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Jonathan N Katz - One of the best experts on this subject based on the ideXlab platform.
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random coefficient models for time series cross section data monte carlo experiments
Political Analysis, 2007Co-Authors: Nathaniel Beck, Jonathan N KatzAbstract:This article considers random coefficient models (RCMs) for time-series‐cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully Pooled Estimator, the unit by unit (unPooled) Estimator, and the (maximum likelihood) RCM Estimator. The maximum likelihood Estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares Estimator of the RCM models is always inferior to the maximum likelihood Estimator, and in smaller samples dramatically so.
Nathaniel Beck - One of the best experts on this subject based on the ideXlab platform.
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random coefficient models for time series cross section data monte carlo experiments
Political Analysis, 2007Co-Authors: Nathaniel Beck, Jonathan N KatzAbstract:This article considers random coefficient models (RCMs) for time-series‐cross-section data. These models allow for unit to unit variation in the model parameters. The heart of the article compares the finite sample properties of the fully Pooled Estimator, the unit by unit (unPooled) Estimator, and the (maximum likelihood) RCM Estimator. The maximum likelihood Estimator RCM performs well, even where the data were generated so that the RCM would be problematic. In an appendix, we show that the most common feasible generalized least squares Estimator of the RCM models is always inferior to the maximum likelihood Estimator, and in smaller samples dramatically so.
Norma F Hubele - One of the best experts on this subject based on the ideXlab platform.
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the distribution of c pm when using a Pooled variance Estimator with some results
Annual meeting of the American Statistical Association : 05 08 2001 - 09 08 2001, 2001Co-Authors: Kerstin Vannman, Norma F HubeleAbstract:Correct use of capability indices by organizations requires knowledge about their Estimators and the corresponding distributions. In this paper, we address the situation when the data are given in subsamples and the Pooled Estimator is used for the variance. Here, the power function for hypothesis testing of Cpm is investigated The primary focus is on studying the effect of subgroup size and number of subsamples used in the Pooled variance Estimator on the efficiency of hypothesis testing procedures involving Cpm. Examples are presented. The results show that using a variance Estimator based on one sample provides a more powerful test of capability than using a Pooled variance Estimator, based on the same number of observations, from many smaller samples.
Das Samarjit - One of the best experts on this subject based on the ideXlab platform.
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Understanding Cross-sectional Dependence in Panel Data
2018Co-Authors: Basak, Gopal K, Das SamarjitAbstract:We provide various norm-based definitions of different types of cross-sectional dependence and the relations between them. These definitions facilitate to comprehend and to characterize the various forms of cross-sectional dependence, such as strong, semi-strong, and weak dependence. Then we examine the asymptotic properties of parameter Estimators both for fixed (within) effect Estimator and random effect (Pooled) Estimator for linear panel data models incorporating various forms of cross-sectional dependence. The asymptotic properties are also derived when both cross-sectional and temporal dependence are present. Subsequently, we develop consistent and robust standard error of the parameter Estimators both for fixed effect and random effect model separately. Robust standard errors are developed (i) for pure cross-sectional dependence; and (ii) also for cross-sectional and time series dependence. Under strong or semi-strong cross-sectional dependence, it is established that when the time dependence comes through the idiosyncratic errors, such time dependence does not have any influence in the asymptotic variance of $(\hat{\beta}_{FE/RE}). $ Hence, it is argued that in estimating $Var(\hat{\beta}_{FE/RE}),$ Newey-West kind of correction injects bias in the variance estimate. Furthermore, this article lay down conditions under which $t$, $F$ and the $Wald$ statistics based on the robust covariance matrix Estimator give valid inference.Comment: There are 27 page
Chong Sun Hong - One of the best experts on this subject based on the ideXlab platform.
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interval estimation of the difference of two population proportions using Pooled Estimator
Communications for Statistical Applications and Methods, 2002Co-Authors: Chong Sun HongAbstract:In order to examine whether the difference between two point estimates of population proportions is statistically significant, data analysts use two techniques. The first is to explore the overlap between two associated confidence intervals. Second method is to test the significance which is introduced at most statistical textbooks under the common assumptions of consistency, asymptotic normality, and asymptotic independence of the estimates. Under the null hypothesis which is two population proportions are equal, the Pooled Estimator (If population proportion is preferred as a point Estimator since two independent random samples are considered to be collected from one population. Hence as an alternative method, we could obtain another confidence interval of the difference of the population proportions with using the Pooled estimate. We conclude that, among three methods, the overlapped method is under-estimated, and the difference of the population proportions method is over-estimated on the basis of the proposed method.