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 âÂÂblack boxâ of the monetary transmission mechanism. Both models generate qualitatively reasonable impulse response functions. Using the SFVAR model, both the âÂÂprice puzzleâ and the âÂÂliquidity puzzleâ 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)âÂÂs 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 WatsonâÂÂs (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

Research on Marine Pollution Problems and Solutions in China from the Perspective of Marine Tourism

Based on the perspective of marine tourism, this paper integrates various types of marine pollution, and puts forward high-quality development solutions and future extension direction of marine tourism. Through the research, it is found that the main culprits of marine pollution mainly include the following seven points: human activities produce garbage; white pollution; ship pollution; exploration of marine oil and gas resources and mineral pollution; land reclamation; pollution in mariculture industry and new estrogen pollution. The causes of marine pollution and countermeasures are discussed

Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm

KNOWLEDGE MANAGEMENT CAPABILITY AND FIRM PERFORMANCE: THE MEDIATING ROLE OF ORGANIZATIONAL AGILITY

Implementing knowledge management capability (KMC) effectively is becoming an important strategic issue for organizational success. However, our understanding about the underlying mechanism of KMC on firm performance is still limited. Based on the dynamic capabilities perspective, this study tries to explore how KMC (i.e., exploration KMC and exploitation KMC) affects firm performance through the mediating role of operational adjustment agility and market capitalizing agility. Survey data from 211 firms indicate that both operational adjustment agility and market capitalizing agility can fully mediate the influence of KMC on firm performance. In addition, the relationship intensions of these two KMC on organizational agility are distinguishing. We conclude with implications and limitations for future research

Nutrition, Histone Epigenetic Marks, and Disease

The dietary intake of essential nutrients and bioactive food compounds is a process that occurs on a daily basis for the entire life span. Therefore, your diet has a great potential to cause changes in the epigenome. Known histone modifications include acetylation, methylation, biotinylation, poly(ADP-ribosylation), ubiquitination, and sumoylation. Some of these modifications depend directly on dietary nutrients. For other modifications, bioactive dietary compounds may alter the activities of enzymes that establish or remove histone marks, thereby altering the epigenome. This chapter provides an overview of diet-dependent epigenomic marks in histones and their links with human health

On zeros of characters of finite groups

summary:For a finite group $G$ and a non-linear irreducible complex character $\chi$ of $G$ write $\upsilon (\chi )=\{g\in G\mid \chi (g)=0\}$. In this paper, we study the finite non-solvable groups $G$ such that $\upsilon (\chi )$ consists of at most two conjugacy classes for all but one of the non-linear irreducible characters $\chi$ of $G$. In particular, we characterize a class of finite solvable groups which are closely related to the above-mentioned question and are called solvable $\varphi$-groups. As a corollary, we answer Research Problem $2$ in [Y. Berkovich and L. Kazarin: Finite groups in which the zeros of every non-linear irreducible character are conjugate modulo its kernel. Houston J. Math.\ 24 (1998), 619--630.] posed by Y. Berkovich and L. Kazarin

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 âÂÂblack boxâ of the monetary transmission mechanism. Both models generate qualitatively reasonable impulse response functions. Using the SFVAR model, both the âÂÂprice puzzleâ and the âÂÂliquidity puzzleâ 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)âÂÂs 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 WatsonâÂÂs (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