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Essays on Forecasting Macroeconomic Variables Using Mixed Frequency Data

出版	Rutgers University, 2016
URL	http://books.google.com.hk/books?id=QcLOtAEACAAJ&hl=&source=gbs_api

註釋This dissertation investigate the forecasting performance of mixed frequency factor models with mixed frequency dataset. In the first chapter, I consider the mixed fre- quency factor approach used in ADS (2009) to construct their co-incident activity index, and ask the question of whether a class of mixed frequency indexes is useful for predicting the future values of quarterly U.S. real GDP growth and monthly industrial production, unemployment and inflation. My forecasting assessment of the mixed frequency factor model is performed in conjunction with standard prediction models such as autoregression, multivariate distributed lag models, and diffu- sion index models of the variety examined by Stock and Watson (2002a). The main findings of the study are as follows. First, prediction models using only mixed frequency indexes show their best performance at short-term GDP forecasting horizons, and are particularly good during recessions. Second, prediction models using both mixed frequency indexes and diffusion indexes forecast monthly variables more accurately than models using single frequency type indexes. Third, model combi- nations perform relatively poorly in real GDP forecasting contexts, although they perform better when applied to the prediction of monthly variables. Fourth, survey information can be conveniently exploited with higher frequency variables such as daily and weekly variables, and mixed frequency indexes using such survey information are sometimes useful in forecasting lower frequency variables. In the second chapter, I evaluate the predictive performance of hybrid models for forecasting four economic variables. The hybrid approach takes into account the notion that simple autoregression and sophisticated factor models' predictive abilities may change according to the state of the econ- omy. I find that hybrid prediction models produce better forecasts than standard models and than combination models, in most cases, using the same menu of models discussed above. For example, in one-quarter ahead GDP forecasts, the best hybrid model reduces the mean squared forecast error of the best model combinations and the linear models by 14 and 11 percent, on average, respectively. More specifically, the mean squared forecast error of autoregression is reduced by approximately 35 percent. In 12-month ahead predictions of inflation, the best hybrid model improves the best model combinations and the linear models by 11 percent and 16 percent, on average, respectively. This number again increases, in this case to 36 percent, when comparing only with autoregression. One reason for these findings is that hybrid prediction models more effectively utilize survey information.