Forecasting Economic Variables Using Disaggregated Data for Consumer Expectations: A Comparison of Forecast Combination Methods (in Korean)

Different consumers may form different expectations about future economic conditions. Therefore we need to combine individual forecasts rather than relying a single forecast from a particular consumer group. In this paper we investigate the relative performances of several combination methods in predicting economic variables such as output, consumption spending, employment, unemployment rate, price level, and interest rate, using disagregated data for the consumer expectations. In addition to the principal component method, we consider forecast combination methods based on simple averaging, discount MSFE, Bayesian shrinkage, information creteria, and clustering. We find that consumers' expectation indexes are useful to predict most of the economic variables we consider. We also find that the combination methods based on clustering outperform other forecasting methods, especially when the weight of each cluster is determined using ordinary least squares estimation or Bayesian shrinkage techniques.