The Bahrain Economy: A Var Analysis

ABSTRACT: The last decade or so has witnessed a significant growth in research studies applying Vector Autoregression (VAR) technique for macroeconomic modelling. A VAR technique pioneered by Sims and popularized by researchers such as Litterman and Doan is useful particularly when knowledge about "true" structural relations is absent. This study represents the first attempt to apply such a technique to Bahraini yearly data (1971 - 2002) for five key macroeconomic variables. The results of the study indicated that all key macroeconomic variables are interlinked and influence each other. Oil exports is pure exogenous and is unaffected by any other macroeconomic variable. There is an evidence of bi-directional money-income causality and uni-directional causality from Government expenditure to CPI.. Impulse response and variance decomposition analysis suggest that fiscal policy is relatively more effective in short-run and monetary policy is more effective in the long run. Both oil exports and money are the sources of variation in GDP in the long-run and Government expenditures is the source of variation in the short-run. The result also suggests that money and oil exports are the important sources of variations in CPI in the long-run and Government expenditures is important source in the short-run. Inflation is found to be a fiscal phenomenon in the short-run but monetary phenomenon in the long-run. This result supports the view of monetarists.

KEYWORDS: VAR MODELLING, MACROECONOMIC POLICIES, OIL EXPORTS, BAHRAIN

1. INTRODUCTION

Being pioneer of oil producer in the Arabian Gulf region, Bahrain witnessed the prospects of potential economic prosperity in 1932 with the discovery of oil. Although oil exports contributed significantly in achieving higher levels of GDP over past few decades, its volatile nature (because of oil prices) and gradually decreasing share in GDP provided a challenge of maintaining higher levels of GDP. As a result, export base was diversified to non-oil products like Petrochemicals and Aluminium whose share in GDP has gradually increased. Inspite of diversifying sources of GDP, the rates of real GDP growth have showed wide fluctuations of more than 8 percent to negative 2 percent, over the period of last ten to fifteen years.

In order to analyze the sources of fluctuations in GDP growth, a standard complete structural macro model is probably desirable. However, such a model is derived on the basis of economic theory. Thereby two major problems arise: the theory must be exact enough to identify the endogenous and exogenous variables and the functional form connecting them. The second problem concerns the identification problem of recovering structural parameters from estimated reduced form. Out of these problems another class of nonstructural models: Vector autoregressive (VAR models) have been evolved, pioneered by Sims (1980) and popularized by researchers such as Litterman (1984) and Doan(1984). VAR model does not require any explicit economic theory to estimate a model. It uses only the observed time series properties of the data to forecast economic variables.

The VAR models have many applications (see Cooley and Leory, 1985). They are used to determine how each endogenous variable responds over time to a shock in that variable and in every other endogenous variable. VAR models are useful for analysis of the effect of alternative monetary or fiscal policies (Sims, 1982). The VAR models also provide a straightforward way of predicting the values of set of economic variables at any given point in time.

Our study represents the first attempt to apply such an approach in the case of Bahrain. In this paper, we develop and estimate an annual macroeconometric model for the economy of Bahrain over the period 1971 to 2002 using VAR technique proposed by Litterman (1984) and Sims (1980, 1982 & 1986). The main focus of this study is to analyze empirically the strength of short-run and long-run impacts of anticipated and unanticipated macroeconomic policies and oil exports shock (or innovations) on Bahrain's macroeconomy.

The paper is divided into five parts. VAR approach is outlined next, followed by a discussion on the data used to estimate the model. The next part discusses the empirical results, concluding with the summary and conclusions.

2. THE VAR METHODOLOGY

The methodology of the VAR is briefly described here. A k-equation VAR can be represented in a matrix form as follows:

A(L)Yt = A + Ut (1)

and

A(L) = I - H1L1 - H2L2 - .....HkLn (2)

Yt is an kx1 vector of variables, A is an kx1 vector of constants, and Ut is an kx1 vector of random variables. Equation (2) is an kxk matrix of normalized polynomial in lag operator L (Lm Yt=Yt-1) with the first entry of each polynomial on A's being unity.

Since the right-hand side of the equations in the system contains only the predetermined variables, the error terms are assumed to be serially uncorrelated with constant variance and zero mean.

Hence, each equation in the system can be estimated using OLS. Moreover, OLS estimates are consistent and asymptotically efficient. Even though the error terms are correlated across equations, Seemingly Unrelated Regression (SUR) do not add to the efficiency of the estimation procedure since all regressions have identical right-hand-side variables. However, before estimating the model, the lag length must be chosen. If L is the lag length, number of coefficients to be estimated is k(kL + c), where c is the number of constants. The VAR model presented above indicates that the current innovations (Ut) are unanticipated but becomes part of the information set in the next period. The implication is that the anticipated impact of a variable is captured in the coefficients of lagged polynomials while the residuals capture unforeseen contemporaneously events. A joint F-test on the lagged polynomials provides information regarding the impact of the anticipated portion of the right-hand side variables. The impact of the unanticipated policy shocks (i.e. the policy variables such as changes in money supply and government expenditures) on other economic variables can be analyzed by employing the "impulse response functions" (IRFs) and "variance