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dc.contributor.advisorKim, Hyeongwooen_US
dc.contributor.authorShi, Wenen_US
dc.date.accessioned2016-05-05T19:55:12Z
dc.date.available2016-05-05T19:55:12Z
dc.date.issued2016-05-05
dc.identifier.urihttp://hdl.handle.net/10415/5165
dc.description.abstract“Assessing and Forecasting Financial Vulnerability in the U.S.: A Factor Model Approach,” This paper presents a factor-based forecasting model for the financial market vulnerability in the U.S. We estimate latent common factors via the method of the principal components from 170 monthly frequency macroeconomic data to out-of-sample forecast the Cleveland Financial Stress Index. Our factor models outperform both the random walk and the autoregressive benchmark models in out-of-sample predictability for short-term forecast horizons, which is a desirable feature since financial crises often come to a surprise realization. Interestingly, the first common factor, which plays a key role in predicting the financial vulnerability index, seems to be more closely related with real activity variables rather than nominal variables. The recursive and the rolling window approaches with a 50% split point perform similarly well. “The Determinants of the Benchmark Interest Rates in China: A Discrete Choice Model Approach,” This paper empirically investigates the determinants of key benchmark interest rates in China using an array of constrained ordered probit models for quarterly frequency data from 1987 to 2013. Specifically, we estimate the behavioral equation of the People's Bank of China that models their decision-making process for revisions of the benchmark deposit rate and the lending rate. Our findings imply that the PBC's policy decisions are better understood as responses to changes in inflation and money growth, while output gaps and the exchange rate play negligible roles. We also implement in-sample fit analyses and out-of-sample forecast exercises. These tests show robust and reasonably good performances of our models in understanding dynamics of these benchmark interest rates. “Estimating Interest Rate Setting Behavior in Korea: A Constrained Ordered Choices Model Approach,” We study the Bank of Korea’s interest rate setting behavior using an array of constrained ordered choices models, where the Monetary Policy Committee (MPC) revises the target policy interest rate only when the current market interest rate deviates from the optimal rate by more than certain threshold values. Our models explain changes in the monetary policy stance fairly well for the monthly frequency Korean data since January 2000. We find important roles for the output gap and the won depreciation rate against the US dollar in understanding the Bank of Korea’s rate decision-making processes. We also implement out-of-sample forecast exercises with September 2008 (Lehman Brothers Bankruptcy) for a split point. We demonstrate that out-of-sample predictability improves greatly using standard error adjusted inaction bands for the rate cut and the rate hike decisions.en_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectEconomicsen_US
dc.titleEssays on Macroeconomic and Financial Stabilityen_US
dc.typeDissertationen_US
dc.embargo.lengthMONTHS_WITHHELD:13en_US
dc.embargo.statusEMBARGOEDen_US
dc.embargo.enddate2017-05-08en_US


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