Liu, Dandan (2005-08). Essays on macroeconomics and forecasting. Doctoral Dissertation. Thesis uri icon

abstract

  • 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 ??????black box?????? of the monetary transmission mechanism. Both models generate 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 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)??????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 Watson??????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.
  • 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 ??????black box?????? of the monetary transmission mechanism. Both models
    generate 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 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)??????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 Watson??????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.

publication date

  • August 2005