Hi, I am writing my SOP for applying Economics PhD. I have finish the part of my previous works and my research interest, and found that it is already about 800 words now. I plan to keep this sop in about 1000 words, while I still have my TA (of graduate level core curriculum), RA (what I am now researching), extracurricular activity (gave a text mining lecture in Taiwan R User Group) experiences, and of course linkage my research interest to the faculty of each school need to include.
Can you give me some advice to remove the redundant information as well as where can I put the extra experience listed above? Any comment is very welcome and thank you in advance.
Note: Prof. AAA, Prof. BBB, Prof. CCC, Prof. DDD would write me my recommendation letters.
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It was in National Taiwan University (NTU) Public Policy Forum that I firstly touched the issue of asymmetric intervention on foreign exchange market of central bank. In pursuing the existence of asymmetric intervention on foreign exchange market, it eventually bends my path to econometric research. When I was a junior in NTU, the best university in Taiwan, I took a course of Monetary and Banking and started to gain interest in monetary policy. Since I have taken some mathematics courses like Advanced calculus (Introductory mathematical analysis) and linear algebra, I prefer more rigorous approaches than just having intuition: I began to attend different kinds of seminars to learn how economists analyze economic issues. Once, NTU Public Policy Forum was held to discuss the influence of monetary policy on economy by time series analysis, and I was fascinated.
In the forum, several papers employ time series analysis to discuss how central bank behaves after New Taiwanese Dollar fluctuates, and I suddenly found how data analysis matters in researching. So later in my first year of Master's degree, I took a course of time-series-approach international finance, which taught by Prof. AAA, and write a term paper to evaluate how foreign direct investment (FDI) influence gross domestic product (GDP) through the financial market channel. I use several benchmarks like the credit of private sector and stock market capitalization to evaluate the efficiency of financial market and employed basic panel data model to estimate the model. I learned that even though the theory about this issue is still in discuss, data do confirm us the existence of some effect. This thought corroborates my resolution of pursuing a Ph.D.
In order to analyze data properly, I took several mathematics courses such as real analysis (measure theory), advanced statistical inference, (measure-theoretic) probability theory as well as stochastic calculus during my Master's degree. I also took doctoral level econometric theory taught by Prof. BBB to learn rigorous theory of econometrics. Because my interest in time series analysis meets Prof. BBB's research field of interest, my M.S. thesis is advised by Prof. BBB. My M.S. thesis intend to use large dimension of macroeconomic variables to forecast DGP growth rate by supervised factor (SF) model. SF model in my thesis includes partial least square, principal covariate regression and combining forecast principal component analysis. These methods, comparing to dynamic factor (DF) model, premixed the variable of interest with the macroeconomic variables before factorization process, thus the factors have better predictability than DF model. In order to enhance forecast performance, I also consider to preselect the macroeconomic variables by variable selection methods such as least absolute shrinkage and selection operator (LASSO), least angle regression (LARS) as well as orthogonal greedy algorithm (OGA). They invoked my further study later despite variable selection is not considered in my thesis in the end because it will be clearer, if I employed the same dataset as other previous works of DF model, to focus on the improvement of predictability of SF models on DF model. I think research about SF model is promising because several topics such as variable preselection and mixed data frequency (which I also considered during the empirical part) can be applied to improve the forecast. My M.S. thesis is honored with Outstanding Master's Thesis Award given by Taiwan Economic Society and is presented in 2014 Taiwan Econometrics Society Annual Conference and NTU econometrics seminar.
My experience of writing M.S. thesis led me to explore further topics such as whether the variable selection method can be applied to moment selection problem under generalized method of moment (GMM). I did a independent study about this topic advised by Prof. CCC. Meanwhile, I am still learning other methods such as Bayesian estimation in Prof. DDDFpro's Applied Econometric Method class and wrote a term paper trying to look at the asymmetric intervention of Taiwan's foreign exchange market by Bayesian Markov chain Monte Carlo method to estimate the parameter of a small open economy dynamic stochastic general equilibrium (DSGE) model. In these term papers, I encountered some problems that could be research during my Ph.D. For instance, the variable selection methods such as LARS and OGA rank the variables by the variable of interest in regression model, while under GMM scheme, there is no variable of interest; if any, it is zero. On the other hand, the estimation of DSGE model by Bayesian method would have firstly transform the model into state-transition equations and then use Kalman filter to get the prediction. The advantage of Bayesian estimation is its data driven property; however, Kalman filter is based on normal distribution, which makes the estimation not that data-driving.
(The next paragraph should link my interest to faculty member's research.)
Can you give me some advice to remove the redundant information as well as where can I put the extra experience listed above? Any comment is very welcome and thank you in advance.
Note: Prof. AAA, Prof. BBB, Prof. CCC, Prof. DDD would write me my recommendation letters.
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It was in National Taiwan University (NTU) Public Policy Forum that I firstly touched the issue of asymmetric intervention on foreign exchange market of central bank. In pursuing the existence of asymmetric intervention on foreign exchange market, it eventually bends my path to econometric research. When I was a junior in NTU, the best university in Taiwan, I took a course of Monetary and Banking and started to gain interest in monetary policy. Since I have taken some mathematics courses like Advanced calculus (Introductory mathematical analysis) and linear algebra, I prefer more rigorous approaches than just having intuition: I began to attend different kinds of seminars to learn how economists analyze economic issues. Once, NTU Public Policy Forum was held to discuss the influence of monetary policy on economy by time series analysis, and I was fascinated.
In the forum, several papers employ time series analysis to discuss how central bank behaves after New Taiwanese Dollar fluctuates, and I suddenly found how data analysis matters in researching. So later in my first year of Master's degree, I took a course of time-series-approach international finance, which taught by Prof. AAA, and write a term paper to evaluate how foreign direct investment (FDI) influence gross domestic product (GDP) through the financial market channel. I use several benchmarks like the credit of private sector and stock market capitalization to evaluate the efficiency of financial market and employed basic panel data model to estimate the model. I learned that even though the theory about this issue is still in discuss, data do confirm us the existence of some effect. This thought corroborates my resolution of pursuing a Ph.D.
In order to analyze data properly, I took several mathematics courses such as real analysis (measure theory), advanced statistical inference, (measure-theoretic) probability theory as well as stochastic calculus during my Master's degree. I also took doctoral level econometric theory taught by Prof. BBB to learn rigorous theory of econometrics. Because my interest in time series analysis meets Prof. BBB's research field of interest, my M.S. thesis is advised by Prof. BBB. My M.S. thesis intend to use large dimension of macroeconomic variables to forecast DGP growth rate by supervised factor (SF) model. SF model in my thesis includes partial least square, principal covariate regression and combining forecast principal component analysis. These methods, comparing to dynamic factor (DF) model, premixed the variable of interest with the macroeconomic variables before factorization process, thus the factors have better predictability than DF model. In order to enhance forecast performance, I also consider to preselect the macroeconomic variables by variable selection methods such as least absolute shrinkage and selection operator (LASSO), least angle regression (LARS) as well as orthogonal greedy algorithm (OGA). They invoked my further study later despite variable selection is not considered in my thesis in the end because it will be clearer, if I employed the same dataset as other previous works of DF model, to focus on the improvement of predictability of SF models on DF model. I think research about SF model is promising because several topics such as variable preselection and mixed data frequency (which I also considered during the empirical part) can be applied to improve the forecast. My M.S. thesis is honored with Outstanding Master's Thesis Award given by Taiwan Economic Society and is presented in 2014 Taiwan Econometrics Society Annual Conference and NTU econometrics seminar.
My experience of writing M.S. thesis led me to explore further topics such as whether the variable selection method can be applied to moment selection problem under generalized method of moment (GMM). I did a independent study about this topic advised by Prof. CCC. Meanwhile, I am still learning other methods such as Bayesian estimation in Prof. DDDFpro's Applied Econometric Method class and wrote a term paper trying to look at the asymmetric intervention of Taiwan's foreign exchange market by Bayesian Markov chain Monte Carlo method to estimate the parameter of a small open economy dynamic stochastic general equilibrium (DSGE) model. In these term papers, I encountered some problems that could be research during my Ph.D. For instance, the variable selection methods such as LARS and OGA rank the variables by the variable of interest in regression model, while under GMM scheme, there is no variable of interest; if any, it is zero. On the other hand, the estimation of DSGE model by Bayesian method would have firstly transform the model into state-transition equations and then use Kalman filter to get the prediction. The advantage of Bayesian estimation is its data driven property; however, Kalman filter is based on normal distribution, which makes the estimation not that data-driving.
(The next paragraph should link my interest to faculty member's research.)