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.

The diploma thesis deals with the basic Real Business Cycle (RBC) model. RBC theory provides pure supply-side explanation of economic fluctuations. Generaly acknowledged contribution of RBC theory is the fact that the model is developed strictly on microeconomic basis. The thesis consists of two basic parts, theoretical and practical. First, historical background of RBC theory is mentioned. Then the basic RBC model is step-by-step derived and all equations are provided with explanations. In the last theoretical part section RBC theory critisism is discussed. In the practical part the derived basic model is applied to the Czech economy. First certain properties of the Czech business cycles are examined. Then, the basic model is calibrated, simulated and the results are commented.

Diese Dissertation umfasst sowohl einen theoretisches als auch einen empirischen Beitrag zur Analyse der Messung der gesamten Faktorproduktivität (TFP). Das erste Kapitel inspiziert die bestehende Literatur über die häufigsten Techniken der TFP Messung und gibt einen Überblick über deren Limitierung. Das zweite Kapitel betrachtet Daten, die durch ein Real Business Cycle Modell generiert wurden und untersucht das quantifizierbare Ausmaß von Messfehlern des Solow Residuums als ein Maß für TFP Wachstum, wenn der Kapitalstock fehlerhaft gemessen wird und wenn Kapazitätsauslastung und Abschreibungen endogen sind. Das dritte Kapitel schlägt eine neue Methodologie in einem bayesianischen Zusammenhang vor, die auf Zustands- Raum-Modellen basiert. Das vierte Kapitel führt einen neuen Ansatz zur Bestimmung möglicher Spill-over Effekte auf Grund neuer Technologien auf die Produktivität ein und kombiniert eine kontrafaktische Zerlegung, die von den Hauptannahmen des Malquist Indexes abgeleitet wird mit ökonometrischen Methoden, die auf Machado and Mata (2005) zurückgehen. / This dissertation consists of theoretical and empirical contributions to the study on Total Factor Productivity (TFP) measurement. The first chapter surveys the literature on the most used techniques in measuring TFP and surveys the limits of these frameworks. The second chapter considers data generated from a Real Business Cycle model and studies the quantitative extent of measurement error for the Solow residual as a measure of TFP growth when the capital stock is measured with error and when capacity utilization and depreciation are endogenous. Furthermore, it proposes two alternative measurements of TFP growth which do not require capital stocks. The third chapter proposes a new methodology based on State-space models in a Bayesian framework. Applying the Kalman Filter to artificial data, it proposes a computation of the initial condition for productivity growth based on the properties of the Malmquist index. The fourth chapter introduces a new approach for identifying possible spillovers emanating from new technologies on productivity combining a counterfactual decomposition derived from the main properties of the Malmquist index and the econometric technique introduced by Machado and Mata (2005).