Macroeconomic Decision Support for Optimal Policy Making: The Case of Turkey

Copyright © IFAC Supplementary Ways for Improving International Stability, Vienna, Austria, 1995

MACROECONOMIC DECISION SUPPORT FOR OPTIMAL POLICY MAKING: THE CASE OF TURKEY

Soh bet Karbuz

Department of Economics University of Bielefeld, Germany

Abstract: Planning in macroeconomic policy making (still) plays an essential role in the development process of developing countries. The experience of planning however, witnesses failure in the use of appropriate quantitative tools. In the study, a macroeconomic decision support framework for policy optimization is suggested and its application via optimal macroeconomic policies for the Turkish economy over the sixth five year development plan period (1990-1994) is illustrated. The results show that policy optimization is a very powerful tool for forming macroeconomic policies, at least in Turkey. Keywords: economics, developing countries, multicriteria optimization, optimal control.

I. INTRODUCTION

Applying optimization methods to the projections of the macromodels is a prerequisite for designing optimal macroeconomic policies in developing countries. The aim is then to find the values of the policy instruments that yield the optimal, best compromised, most economically desirable and politically attainable values for the objective variables of the economy analyzed.

The crises of macroeconomic management in many developing countries over the past two decades have prompted a new interest in macroeconomic policymaking in recent years. Although this new interest also addresses long-term issues of structural reforms, the main focus has been on macroeconomic stabilization in the short-term. However, problems that have strong short-term manifestations must be seen through long-term strategies. It is the development planning that offer such possibilities.

A small-scale macroeconometric model will be introduced for the Turkish economy in the next section. In section three economic policy formulation using optimization methods will be discussed. Setup for the applications of two different optimization methods (the algorithm OPTCON as an optimum control method, and the reference point algorithm as a multicriteria optimization method) to the same model will be discussed in section fOIf; In section five, the comparisons of the results obtained in the simulation and optimization experiments were made with their available actual values, the forecasts of the OECD and of the State Planning Organization of Turkey (SPO). Some remarks about the study and suggestions for future research conclude the study.

Economic policy making can be seen as an interdisciplinary approach consisting macroeconomics, development planning, econometrics, simulation, optimal control, and multi criteria optimization in a decision support framework. Model building, estimation and simulation are the most common used procedures for economic policy analysis. As long as these procedures are not supported by quantitative optimization techniques, the results are rather misleading, insufficient and ineffective.

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2. AN ECONOMETRIC MODEL OF THE TURKISH ECONOMY

3.1 Stochastic Optimum Control Approach Optimum control theory has been used in several studies to determine optimal policies for econometric models (Chow, 1975, 1981 ; Kendrick, 1981 ; Neck and Karbuz, 1995). The algorithm OPTCON (Matulka and Neck, 1992), which is used in this study, determines approximate solutions of stochastic optimum control problems with a quadratic objective function and a nonlinear multivariable dynamic model under additive and parameter uncertainties.

A small-scale dynamic, non-linear macroeconometric model based on familiar theoretical considerations was developed and estimated (using ordinary least squares and three stages least squares) over the period from 1970 to 1989. It was tested and then simulated (including stochastic simulation) between the years 1980 and 1994. The ex-ante forecast horizon was chosen to be 1990-1994, which covers the sixth fiveyear development plan period of Turkey. The assumed or projected values of the exogenous variables of the model were taken, as far as possible, form the SPO (1989) and the OECD sources. Some exogenous variables were forecast by time series methodology. For the years 1990 and 1991 actual values of the exogenous variables were used. All nominal and real variables in the model are in billions of Turkish Liras. The estimation and test statistics together with simulation results suggest that the model is not far-off the mark as a framework for macroeconomic analysis of the Turkish economy. Estimated parameters of the model generally conform to standard economic theory, and in many cases approximate those that are available in the literature.

The objective function is assumed to be quadratic in the deviations of the state and control variables from their respective desired values. ,

1

-

2

Ut

,·1

where

Yt

and

Ut

Wt

[Y'

-

Ut

:'1

(2)

Ut

t=l ,oo.T

denote the vectors of state and control

variables respectively, Yt and Ut denote desired levels of these variables, and T denotes the terminal period of the finite planning horizon. The matrix W t contains the weights given to the respective deviations of state and control variables from their desired values. a is the discount factor of the objective function . The algorithm starts by computing a tentative path of the state vector from the system equations with the given tentative path for the control variables using the Gauss-Seidel algorithm. It then linearizes the system equations at the reference values obtained before, replacing the nonlinear time-invariant system by a linear time-varying one. The parameter matrices of the linearized system equations are functions of the random parameter vector. These functions are approximated by linear functions by computing the derivatives of the parameters of the linearized system with respect to the parameter vector. These derivatives are also used to evaluate the variancecovariance matrices of the parameters of the linearized system equations and the expected values needed for the Riccati equations. Using dynamic programming, linear feedback policy rules are derived. The feedback rules and the state equations are used to compute the sequence of controls and states forwards in time. The resulting values for the state and control variables are used as new tentative paths, and another iteration is started by linearizing the system along these values etc., until convergence is reached. The software OPTCON Version 2.2 (see, Karbuz, et al. , 1994) was used in the calculations of the control experiments.

3. ECONOMIC POLICY FORMULATION USING OPTIMIZA TION METHODS Macroeconomic policy problems can be viewed as involving the optimization of an intertemporal objective function by a policy maker (PM ) who is constrained by a dynamic system subject to various kinds of uncertainties in the following form :

e, E) = O}

Ut

Wt=a W

The model contains 10 behavioral equations and 7 identities. It is basically Keynesian but contains some neoclassical and developing country specific elements as well. Aggregate demand is the driving force of the economy, production is assumed to adjust. The fundamental building blocks of the model are ; components of real aggregate demand, prices and wages, labor market, money market, formation of income, and the public sector. The main instrument variables of the Turkish economy are related directly or indirectly to the main objective variables. The details about the model is given in Karbuz (1994).

min '{; {J(Y, U) I F(Y, Z, U,

I [Y'

:,]

-

(1)

where g' denotes expectati r n, Y denotes the endogenous variables, Z an T denote the non5 and controlled controlled exogenous var variables respectively. J denote'") the policy objective function , F is the nonlinear model of the economy, E is the vector of random noise, and e is the vector of unknown parameters. 48

3.2 Multicriteria Optimization Approach

where Rk's are calculated as surpluses from the equation (4) and considered as constants, assuming some path for the x and y variables. If this equation is partitioned in parts and is rearranged, the following multicriteria optimization problem is obtained:

The history of macroeconomic policy-making has exhibited a permanent interest in the study of incompatible and conflicting objectives. The economists' first concern that could be characterized as a multicriteria problem was the efficient allocation of resources . Since then there has been a growing interest on the use of multi criteria decision techniques in the problems of economics. A useful survey is provided by Wallenius (1982) . However, there is a continuing need for applications in the field of macroeconomic planning. There is evidence, at least in Finland, that the public sector is an increasingly interested user of mUltiple criteria decision making (MCOM) methods (Dyer, et al. , 1992 ).

k=I , ... ,5

where y~bj and Ut denote the vectors of objective and control variables respectively. Superscript asteriks indicate the optimal values of the instruments which were obtained over the periods from t to t+k-1. For the period t+k the assumed values, which had been used for forecasting, were taken as a reference path for the instruments. After the first optimization, which takes place at time t, the optimal values for the instruments are given as a set of constraints in the second optimization, which takes place at time t+ I.

The reference point approach (Wierzbicki, 1980), which is used in this study, combines the advantages of the method of goal programming and the method of displaced ideals. The basic idea in this approach is to rank multidimensional decision alternatives, q, relative to a reference point q. A reference point is a suggestion by the PM which reflects in some sense the desired levels of the various objectives. An achievement scalarizing function s(q, q) defined over the set of objectives vector q, may be associated with the reference point. The problem is to find a point q in the Pareto set that is nearest to the reference point. With this interpretation in mind, reference point optimization may be viewed as a way of guiding a sequence { q k} of Pareto points

4. SETUP OF THE OPTIMIZATION EXPERIMENTS In optimum control and multicriteria optimization experiments, the level of real GNP, the level of prices and the rate of unemployment were considered to be objective variables. The exchange rate, import duties, net indirect tax ratio, discount rate, government consumption and domestic credits were considered to be policy instruments.

generated from a sequence of { q k} of reference objectives. These sequences are generated in an interactive procedure and this should result in a set of attainable non inferior points {q k} of interest to the PM . Policy maker adjusts his preferences and uses them in interaction processes in order to move towards his desired or justified solution.

4.1 Setup a/the Experiments with OPTCON In optimum control experiments, 7% p.a. was considered as the desired growth rate of real GNP and 6% as the desired level for the unemployment rate . For the price level, it was assumed that the hypothetical PM wants to reduce the rate of inflation gradually, a 7% decrease between 1991 and 1994 so that inflation rate will be 35% in 1994.

The first thing to do before using the reference point approach is to linearize the model described in section 2. Following B6hm and Brandner (1988) it is assumed that the dynamic multipliers are coefficients of the linearized final form of the model which is approximated by finite differences YI

=

It was assumed that 1989 historical values of the control variables to be given and postulated desired growth rates of 70% for import duties, (80% in reality), 6% for government consumption, (more than 60% in reality) . The net indirect tax ratio has a desired constant value of 0.6 for all periods. For the interest rate an increase of five percentage points in each period was assumed. The exchange rate was assumed to follow a decreasing gt..ewth path (depreciation) until the end of the planning horizon.

I

L:'l'jXI_j + RI

(3)

j= O

where the remainder term (Rt ) includes the approximation error and lags of higher order as well as the error dynamics. 'l'j'S are the relevant matrices of dynamic multipliers. For multiperiod problems this equation can be rewritten as the following

In the weight matrix all off-diagonal elements are set equal to zero, and the main diagonal elements were given weights of 10 for the objective variables, and of 7 for the first, third and fourth control variables and of 1 for the second, fifth and sixth control variables.

k

YI+k

= L:'l'k_jXI+j + Rk

, k=I ,... ,5

(5)

(4)

j= O

49

Several experiments varying the weights for the objective and control variables were performed. The weights given above seemed to serve relatively the best satisfactory values for the variables in question.

will be discussed. The results of these different procedures for the objective variables are given in Table 1. The results for inflation and real GNP growth for the period 1992-1994 are compared to the forecasts of the OECD and the SPO in Table 2.

Two kinds of optimum control experiments were performed. First, for the deterministic experiment, it was assumed that all parameters of the model to be known with certainty. The only stochastic influences considered were the additive error terms in the behavioral equations, whose variances contribute to the optimal value of the objective function but do not affect the optimal policies. Second, a full stochastic experiment was performed. The aim of this experiment was to get some information about how optimal policies look when there is high amount of uncertainty about the parameters ofthe model.

Table 1 Comparisons of the Results of the Applied Procedures for the Objective Variables

4.2 Setup a/the Experiments with DIDAS The reference point algorithm, which is used in the software program DIDAS (Rogowski, et al., 1988), requires upper and lower bounds for the instruments. These bounds over the planning period were attained in such a way as to keep them in reasonable and politically desirable intervals.

Price Lel,ei (1985=100) ACTUAL

SIMULATED (OLS) SIMULATED (3SLS) OPTCON (deler.) OPTCON (sloch.) MCDM Rea/GNP ACTUAL SIMULATED (OLS) SIMULATED (3SLS) OPTCON (deler.) OPTCON (slOch.) MCDM Unemp/uYlllelll /lale ACTUAL SIMULATED (OLS) SIMULATED (3SLS) OPTCON (del er.) OPTCON (sloch.) MCDM

1990

1991

1992

1993

769.8

1201.4

*NA*

1263.4 1246.3 1140.8 1104.3 1183 .2

*NA*1 1951.5 1894 .7 1643.4 1605.0 1866.0

*NA*

816.4 802.7 767.3 709.1 744 .8

2885.4 2738.4 2281.1 2239.4 2478.5

4219.4 3951.4 3063 .1 3215.8 3424.3

1990 373 14 36473 36127 36675 36626 37600

1991 37860 37065 36792 39242 39 153 38430

1992 *NA* 38789 38544 41989 41897 40700

1993 *NA* 41382 41275 44928 44889 44810

1994 *NA* 43915 44182 48073 47942 48170

1990 7.96 8.51 7.7 1 7.60 8.08 7.55

1991 7.92 8.47 7.66 6.56 7.64 7.47

1992 *NA* 9.05 8.32 6.57 7.76 6.87

1993 *NA* 9.40 8.74 6.80 7.79 6.44

1994 *NA* 9.56 8.95 6.96 8.20 6.06

1994

The main result that can be drawn from the table is that in almost all cases, optimization algorithms give much more satisfactory results than those of simulation. Moreover, the optimization results compared to the actual values suggest that the economy could have performed better under optimization whereas simulation of the model does not confirm that. For the real GNP and the price level, the differences between simulation and optimization are not obvious in 1990. However, this difference becomes remarkable through the end of the planning horizon. In the case of the rate of unemployment, deterministic optimization and MCDM provide more satisfactory results.

A devaluation in the exchange rate was allowed to be not more than 40% compared to its value in the previous period. The reason for this was to minimize the inflationary effect caused by the increases in the exchange rate. Moreover, the upper bounds for this variable were set in such a way that it could never have a value which was greater than its historical highest and those obtained in optimal control experiments. This was also the case for import duties. The discount rate was allowed to have a constant growth path of five percentage points in each period. The motivation for this was the hope that inflationary expectations will be broken by reducing the growth path of the exchange rate. This could negatively affect speculative investments in the sense that investing in foreign exchange would never bring a profit which could be obtained by interest rate . Real government consumption and nominal domestic credits were allowed to have a maximum 15% and 80% increase per annum respectively. During the interactions an effort was made to find the relatively optimal, politically attainable and desirable results in each of the planning period.

The target level for the unemployment rate could be achieved with about 10% excess difference. This is due to an indirect trade-off between inflation (and even growth rate) and unemployment which implies the existence of Phillips curve and Okun's law that are not explicit in the model. Therefore, joint optimization of these three variables is not possible. Moreover, the introduction of uncertainty (inclusion of variance covariance matrix of the parameters) did not dramatically change the results compared to those obtained from the deterministic experiment.

5. COMPARATIVE EVALUATION OF THE RESULTS

In MCDM experiments it was seen that a small increase in prices caused a bigger increase than if the

In this section, a comparative evaluation of the results obtained from two different types of simulations, two types of optimal control, and one MCDM method

I Not available.

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optimizations. In the first year, the stochastic experiment allows for a very strict policy thereby reducing the rate of interest, which increases the demand for money. That is also the case in the remaining periods. The reason for the strange jump in 1994 might be to compensate the gap created by the very small increase in the exchange rate, between the desired value and the calculated value.

situation was reversed. Therefore, it was tried to keep the rate of unemployment at its lowest level at the relative expense of prices. As can be seen from the Table 2, in all cases optimization algorithms provide low inflation rates except the ad hoc one given by the SPO. In the plan it is only indicated that inflation will be reduced to 13.5% in 1994. The results for the real GNP growths are in line with the SPO forecasts . The OECD forecasts for the real GNP growth are, in general, the lowest among others. Except for 1992, MCDM provides higher growth rates compared to the others.

Table 3 Comparisons ofthe Results of the Applied Procedures for the Instruments 1991 4171.8 4639.9 4171.9 3972.0

import duties ACTUAL OPTCON (det.) OPTCON (stc .) MCDM

1990 4911.4 5426.1 5984.1 6449.0

1991 9311.6 8919.1 9282.7 9000.0

illleres/ rale ACTUAL OPTCON (det.) OPTCON (stc .) MCDM

1990 54.0 53.4 43.3 50.0

1991 45.0 59.1 60.7 55.0

N. ind. lax rafio

ACTUAL OPTCON (det.) OPTCON (stc.) MCDM

1990 0.1 0.1 0.1 0.1

1991 0.10 0.03 0.03 0.09

Govn " cons. ACTUAL OPTCON (det.) OPTCON (stc.) MCDM

1990 5021.7 4616.6 4010.8 4895.7

1991 5324.9 4893.9 4653.7 5300.0

Domestic credit ACTUAL OPTCON (det.) OPTCON (stc.) MCDM

1990 1991 59755 93631 54216 86746 53943 86597 71510 100000

ACTUAL (OECD)

Table 2: Comparison with the OECD and the SPO Inflation (%) 1992 1993 1994 Simulation (OLS) 54.5 47.9 46.2 Simulation (3SLS) 52.0 44.5 44.3 OPTCON (deter.) 44.1 38.8 34.3 OPTCON (s!och.) 45.4 39.5 43.6 57.7 32.8 38.2 MCDM OECD 62.1 60.0 58.0 *NA* *NA* I3.5 SFYDP. SPO

OPTCON (det.) OPTCON (stc.) MCDM

1990 2608.6 2899.7 2243.7 2088.0

Exchange rale

Real GNP Growth (%) 1992 1993 1994 4.7 6.1 6.7 7.1 7.0 4.8 7.0 7.0 7.0 7.1 6.8 7.0 5.9 10.1 7.5 4.5 5.0 5.3 7.0 7.0 7.0

From the above presented results it would be too early and, indeed, inappropriate to conclude that optimization is the best way to form macroeconomic policies over the sixth five year development plan of Turkey. A necessary condition for a conclusion is to analyse at what expense these values for the objectives are reached. Since it is the values of the instrument variables that determine the outcome of the objective variables, in Table 3 a comparative evaluation of the instruments is given. In the model, the changes in the exchange rate reflect the expectations of people on inflation; therefore, small increases in this variable contributed to the small increases in the prices. In general the OECD estimates for the exchange rate are higher than those obtained from the optimizations. Although during the first two periods of the planning horizon, the values obtained from optimum control experiments for the exchange rate are higher than its historical values the situation is quite different in the remaining periods. MCDM, however, provides a policy where exchange rates are set at even less than the full stochastic case. The last two periods in particular show how an exchange rate policy can be used, eliminating the inflation expectations and without violating the policy-makers desired values for the objective variables.

1992 6861.0 6649.6 6380.3 5264.0

1993 1994 10750.0 16180.0 7980.2 9575.4 7804.9 9549.4 6733.0 7406.0

1992

1993

1994

*NA*

*NA*

*NA*

15161.4 15249.6 15000.0

25775.1 43816.3 25584.2 43635.2 25000.0 43500.0

1992

1993

1994

*NA*

*NA*

*NA*

62.7 63.2 60.0

64.9 64.5 65.0

103.4 75.4 70.0

1992

1993

1994

*NA*

*NA*

*NA*

0.03 0.05 0.09

0.05 0.07 0.09

0.08 0.09 0.09

1992

1993

1994

*NA*

*NA*

*NA*

5187.9 5084.3 5870.0

5498.3 5484.7 6457.0

5825.2 6406.9 7425.0

1992

1993

1994

*NA*

*NA*

*NA*

138793 138701 185500

222069 222013 295000

355310 355300 471800

An increase in government consumption (GCER) causes the level of real GNP go up. This was the case especially for the MCDM method. Here, the desired level of GNPR is achieved by the expansion of government consumption and domestic credits. As was the case for the exchange rate and the rate of interest, stochastic control with its restnct1ve economic policies suppresses GCER as well. The net indirect tax ratio variable follows a cyclical pattern in the control experiments. The control experiments preferred tax-cuts in the earlier periods, but it does not remain temporary. The MCDM gives more realistic results where tax ratio is 10% in the first period and then remains constant until1lhe end of the planning horizon. An increase in the level of nominal domestic credits (DCREDIT) causes the real GNP go up via investments. DCREDIT was transformed to constant prices by using the price level. Therefore, minimization of price level caused the real domestic credits to go up. Moreover, since increases in investments require more people to be employed,

Import duties were found not to be an active instrument. This may be due to the desired path employed for that variable during the optimizations and because of very small coefficients of this variable in the mUltiplier matrices. The actual interest rate values look different from the values obtained from 51

unemployment rate goes down. However, there is a considerably big difference in DCREDIT among optimal control and MCDM due to the tradeoffs between objectives.

REFERENCES Bohm, B. and P. Brandner (1988). Interactive economic policy making with a macroeconometric model: the case of Austria. Research Report # 84. Institut fur Okonometrie und OR. Technische Universitat Wien, Vienna. Chow, G. (1975). Analysis and Control of Dynamic Economic Systems. John Wiley, NY. Chow, G. (1981). Econometric Analysis by Control Methods. John Wiley, NY. Dyer, 1.S, Fishburn, P.C., Steuer, R.E., Wallenius, 1. and S. Zionts (1992). Multiple criteria decision making, multiattribute utility theory: the next ten years. Management Science. 18, 645-654. Karbuz, S., Matulka, 1. and R. Neck (1994). OPTCON: an algorithm for the optimal control of nonlinear stochastic models, user manual. Discussion Paper #286. University of Bielefeld, Germany. Karbuz, S. (1994). Macroeconomic decision support for Turkey, unpublished Doctoral Dissertation. Technical University of Vienna, Vienna. Kendrick, D. (1981). Stochastic Control for Economic Models . McGraw-HilI, NY. Matulka, 1. and R. Neck (1992). OPTCON : an algorithm for the optimal control of nonlinear stochastic models. Annals of Operations Research. 37, 375-40l. Neck, R. and S. Karbuz (1995). Optimal budgetary policies under uncertainty: a stochastic control approach. forthcoming in Annals of Operations Research. OECD. Country Survey: Turkey. Several Issues. Rogowski, T., Sobczyk, 1. and A.P. Wierzbicki (1988), IAC-DIDAS-L: A dynamic interactive decision analysis and support system for multicriteria analysis of linear and dynamic linear models on professional microcomputers. WP-88-110. IIASA, Laxemburg, Vienna. State Planning Organization of Turkey (1989). Sixth Five-Year Development Plan. Ankara. Wallenius, H. (1982). Optimizing macroeconomic policy: a review of approaches and applications. European Journal of Operational Research 10, 221-228. Wierzbicki, A.P. (1980). The use of reference objectives in multiobjective optimization . In Multiple Criteria Decision Making Theory and Applications (G. Fandel and T. Gal, Eds.). Springer Verlag, Berlin.

In short, the exchange rate, net indirect tax ratio and, at times, interest rate instruments played the major role in reducing the price level in optimal control experiments. The increases in real GNP were maintained by the direct and indirect effects of real government consumption and domestic credits and partly import duties. Domestic credit contributed to an unemployment reduction. In MCDM experiments, however, EXC was used to reduce the inflation growth, and GC ER together with DCREDIT contributed to GNP growth. As a side effect a continuous reduction in the unemployment rate was successfully achieved.

6. FINAL REMARKS Ad hoc policy decisions can only be of temporary expedience until some sensible principles are established on which optimal policy decisions can be made. The idea of using quantitative techniques in macroeconomic policy-making seems to be that such techniques may be coupled with descriptive/ predictive models developed by economists in order to provide rapid and systematic exploration of the outcomes of alternative policies than is allowed by the present trial and error exploration with the models . The ability to interrogate a macroeconomic model to discover the better ways of achieving certain results given specified tradeoffs between the multiple outcomes at various points in the future, is clearly a great help in exploring possible policies and their good or bad outcomes. The purpose of this study was to guide actual policy makers for preparing their decisions in macroeconomic policy environment. This is done by supporting them with some quantitative techniques in order to be able to improve the quality of the decisions taken. The results presented in this study suggest that policy optimization is a very powerful tool for forming macroeconomic policies, at least in the case of Turkey. The optimization methods that deal with multiple policy-makers should take a priority for future research . Moreover, the models should incorporate not only purely economic factors but political factors and cultural constraints as well.

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