Modelling and Simulation Society of Australia and New Zealand
Faculty of Business and Law
School of Business/Finance, Economics, Markets and Accounting Research Centre
In this paper we intend to forecast the economic growth of Singapore by employing mixed frequency data. This study is motivated by the following observations: macroeconomic variables are the important indicators of the economic performance, but they are normally available at low frequencies, e.g. quarterly for GDP and monthly for inflation. In contrast, the financial variables such as stock returns are available at high frequency, and often the asset prices are forward-looking and believed to contain useful information about future economic developments (Stock and Watson 2003). It is therefore an interesting question to raise whether or not one can use the high-frequency financial variables to better estimate and forecast the macroeconomic variables. Using mixed-frequency data in forecasting is clearly against the conventional forecasting models which generally require data with the same frequency. Time-aggregating, such as averaging, of the high frequency data is usually practiced to match the sampling rate of lower frequency data. But time-aggregation always leads to loss of individual timing information that might be important for forecasting. Hence, finding a suitable method to handle the high frequency data is a crucial task for every forecaster dealing with mixed frequency data. We employ the Mixed Data Sampling (MIDAS) regression model introduced by Ghysels, et al. (2004). MIDAS regressions are essentially tightly parameterized, highly parsimonious regressions that deal with mixed frequency data. It is designed to find a balance between retaining the individual timing information of the high frequency data and reducing the number of parameters that need to be estimated. It is believed to have better estimating and forecasting ability than many other conventional models. A number of studies adopted MIDAS models to forecast quarterly series using monthly or daily financial data, mostly from the US (Anthony 2007; Clements and Galvão 2009). Singapore is a small open economy, and vulnerable to the global economic conditions. Although its stock market is not comparable with that of the US in term of capitalization, the Singapore stock market performance is believed to be highly correlated with its real macroeconomic variable and contains important information for economic forecasting. In this paper, we forecast one-quarter-ahead Singapore GDP growth rate using Singapore stock market return sampled at various high frequencies. We investigate the forecasting performance from three models: a Mixed Data Sampling (MIDAS) regression model, a direct regression model on high frequency data and a time-averaging regression model. Our results show that MIDAS regression using high frequency stock return data produces better forecast of GDP growth rate than the other two models. Best forecasting performance is achieved using weekly stock return. The forecasting result is further improved by performing intra-period forecasting.