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 in‡ation adjustment equation. In general, we ...nd it very di¢ cult to explain the short run behaviour of the exchange rate by fundamental factors which complies with the arguments of Meese and Rogo¤ (1983).

What is the bottomline 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 ...ndings will be potentially biased if equations are estimated in levels.

We anticipate that it will be a di¢ cult 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 in‡uence the key macroeconomic indicators. Our initial aim has been modest because data issues and the Asian Crisis made it very di¢ cult 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 and Hendry, 1999) becomes relevant. But a forecasting exercise would be rather limited because forecasts of the pre-crisis model will su¤er from the structural break in 1997.

Another interesting forecasting exercise would be to estimate a full sample cointegrated VAR model and analyze the factors that signi...cantly 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 di¤erent explanations for the forecast failure would provide a valuable addition to our reasoning in section 5 to account for the di¤erences 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.

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