سیاست های پولی، شکست ساختاری و مکانیزم انتقال پولی در تایلند

The paper studies monetary policy and the monetary transmission mechanism in Thailand in light of the Asian crisis in 1997. Existing studies that adopt structural VAR approaches do not give a clear and agreed-upon view how monetary shocks are transmitted to the Thai economy that is subject to structural breaks. In our study, we explicitly model a pre-crisis and post-crisis cointegrated VAR model. We support the arguments of Corbett and Vines [Corbett, J., & Vines, D. (1999). Asian currency and financial crises: Lessons from vulnerability, crisis and collapse. The World Economy, 22(2), 155–177] as well as Phongpaichit and Baker [Phongpaichit, P., & Baker, C. (2002). Thailand: Economy and politics (2nd ed.). Oxford: Oxford University Press] that the trinity of open capital markets, pegged exchange rate regime and monetary policy autonomy is inconsistent in the pre-crisis period. In contrast, the model points to an effective monetary policy in the post-crisis period. Further, we analyse the common driving trends of the model.

Thailand has undergone some rapid changes in its monetary policy framework in the last decade. The collapse of its exchange rate, the baht, in 1997 led to the abandonment of the pegged exchange rate regime and movement towards a monetary targeting regime implemented through the IMF program. Following the end of the IMF program an inflation targeting regime was introduced in 2000 where targeting of domestic money supply was replaced by an explicit inflation objective. Given these regime shifts and structural break of the Asian crisis, existing work on the monetary transmission mechanism in Thailand has been unsatisfactory. Many studies such as Patrawimolpon, Rattanalankar, Charumilind, and Ngamchant (2001), Fung (2002), Disyatat and Vongsinsirikul (2003) and Hirunraengchok (2004) build structural VAR models in order to analyse the responses of Thai economic variables to monetary policy shocks. These studies often face econometric deficiencies insofar as they do not deal adequately with the structural break of the Asian crisis, lack the property of a well-specified model or do not account for the nonstationarity of their macroeconomic data. The bottom line is that many of such studies using the structural VAR approach to the Thai economy fail to render a clear and agreed-upon view of how monetary policy shocks are transmitted to that economy. In our study, we propose a different way of studying the monetary transmission mechanism in Thailand based on the cointegrated VAR model. We first estimate an unrestricted VAR model and then determine the relevant long-run cointegrating relationships. Following an identification of the cointegrating vectors, we are able to identify the short-run structure of the cointegrated VAR model and use the model to illuminate the patterns with which monetary policy shocks are potentially affecting key macroeconomic variables in the Thai economy. Hereby, we make usage of the difference between common stochastic trends that have permanent effects on the system variables and shocks to endogenous variables that are only transitory in character. A major challenge to the analysis is that Thailand has been seriously hit by the Asian crisis in 1997. This will make the specification of a model a very difficult task because the relevant variables of the model exhibit jump-like behaviour around the crisis. We anticipate that the underlying stochastic process in the Thai economy has changed following the crisis. Similar to Patrawimolpon et al. (2001) and Fung (2002), we implement a subsample-based analysis, that, is, a pre- and post-crisis model but with the novel approach to rationalize and synthesize the differences between both models. Our results for the pre-crisis model reveal that unlike many studies on Thailand, we cannot recoup an interest rate policy function and that the central bank had difficulty controlling money. These findings support Corbett and Vines (1999) and Phongpaichit and Baker (2002) who state that Thai monetary policy was immobilized in the period before the devaluation of the baht in 1997. They put forward the Mundell–Fleming type argument that the trinity of open capital markets, pegged exchange rate regime and monetary policy autonomy is inconsistent. Regarding the Thai monetary transmission mechanism we find that monetary shocks have permanent positive effects on inflation. We do not obtain a Phillips curve relationship for the pre-crisis model but inflation appears to be a monetary phenomenon and caused by excessive liquidity in the economy. In addition, interest rate shocks have consistently negative effects on output. This could be due to a possible financial accelerator explanation (Bernanke, Gertler, & Gilchrist, 1996). For the post-crisis model, we observe that the cointegrating rank of the model increases. Monetary policy becomes an effective tool to steer the economy by virtue of the fact that the exchange rate becomes flexible, and we are able to obtain an interest rate policy function. We also find a type of money demand function in which money supply is related to the output gap and the exchange rate which could proxy as an opportunity cost. We organize the paper as follows: Section 2 gives a brief literature overview whereas Section 3 provides an overview of the Thai economy as well as the data used in our study. In Section 4, we analyse the long-run relationships in a cointegrated VAR model using a pre-crisis and post-crisis subsample. Section 5 provides evidence for the hypothesis that a full sample model that includes the Asian crisis is unstable. Following the long-run analysis we focus on the short-run dynamics of the model by identifying the common stochastic driving forces (Section 6). Finally, Section 7 concludes with possible extensions to our work.

We attempted to analyse monetary policy and the monetary transmission mechanism in the Thai economy based on a well-specified cointegrated VAR approach. Our findings reveal that any full sample modelling strategy that incorporates the Asian crisis will be misspecified, and policy inferences from such a model are potentially invalid. Our subsample-based modelling strategy is able to account for the effects of the structural break and pinpoint the fundamental differences in both the pre-crisis and post-crisis model. Specifically, we find that Thai monetary policy was potentially immobilized in the pre-crisis period by virtue of the pegged exchange rate regime and the financial liberalization of the Thai economy. Many existing studies that investigate the monetary transmission mechanisms in Thailand do not take account of this fact and make assumptions of the monetary policy stance of the Thai central bank in this period. For example, Patrawimolpon et al. (2001) assume that the central bank either targets money supply or the interest rate in their pre-crisis model. Such assumptions are potentially invalid insofar because they are at odds with the typical Mundell–Fleming type argument of the inconsistency of the trinity independent monetary policy, fixed exchange rate regime and open capital account. Analogous to the above argumentation, we do not obtain an interest rate policy function. Our two cointegrating vectors in the pre-crisis model correspond to an inflation adjustment equation and IS-type equation. Similarly to Bhanthumnavin (2002), we cannot obtain a Phillips curve relationship. Inflation seems foremost a monetary phenomenon and is positively related to the inverse velocity of money. Our results for the pre-crisis monetary transmission mechanism are consistent with the identification of the common stochastic trends. In details, money shocks have permanent effects on inflation but only transitory effects on output. The transitory response of output supports the reasoning of Corbett and Vines (1999) and Phongpaichit and Baker (2002) that the monetary tightening of the Thai central bank in 1993–1994 to cool down the economy was ineffective because financial intermediaries were able to borrow abroad at cheaper interest rates. Also, there is no channel from monetary shocks to the exchange rate which is economically intuitive. Shocks to the interest rates significantly influence output which we attributed to a possible financial accelerator argument (Bernanke et al., 1996) or the fact that domestic interest rates move with foreign interest rates which were not included in the model. In the post-crisis model, we do find a money demand equation in which money supply is determined by the output gap and the exchange rate which could proxy as the opportunity cost of holding money. One potential explanation could be that the exchange rate is proxying current expectation of future macroeconomic imbalances or uncertainties so that the central bank uses monetary tools to correct for that. Of course, the type of money demand is subject to the included system variables. Further, we obtain an interest rate policy and inflation adjustment equation. In general, we find it very difficult to explain the short-run behaviour of the exchange rate by fundamental factors which complies with the arguments of Meese and Rogoff (1983). What is the bottom line of the paper? We still do not know the full monetary transmission mechanism of money shocks to the economy, although we could disentangle various components. The question is ‘What is the black box?’ to borrow the terminology of Bernanke and Gertler (1995). To answer the questions, we could extend the model with further macroeconomic variables such as investment, stock market and bank lending data in order to obtain a full picture how monetary policy shocks are transmitted to the economy through the asset price and credit channels. For instance, Disyatat and Vongsinsirikul (2003) add these additional variables individually to a small (three variable) VAR model. Their approach is problematic because they do not deal with nonstationarity so any findings will be potentially biased if equations are estimated in levels. We anticipate that it will be a difficult task to incorporate these variables into the cointegrated VAR framework. The structural break of the Asian crisis complicates such an extended study. Also, we might lose tractability of the model if the dimension rapidly increases, an argument which Juselius (2007) elaborates on. This is surely a limitation of our VAR approach because the economy is an interrelated dynamic system in which many factors influence the key macroeconomic indicators. Our initial aim has been modest because data issues and the Asian crisis made it very difficult to decompose the short-run dynamics of the system variables. From a policy viewpoint, we see scope for further improvements of the cointegrated VAR modelling approach in order to draw sensible statistical inferences. For instance, the literature on forecasting in a nonstationary world (Clements & Hendry, 1999) becomes relevant. But a forecasting exercise would be rather limited because forecasts of the pre-crisis model will suffer from the structural break in 1997. Another interesting forecasting exercise would be to estimate a full sample cointegrated VAR model and analyse the factors that significantly contribute to the forecast failure of the Asian crisis. We potentially could adopt the methodology of Clements and Hendry (1999) who provide a taxonomy of forecast errors in varying macroeconomic settings. According to their framework, deterministic shifts are usually the primary determinant of forecast failure. But we know from the estimation of the broken linear trend model by Johansen et al. (2000) that a deterministic trend is not able to potentially overcome the structural break during the Asian crisis. Therefore, possible different explanations for the forecast failure would provide a valuable addition to our reasoning in Section 5 to account for the differences in the pre- and post-crisis period. An issue which is often neglected in related studies is the quality of the data. We argued before that Thailand faces some data problems in terms of the availability of monthly frequencies for GDP. The manufacturing production index (MPI) that we adopt can only imperfectly qualify for measuring the movements of GDP. Finally, our results could be potentially adopted to different country experiences in South East Asia that had fixed exchange rate regimes and were affected by the Asian crisis. Supposedly, many South East Asian countries liberalized their financial markets in the early 1990s, and therefore there might be cross-country similarities between the monetary transmission mechanisms and the degree of effectiveness of monetary policy. For instance, Fung (2002) attempted to explain these in a structural VAR setting. Our cointegrated VAR methodology could provide an alternative to answer such questions.