FFR and its unconditional forecast only appear in 2018. The average predicted PCE core inflation is equal to 1.3% and 1.4% in 2017 and 2018, respectively, when conditioning on the imposed path for oil prices and the FFR. These values basically coincide with those delivered by the FRB-New York DSGE Model (Table 2; Del Negro et al., 2013), but are lower than the analogous released by the FED Board (FOMC) and those surveyed in the Philadelphia SPF.7 In Figure 4 we show the results of our scenario analysis. The conditional forecast is obtained estimating model (1) over the period 1987:Q1 to 2016:Q3, assuming to know in 2014:Q4 the future realized business cycle and labor markets developments. In practice, should the actual value of the policy rate be below its conditional forecast, we could conclude that the FED approach was too cautious. This does not seem to be the case, as indeed the policy rate always lies above its conditional forecast.8 the on line Appendix for more details. 7 Results are qualitatively confirmed when extending the sample up to the first quarter of 2017. See the on line Appendix for details on forecasts obtained after estimating the Bayesian VAR model until 2017:Q1, the most recent data available. 8 The same results hold when conditioning on business cycle dynamics over the period 2014:Q1-2016:Q3. In the Appendix we also show that the actual FFR dynamics is in line with its counterfactual when assuming to know the future path of PCE core inflation as well.
Notes: Black line: actual variable; blue line: median conditional forecast obtained assuming the knowledge of the future path of the FFR and oil prices; red dots: unconditional forecast; blue (red) shaded area: 68% credibility interval.
4. Conclusions We have investigated whether the FED has fallen behind the curve, i.e. has waited too long before start4
Table 2: Forecasts comparison
flation is still weak, probably lying below the target in 2017.
PCE - core 2017
Acknowledgements
2018
Unconditional
1.2
1.3
Conditional
1.3
1.4
FRBNY - November 2016
1.2
1.3
FRBNY - February 2017
1.4
1.4
FOMC - December 2016
1.8
2.0
FOMC - March2017
1.9
2.0
SPF - November 2016
1.9
1.9
SPF - February 2017
1.9
2.0
I thank the Editor Pierre-Daniel Sarte and an anonymous referee for their helpful comments. I would also like to thank Robert Kollmann for a constructive discussion over a previous draft of this paper. I am particularly indebted to Diana Rocco for proofreading and language help. Part of this project was developed during my visiting at EIEF, whose hospitality is gratefully acknowledged. The views here expressed are those of the author and do not necessarily involve those of the Banca d’Italia or the Eurosystem. Appendix A. Supplementary data Supplementary material related to this article can be found online.
Notes: Median forecasts, annualized percentage points. Y-o-y averages. FRBNY entries refer to the large DSGE model used by the NY FED (Del Negro et al., 2013), while FOMC denote the forecast released following the FED Monetary policy committe. SPF entries refer to the Philadelphia FED Survey of Professional Forecasters.
References Abbate, A., Eickmeier, S., Prieto, E., Sep. 2016. Financial shocks and inflation dynamics. CAMA Working Papers 2016-53, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University. Banbura, M., Giannone, D., Lenza, M., 2015. Conditional forecasts and scenario analysis with vector autoregressions for large crosssections. International Journal of Forecasting 31 (3), 739–756. Baumeister, C., Hamilton, J. D., 09 2015. Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information. Econometrica 83 (5), 1963–1999. Bobeica, E., Jarocinski, M., 2017. Missing disinflation and missing inflation since the Great Recession: a VAR perspective. ECB Working Papers forthcoming, European Central Bank. Conti, A. M., Neri, S., Nobili, A., Jul. 2015. Why is inflation so low in the euro area? Temi di discussione (Economic working papers) 1019, Bank of Italy, Economic Research and International Relations Area. Del Negro, M., Eusepi, S., Giannoni, M., Sbordone, A. M., Tambalotti, A., Cocci, M., Hasegawa, R. B., Linder, M. H., 2013. The FRBNY DSGE model. Tech. rep. Giannone, D., Lenza, M., Momferatou, D., Onorante, L., 2014. Shortterm inflation projections: A Bayesian vector autoregressive approach. International Journal of Forecasting 30 (3), 635–644. Gilchrist, S., Zakrajsek, E., June 2012. Credit Spreads and Business Cycle Fluctuations. American Economic Review 102 (4), 1692– 1720. Kilian, L., June 2009. Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review 99 (3), 1053–69. Krippner, L., 2013. Measuring the stance of monetary policy in zero lower bound environments. Economics Letters 118 (1), 135–138. Laseen, S., Sanjani, M. T., Jul. 2016. Did the Global Financial Crisis Break the U.S. Phillips Curve? IMF Working Papers 16/126, International Monetary Fund. Nechio, F., Rudebusch, G. D., November 2016. Has the Fed Fallen behind the Curve This Year? FRBSF Economic Letter (33).
Figure 4: Scenario: FFR dynamics and the business cycle
Notes: Black line: actual variable; blue line: estimated median conditional forecast obtained assuming the knowledge of the future path of real GDP, unemployment and wages; blue shaded area: 68% C.I. Estimation sample is 1987Q12016Q3. In-sample forecast period is 2015Q1-2016Q3.
ing its lift-off from the extraordinary monetary stimulus. Our evidence, based on both structural and reducedform VAR models shows that the FED monetary stance is appropriate given macroeconomic developments. In spite of favourable labor markets signals, PCE core in5
Rubio-Ram´ırez, J., Waggoner, D. F., Zha, T., 2010. Structural vector autoregressions: Theory of identification and algorithms for inference. The Review of Economic Studies 77 (2), 665–696. Waggoner, D. F., Zha, T., November 1999. Conditional Forecasts In Dynamic Multivariate Models. The Review of Economics and Statistics 81 (4), 639–651. Wu, J. C., Xia, F. D., 03 2016. Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound. Journal of Money, Credit and Banking 48 (2-3), 253–291.
6
Has the Fed Fallen Behind the Curve? Evidence from VAR models •
We ask whether the FED has riskily delayed the exit from its large monetary easing
•
We focus on the effects of monetary policy on US PCE core inflation
•
Monetary policy shocks are not pushing inflation much beyond its baseline
•
Scenario analysis shows that the FED’s stance is in line with macroeconomic dynamics
•
PCE core inflation will likely lie below the FED’s target in 2017
PII: DOI: Reference:
S0165-1765(17)30238-0 http://dx.doi.org/10.1016/j.econlet.2017.06.011 ECOLET 7655
To appear in:
Economics Letters
Received date : 16 January 2017 Revised date : 2 May 2017 Accepted date : 9 June 2017 Please cite this article as: Conti, A.M., Has the fed fallen behind the curve? Evidence from VAR models. Economics Letters (2017), http://dx.doi.org/10.1016/j.econlet.2017.06.011 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.
Has the Fed Fallen Behind the Curve? Evidence from VAR models Antonio M. Conti ‡,♦ ‡
Banca d’Italia, Economic Outlook and Monetary Policy Directorate, Rome, Italy. ♦ ECARES, UlB, Bruxelles, Belgium. [email protected]
Abstract We evaluate the role of US monetary policy in shaping inflation and economic activity by means of a medium–scale Bayesian VAR model, where the ZLB and the unconventional measures are addressed by using a shadow interest rate. The historical decomposition and a conditional forecast scenario show that the FED’s stance is in line with GDP and inflation dynamics. Moreover, core inflation will likely lie below its target in 2017. Keywords: monetary policy; inflation; Bayesian VAR; sign restrictions; conditional forecasting.
Preprint submitted to Economics Letters
May 2, 2017
1. Introduction The Federal Reserve (FED) launched an extraordinary and prolonged monetary easing program to contrast the effects of the Great Recession of 2008–09. As the US economy rebounded, the debate spread on whether and when the FED should have raised the policy rate, emphasizing the negative consequences of waiting “too long” in terms of inflationary pressures. However, contrary to forecasters’ and even to some FED policymakers’ calls, the first tightening occurred in December 2015, and the second followed last November. We study whether the FED has “fallen behind the curve” (Nechio and Rudebusch, 2016), both in a structural and reduced–form VAR framework. In particular, we first assess the contribution of monetary policy shocks to inflation dynamics, identified by sign restrictions implemented following the algorithm by RubioRam´ırez et al. (2010) in a medium–scale Bayesian VAR model which includes domestic, global, real and financial variables. To capture unconventional monetary policies we use a shadow interest rate, which helps for modelling monetary stance when policy rates hit the zero lower bound (ZLB), as suggested by Krippner (2013) and Wu and Xia (2016) among others. In order to do so, we borrow their estimated shadow rates available on their respective websites1 . Next, we use the same Bayesian VAR model to forecast US PCE core inflation over 2017–18, both unconditionally and conditionally on possible future paths of the Fed Funds rate (FFR hereafter) and oil prices, relying on the methodology pioneered by Waggoner and Zha (1999) and then refined by recent contributions on conditional forecasts in large Bayesian VAR models by Giannone et al. (2014) and Banbura et al. (2015). Furthermore, we conduct a scenario analysis which compares a counterfactual FFR consistent with business cycle and labor markets dynamics to the observed FFR dynamics. This evaluation is informative on the adequacy of FED’s monetary stance. Our results are the following. First, the Structural VAR (SVAR) model reveals that monetary policy 1 See
the on line Appendix to this paper for details.
Preprint submitted to Economics Letters
shocks had a moderately expansionary effect on inflation in 2016, after being barely contractionary in the last quarter of 2015. Accordingly, they were moderately supportive of economic activity and employment conditions. Second, the results of the forecasting analysis are reassuring on the appropriateness of the FED’s stance. Indeed, the model foresees a slowing of PCE inflation, at around 1.4%, under the assumption of a range of between 4 and 6 hikes within the period 2017–2018. The forecast for PCE core inflation is below the estimates released by the FED in December 2016 and those surveyed in the SPF, but it is very similar to the prediction by the NY FED Large DSGE model (Del Negro et al., 2013). Finally, a conditional forecasting scenario deems the actual FFR dynamics consistent with the one predictable by the FED had the future business cycle pattern been fully available at the end of 2014. This suggests that the FED has not been too cautious towards inflation. In Section 2 we outline the framework, present the data and report the findings of the structural analysis. We describe the conterfactuals and their results in Section 3, whereas in Section 4 we briefly conclude.
2. Bayesian VAR framework To tackle our research question, we adopt a Bayesian VAR model, a flexible tool which provides a realistic empirical representation of data. By contrast, its main limitation lies in its linearity, which makes it somewhat ill-suited to deal with potential non–linear effects of shocks at the ZLB. To some extent, however, this limitation may be dampened by the use of a shadow interest rate to capture negative monetary policy rates at the ZLB (see, in particular, Krippner, 2013; Wu and Xia, 2016). Furthermore, the shadow rate is clearly linked to the size of the FED’s balance sheet, commonly adopted by studies on unconventional monetary policies: the correlation between the two variables is equal to -0.8 (Figure 1). June 17, 2017
Figure 1: FED monetary policy stance
evidence in Laseen and Sanjani, 2016; Abbate et al., 2016). Log transformation is applied, except for unemployment, interest rates and the EBP.3 Identification of A0 relies on the sign restrictions approach proposed by Rubio-Ram´ırez et al. (2010), imposed on impact, which allow us to disentangle five structural shocks: other than monetary policy, we also identify shocks to oil supply, global demand, domestic aggregate demand and supply (see Table 1). Table 1: Sign restrictions Shock Variable ot wyt pt yt S Rt qt wt urt EBPt
Notes: Green shaded area: FED Total Assets. Interest rates are plotted on the right-hand scale. Blue lines: FFR (straight); shadow interest rate by Wu and Xia (dashed); shadow interest rate by Krippner (circled).
2.1. Model Let yt be an n × 1 vector of endogenous macroeconomic variables. Their joint dynamics is described by the following system of equations: p X 0 0 0 0 iid (1) yt− j A j + εt , εt ∼ N(0, In ) y t A0 = c +
Oil Supply
Global Demand
– + – +
– – – – – +
Aggregate Demand
Monetary Policy
Aggregate Supply +
– – – – – +
– – + +
– +
+ +
Notes: A “+” (or “–” ) for the exchange rate implies that the structural shock leads to an appreciation (depreciation) of the US dollar vis-`a-vis its main trade partners. Blank entries: no restrictions.
The restrictions are sufficient to disentangle the shocks, and, together with the other estimation details, are extensively discussed in the on–line Appendix.4
j=1
where t = 1, 2, . . . , T , c is a n × 1 vector of constants, εt is a n × 1 vector of exogenous shocks A j is a n × n matrix of parameters for 0 ≤ j ≤ p with A0 invertible, p is the lag length, and T is the sample size. The vector εt conditional on past information and initial conditions y0 ..y1−p is gaussian with mean zero and variance covariance matrix In .
2.3. Findings of the Structural analysis Figure 2 plots the historical decomposition of the variables of interest for the FED, focusing on the recent period of low inflation (2013–2016). The five identified shocks explain the bulk of the deviation of inflation from its VAR baseline, validating our identification (Figure 1a). The unexplained part widens in 2016, as inflation increases. This suggests that other shocks are the main drivers of inflation in 2016. The more obvious sources seem to originate from the labor market, such as labor demand or wage bargaining shocks. Although we do not identify these shocks, we stress how high the deviation of wage growth from its baseline is in 2015:Q1,
2.2. Data and Identification of the Structural Shocks The sample includes seasonally adjusted quarterly observations for the period 1987:Q1 to 2016:Q3. The baseline specification includes the real oil price (ot ), world demand (wyt ), PCE price index net of food and energy (pt ), real GDP (yt ), the FFR (replaced by the shadow rate at the ZLB; S Rt ), the nominal effective exchange rate of the dollar against major US partners (qt ), wages (wt , in real terms in the baseline specification),2 the unemployment rate (urt ) and the EBP by Gilchrist and Zakrajsek (2012) (EBPt ), which measures credit markets stress and is important in explaining US inflation dynamics (in this regard, see, among others, VAR
3 Our baseline specification improves on the structural analysis carried out by Conti et al. (2015) and Bobeica and Jarocinski (2017) by incorporating labor and financial variables. 4 Sign restrictions follow theoretical and empirical prescriptions (Kilian, 2009; Rubio-Ram´ırez et al., 2010). We leave to further research the implementation of the influential contribution recently made by Baumeister and Hamilton (2015) on the informativeness of allegedly uninformative priors.
2 Considering nominal wages does not alter the results. See the Appendix for more details.
2
Figure 2: Historical decomposition
same time span the role of other shocks (i.e., not identified, depicted by the orange bars) turns predominant in explaining unemployment dynamics. The broad picture looks consistent with our findings of the five identified shocks accounting for around 30 and 60% of wage growth and unemployment(Figure 3c–d). We now turn to monetary shocks, as their contribution to inflation dynamics may help for understanding whether, and to what extent, the FED is driving inflation upwards. Figure1a shows that their effect is not negligible: the contribution of monetary policy shocks identified by means of the shadow rate is, on average, modest. In fact, it is positive from late 2013 until the fourth quarter of 2015, when it turns barely negative as the FED raised the FFR for the first time since 2007. In 2016 monetary policy contribution was slightly positive, however marginal with respect to the contribution of other shocks. Additionally, monetary policy shocks are not significant in shaping the deviation of real GDP growth from its baseline (Figure 1b). Thus, the SVAR model demonstrates the modest although expansionary - role played by monetary policy shocks in shaping inflation in 2016. This seems to contrast the idea that FED’s actions could spark a burst in inflation in the coming years. However, since the most recent widening in the deviation from core inflation baseline does not allow for a precise answer to our question of interest, we turn to the second part of the empirical analysis. 3. Conditional forecasts and scenario analysis We complement the structural analysis of monetary shocks by running a forecasting analysis. We estimate model (1) on the full sample and then use it to forecast inflation, both unconditionally and conditionally. As for the latter, we follow an approach similar to the one used by Giannone et al. (2014) in designing two different exercises. In the first exercise, we ask what would happen to PCE core inflation (and other variables such as economic activity) should the policy rate be raised by 1 percentage point in 2017, i.e. the four hikes under current debate.5 We then examine the obtained unconditional and conditional forecasts to detect possible overshooting of PCE core inflation with respect to the target. To be as realistic as possible we include the most recent rebound of oil prices, as we impose this exogenous path among the conditions as well.6
Notes: Black line: deviation of the actual variable from its VAR-baseline; bars: shocks’ contribution. From the top to the bottom: inflation, real GDP growth, wages growth, unemployment rate. Sample: 1987:Q1 - 2016:Q3.
before reverting to more moderate levels. To the extent that wage dynamics produces inflationary pressures, one may argue that labor market shocks translated into PCE increase in recent quarters. Accordingly, in the
5 We
also condition on two hikes by 25 basis points each in 2018. January 2017 Oil prices were envisioned at around 58$ in 2018Q4. They are now floating around 56$ at the same horizon (see 6 In
3
Figure 3: Forecasts
We subsequently ask a related question: what would the FFR implied by model (1) have been had the FED observed business cycle dynamics in 2015-2016? A counterfactual FF higher than the actual would signal that the FED is behind the curve. 3.1. Findings of the counterfactuals Figure 3 shows the results of the first simulation. The unconditional forecast shows PCE inflation growing to 1.7% in the fourth quarter of 2016, before gradually falling in 2017, then stabilizing at around 1.2%. This reflects the FFR path, foreseen by the model around 1.2% at the end of 2017, implying two hikes of 0.25 basis points each, and a rise in oil prices (Figure 3c-d). As for economic activity, in spite of the less expansionary monetary stance, real GDP experiments a growth by 2.5%, subsequently slowing at 2.1%. Conditional forecasts describe a similar picture: inflation is somewhat higher because of the higher oil prices. In fact, differences between the imposed path of the FFR and its unconditional forecast only appear in 2018. The average predicted PCE core inflation is equal to 1.3% and 1.4% in 2017 and 2018, respectively, when conditioning on the imposed path for oil prices and the FFR. These values basically coincide with those delivered by the FRB-New York DSGE Model (Table 2; Del Negro et al., 2013), but are lower than the analogous released by the FED Board (FOMC) and those surveyed in the Philadelphia SPF.7 In Figure 4 we show the results of our scenario analysis. The conditional forecast is obtained estimating model (1) over the period 1987:Q1 to 2016:Q3, assuming to know in 2014:Q4 the future realized business cycle and labor markets developments. In practice, should the actual value of the policy rate be below its conditional forecast, we could conclude that the FED approach was too cautious. This does not seem to be the case, as indeed the policy rate always lies above its conditional forecast.8 the on line Appendix for more details. 7 Results are qualitatively confirmed when extending the sample up to the first quarter of 2017. See the on line Appendix for details on forecasts obtained after estimating the Bayesian VAR model until 2017:Q1, the most recent data available. 8 The same results hold when conditioning on business cycle dynamics over the period 2014:Q1-2016:Q3. In the Appendix we also show that the actual FFR dynamics is in line with its counterfactual when assuming to know the future path of PCE core inflation as well.
Notes: Black line: actual variable; blue line: median conditional forecast obtained assuming the knowledge of the future path of the FFR and oil prices; red dots: unconditional forecast; blue (red) shaded area: 68% credibility interval.
4. Conclusions We have investigated whether the FED has fallen behind the curve, i.e. has waited too long before start4
Table 2: Forecasts comparison
flation is still weak, probably lying below the target in 2017.
PCE - core 2017
Acknowledgements
2018
Unconditional
1.2
1.3
Conditional
1.3
1.4
FRBNY - November 2016
1.2
1.3
FRBNY - February 2017
1.4
1.4
FOMC - December 2016
1.8
2.0
FOMC - March2017
1.9
2.0
SPF - November 2016
1.9
1.9
SPF - February 2017
1.9
2.0
I thank the Editor Pierre-Daniel Sarte and an anonymous referee for their helpful comments. I would also like to thank Robert Kollmann for a constructive discussion over a previous draft of this paper. I am particularly indebted to Diana Rocco for proofreading and language help. Part of this project was developed during my visiting at EIEF, whose hospitality is gratefully acknowledged. The views here expressed are those of the author and do not necessarily involve those of the Banca d’Italia or the Eurosystem. Appendix A. Supplementary data Supplementary material related to this article can be found online.
Notes: Median forecasts, annualized percentage points. Y-o-y averages. FRBNY entries refer to the large DSGE model used by the NY FED (Del Negro et al., 2013), while FOMC denote the forecast released following the FED Monetary policy committe. SPF entries refer to the Philadelphia FED Survey of Professional Forecasters.
References Abbate, A., Eickmeier, S., Prieto, E., Sep. 2016. Financial shocks and inflation dynamics. CAMA Working Papers 2016-53, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University. Banbura, M., Giannone, D., Lenza, M., 2015. Conditional forecasts and scenario analysis with vector autoregressions for large crosssections. International Journal of Forecasting 31 (3), 739–756. Baumeister, C., Hamilton, J. D., 09 2015. Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information. Econometrica 83 (5), 1963–1999. Bobeica, E., Jarocinski, M., 2017. Missing disinflation and missing inflation since the Great Recession: a VAR perspective. ECB Working Papers forthcoming, European Central Bank. Conti, A. M., Neri, S., Nobili, A., Jul. 2015. Why is inflation so low in the euro area? Temi di discussione (Economic working papers) 1019, Bank of Italy, Economic Research and International Relations Area. Del Negro, M., Eusepi, S., Giannoni, M., Sbordone, A. M., Tambalotti, A., Cocci, M., Hasegawa, R. B., Linder, M. H., 2013. The FRBNY DSGE model. Tech. rep. Giannone, D., Lenza, M., Momferatou, D., Onorante, L., 2014. Shortterm inflation projections: A Bayesian vector autoregressive approach. International Journal of Forecasting 30 (3), 635–644. Gilchrist, S., Zakrajsek, E., June 2012. Credit Spreads and Business Cycle Fluctuations. American Economic Review 102 (4), 1692– 1720. Kilian, L., June 2009. Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market. American Economic Review 99 (3), 1053–69. Krippner, L., 2013. Measuring the stance of monetary policy in zero lower bound environments. Economics Letters 118 (1), 135–138. Laseen, S., Sanjani, M. T., Jul. 2016. Did the Global Financial Crisis Break the U.S. Phillips Curve? IMF Working Papers 16/126, International Monetary Fund. Nechio, F., Rudebusch, G. D., November 2016. Has the Fed Fallen behind the Curve This Year? FRBSF Economic Letter (33).
Figure 4: Scenario: FFR dynamics and the business cycle
Notes: Black line: actual variable; blue line: estimated median conditional forecast obtained assuming the knowledge of the future path of real GDP, unemployment and wages; blue shaded area: 68% C.I. Estimation sample is 1987Q12016Q3. In-sample forecast period is 2015Q1-2016Q3.
ing its lift-off from the extraordinary monetary stimulus. Our evidence, based on both structural and reducedform VAR models shows that the FED monetary stance is appropriate given macroeconomic developments. In spite of favourable labor markets signals, PCE core in5
Rubio-Ram´ırez, J., Waggoner, D. F., Zha, T., 2010. Structural vector autoregressions: Theory of identification and algorithms for inference. The Review of Economic Studies 77 (2), 665–696. Waggoner, D. F., Zha, T., November 1999. Conditional Forecasts In Dynamic Multivariate Models. The Review of Economics and Statistics 81 (4), 639–651. Wu, J. C., Xia, F. D., 03 2016. Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound. Journal of Money, Credit and Banking 48 (2-3), 253–291.
6
Has the Fed Fallen Behind the Curve? Evidence from VAR models •
We ask whether the FED has riskily delayed the exit from its large monetary easing
•
We focus on the effects of monetary policy on US PCE core inflation
•
Monetary policy shocks are not pushing inflation much beyond its baseline
•
Scenario analysis shows that the FED’s stance is in line with macroeconomic dynamics
•
PCE core inflation will likely lie below the FED’s target in 2017