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  • Search: subject:"finite sample moments"
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Year of publication
Subject
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finite sample moments 4 bias correction 3 Nagar approximation 2 OLS bias 2 Bias correction 1 Efficiency gains 1 Finite sample moments 1 Higher-order asymptotic expansions 1 Lagged dependent variables 1 Optimal portfolio weights 1 Size improvements 1 and the estimated bias is small and statistically insignificant 1 but has a higher MSE. A hypothesis test is given for the presence of this bias. The techniques are applied to survey data on food expenditure 1 but not always. In simulations 1 efficiency gains 1 higher-order asymptotic expansions 1 jackknife and pairs bootstrap methods are equal to Op(n-3/2). Sometimes they are effective at lowering bias and MSE 1 lagged dependent variables 1 pairs bootstrap 1 pairs bootstrap The O(n-1) bias and O(n-2) MSE of OLS are derived for iid samples. An approach is suggested for handling nonexistent finite sample moments. Bias corrections based on plug-in 1 shrinkage 1 size improvement 1 the bootstrap correction removes more bias than the others 1 weighting 1
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Online availability
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Free 3 Undetermined 2
Type of publication
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Book / Working Paper 3 Article 2
Language
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Undetermined 3 English 2
Author
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Magee, Lonnie 2 KIVIET, Jan F. 1 Kiviet, Jan F. 1 OKHRIN, YAREMA 1 PHILLIPS, Garry D.A. 1 Phillips, Garry D.A. 1 SCHMID, WOLFGANG 1
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Institution
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Department of Economics, McMaster University 2 Division of Economics, Nanyang Technological University 1
Published in...
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Computational Statistics & Data Analysis 1 Economic Growth Centre Working Paper Series 1 International Journal of Theoretical and Applied Finance (IJTAF) 1 Quantitative Studies in Economics and Population Research Reports 1 Social and Economic Dimensions of an Aging Population Research Papers 1
Source
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RePEc 5
Showing 1 - 5 of 5
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Ordinary Least Squares Bias and Bias Corrections for <em>iid</em> Samples
Magee, Lonnie - Department of Economics, McMaster University - 2007
nonexistent finite sample moments. Bias corrections based on plug-in, weighting, jackknife and pairs bootstrap methods are equal … insignificant. Key words: OLS bias; finite sample moments; Nagar approximation; bias correction; pairs bootstrap JEL …
Persistent link: https://www.econbiz.de/10005181112
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Improved Variance Estimation of Maximum Likelihood Estimators in Stable First-Order Dynamic Regression Models
KIVIET, Jan F.; PHILLIPS, Garry D.A. - Division of Economics, Nanyang Technological University - 2012
In dynamic regression models conditional maximum likelihood (least-squares) coefficient and variance estimators are biased. From expansions of the coefficient variance and its estimator we obtain an approximation to the bias in variance es- timation and a bias corrected variance estimator, for...
Persistent link: https://www.econbiz.de/10010927739
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Improved variance estimation of maximum likelihood estimators in stable first-order dynamic regression models
Kiviet, Jan F.; Phillips, Garry D.A. - In: Computational Statistics & Data Analysis 76 (2014) C, pp. 424-448
In dynamic regression models conditional maximum likelihood (least-squares) coefficient and variance estimators are biased. Using expansion techniques an approximation is obtained to the bias in variance estimation yielding a bias corrected variance estimator. This is achieved for both the...
Persistent link: https://www.econbiz.de/10010871320
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Ordinary Least Squares Bias and Bias Corrections for iid Samples
Magee, Lonnie - Department of Economics, McMaster University - 2007
finite sample moments. Bias corrections based on plug-in, weighting, jackknife and pairs bootstrap methods are equal to Op …
Persistent link: https://www.econbiz.de/10005181076
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ESTIMATION OF OPTIMAL PORTFOLIO WEIGHTS
OKHRIN, YAREMA; SCHMID, WOLFGANG - In: International Journal of Theoretical and Applied … 11 (2008) 03, pp. 249-276
The paper discusses finite sample properties of optimal portfolio weights, estimated expected portfolio return, and portfolio variance. The first estimator assumes the asset returns to be independent, while the second takes them to be predictable using a linear regression model. The third and...
Persistent link: https://www.econbiz.de/10005060222
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