The Advantages of Dynamic Factor Models as Techniques for Forecasting: Evidence from Taiwanese Macroeconomic Data

This study applies an approximate dynamic factor model to forecast three macroeconomic variables of Taiwan – inflation based on consumer price index, unemployment rate, and industrial production growth rate. Our data contain 95 macroeconomic variables of Taiwan and 89 international time series during 1981Q1-2006Q4. We perform out-of-sample forecasting from a rolling-window estimation scheme and compare our models with a univariate autoregressive model and a vector autoregressive model. We find that our dynamic factor model has superior performance in predicting inflation for all forecasting horizons. However, limited superior performance is found in the application to industrial production growth rate and unemployment rate. Moreover, we do not find that including international variables help to improve the performance of a dynamic factor model in our application.