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Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data

dc.contributor.author Tauchen, George E
dc.date.accessioned 2010-03-09T15:28:07Z
dc.date.available 2010-03-09T15:28:07Z
dc.date.issued 1986
dc.identifier.uri https://hdl.handle.net/10161/1886
dc.description.abstract The article examines the properties of generalized method of moments GMM estimators of utility function parameters. The research strategy is to apply the GMM procedure to generated data on asset returns from stochastic exchange economies; discrete methods and Markov chain models are used to approximate the solutions to the integral equations for the asset prices. The findings are as follows: (a) There is variance/bias trade-off regarding the number of lags used to form instruments; with short lags, the estimates of utility function parameters are nearly asymptotically optimal, but with longer lags the estimates concentrate around biased values and confidence intervals become misleading. (b) The test of the overidentifying restrictions performs well in small samples; if anything, the test is biased toward acceptance of the null hypothesis.
dc.format.extent 574119 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher Journal of Business & Economic Statistics
dc.subject intergral equations
dc.subject markov chain
dc.subject monte carlo simulation
dc.title Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data
dc.type Journal article
duke.contributor.id Tauchen, George E|0114412


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