Essays on macroeconomics and forecasting
This dissertation consists of three essays. Chapter II uses the method of structural
factor analysis to study the effects of monetary policy on key macroeconomic variables
in a data rich environment. I propose two structural factor models. One is the structural
factor augmented vector autoregressive (SFAVAR) model and the other is the structural
factor vector autoregressive (SFVAR) model. Compared to the traditional vector
autogression (VAR) model, both models incorporate far more information from
hundreds 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 the
understanding of the Ã¢Â?Â?black boxÃ¢Â?Â? of the monetary transmission mechanism. Both models
generate qualitatively reasonable impulse response functions. Using the SFVAR model,
both the Ã¢Â?Â?price puzzleÃ¢Â?Â? and the Ã¢Â?Â?liquidity puzzleÃ¢Â?Â? are eliminated.
Chapter III employs the method of structural factor analysis to conduct a
forecasting exercise in a data rich environment. I simulate out-of-sample real time
forecasting using a structural dynamic factor forecasting model and its variations. I use
several structural factors to summarize the information from a large set of candidate
explanatory variables. Compared to Stock and Watson (2002)Ã¢Â?Â?s models, the models proposed in this chapter can further allow me to select the factors structurally for each
variable to be forecasted. I find advantages to using the structural dynamic factor
forecasting models compared to alternatives that include univariate autoregression (AR)
model, the VAR model and Stock and WatsonÃ¢Â?Â?s (2002) models, especially when
forecasting real variables.
In chapter IV, we measure U.S. technology shocks by implementing a dual
approach, 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 correlation
between output fluctuation and technology shocks to be much smaller than those
revealed in most real-business-cycle (RBC) studies. Our results support the findings of
Burnside, Eichenbaum and Rebelo (1996), who showed that the correlation between
technology shocks and output is exaggerated in the RBC literature. This suggests that
one should examine other sources of fluctuations for a better understanding of the
business cycle phenomena.
Advisor:Jansen, Dennis; Bessler, David; Hernandez-Verme, Paula; Li, Qi
School:Texas A&M University
School Location:USA - Texas
Source Type:Master's Thesis
Keywords:monetary policy factor analysis forecasting solow residual
Date of Publication:08/01/2005