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研究生:葉錦徽
研究生(外文):Yeh, Jin-Huei
論文名稱:SampleInformationExtractionviaMonte-CarloMethodsinEconometrics
論文名稱(外文):Sample Information Extraction via Monte-Carlo Methods in Econometrics
指導教授:陳美源陳美源引用關係
指導教授(外文):Chen Mei-Yuan
學位類別:碩士
校院名稱:國立中正大學
系所名稱:國際經濟研究所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:1998
畢業學年度:86
語文別:中文
論文頁數:46
外文關鍵詞:Gibbs SamplingBootstrapMonte-Carlo MethodNearly Integrated ProcessSpurious RegressionSample Information ExtractionGibbs SamplingBootstrapMonte-Carlo MethosNearly Integrated ProcessSpurious RegressionSample Information Extraction
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The Growing Capacity of computer computing enables us to get
more insights of a sample and helps a lot in statistical
inference. In this paper, we present two computer-based
sampling approaches via Monte Carlo methods developed for
inference in econometrics. We discuss the information-
extracting system of the two algorithms, and then compare the
performance of the two algorithms with the classical inference
through Bayesian approach. Applying the Gibbs sampling
algorithm in the study of nearly-integrated process, we found
itout perform the D-F test in testing a nearly-integrated
stationary sequence.When comes to the study of the spurious
relationships between two mutually independent AR(1) sequences,
all the three tests, including conventional ttest, the bootstrap
t test and the Gibbs sampling algorithm perform equallyand
neither test clearly dominates the other.

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