Essays on macroeconomics and forecasting

This dissertation consists of three essays. Chapter II uses the method of structuralfactor analysis to study the effects of monetary policy on key macroeconomic variablesin a data rich environment. I propose two structural factor models. One is the structuralfactor augmented vector autoregressive (SFAVAR) model and the other is the structuralfactor vector autoregressive (SFVAR) model. Compared to the traditional vectorautogression (VAR) model, both models incorporate far more information fromhundreds of data series, series that can be and are monitored by the Central Bank.Moreover, the factors used are structurally meaningful, a feature that adds to theunderstanding of the “black box” of the monetary transmission mechanism. Both modelsgenerate qualitatively reasonable impulse response functions. Using the SFVAR model,both the “price puzzle” and the “liquidity puzzle” are eliminated.Chapter III employs the method of structural factor analysis to conduct aforecasting exercise in a data rich environment. I simulate out-of-sample real timeforecasting using a structural dynamic factor forecasting model and its variations. I useseveral structural factors to summarize the information from a large set of candidateexplanatory variables. Compared to Stock and Watson (2002)’s models, the models proposed in this chapter can further allow me to select the factors structurally for eachvariable to be forecasted. I find advantages to using the structural dynamic factorforecasting models compared to alternatives that include univariate autoregression (AR)model, the VAR model and Stock and Watson’s (2002) models, especially whenforecasting real variables.In chapter IV, we measure U.S. technology shocks by implementing a dualapproach, which is based on more reliable price data instead of aggregate quantity data.By doing so, we find the relative volatility of technology shocks and the correlationbetween output fluctuation and technology shocks to be much smaller than thoserevealed in most real-business-cycle (RBC) studies. Our results support the findings ofBurnside, Eichenbaum and Rebelo (1996), who showed that the correlation betweentechnology shocks and output is exaggerated in the RBC literature. This suggests thatone should examine other sources of fluctuations for a better understanding of thebusiness cycle phenomena.