The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Hashem M Pesaran - One of the best experts on this subject based on the ideXlab platform.
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estimation of time invariant effects in static panel data models
Econometric Reviews, 2018Co-Authors: Hashem M Pesaran, Qiankun ZhouAbstract:ABSTRACTThis article proposes the Fixed Effects Filtered (FEF) and Fixed Effects Filtered instrumental variable (FEF-IV) estimators for estimation and inference in the case of time-invariant effects in static panel data models when N is large and T is fixed. The FEF-IV allows for endogenous time-invariant regressors but assumes that there exists a sufficient number of instruments for such regressors. It is shown that the FEF and FEF-IV estimators are -consistent and asymptotically normally distributed. The FEF estimator is compared with the Fixed Effects Vector Decomposition (FEVD) estimator proposed by Plumper and Troeger (2007) and conditions under which the two estimators are equivalent are established. It is also shown that the variance estimator proposed for FEVD estimator is inconsistent and its use could lead to misleading inference. Alternative variance estimators are proposed for both FEF and FEF-IV estimators which are shown to be consistent under fairly general conditions. The small sample prop...
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estimation of time invariant effects in static panel data models
Social Science Research Network, 2014Co-Authors: Hashem M Pesaran, Qiankun ZhouAbstract:This paper proposes the Fixed Effects Filtered (FEF) and Fixed Effects Filtered instrumental variable (FEF-IV) estimators for estimation and inference in the case of time-invariant effects in static panel data models when N is large and T is fixed. It is shown that the FEF and FEF-IV estimators are √N-consistent, and asymptotically normally distributed. The FEF estimator is compared with the Fixed Effects Vector Decomposition (FEVD) estimator proposed by Plumper and Troeger (2007) and conditions under which the two estimators are equivalent are established. It is also shown that the variance estimator proposed for FEVD estimator is inconsistent and its use could lead to misleading inference. Alternative variance estimators are proposed for both FEF and FEF-IV estimators which are shown to be consistent under fairly general conditions. The small sample properties of the FEF and FEF-IV estimators are investigated by Monte Carlo experiments, and it is shown that FEF has smaller bias and RMSE, unless an intercept is included in the second stage of the FEVD procedure which renders the FEF and FEVD estimators identical. The FEVD procedure, however, results in substantial size distortions since it uses incorrect standard errors. We also compare the FEF-IV estimator with the estimator proposed by Hausman and Taylor (1981), when one of the time-invariant regressors is correlated with the fixed effects. Both FEF and FEF-IV estimators are shown to be robust to error variance heteroskedasticity and residual serial correlation.
Sat Gupta - One of the best experts on this subject based on the ideXlab platform.
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estimation of population coefficient of variation in simple and stratified random sampling under two phase sampling scheme when using two auxiliary variables
Communications in Statistics-theory and Methods, 2017Co-Authors: Sat GuptaAbstract:We propose an improved class of exponential ratio type estimators for coefficient of variation (CV) of a finite population in simple and stratified random sampling using two auxiliary variables under two-phase sampling scheme. We examine the properties of the proposed estimators based on first order of approximation. The proposed class of estimators is more efficient than the usual sample coefficient of variation (CV) estimator, ratio estimator, exponential ratio estimator, usual difference estimator and Hamad et al. (2013) difference type estimator. We also use real data sets for numerical comparisons.
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improved family of estimators of population variance in simple random sampling
Journal of statistical theory and practice, 2015Co-Authors: Subhash Kumar Yadav, Cem Kadilar, Javid Shabbir, Sat GuptaAbstract:In this article, we suggest a general procedure for estimating the population variance through a class of estimators. The bias and mean square error (MSE) of the proposed class of estimators are obtained to the first degree of approximation. The proposed class of estimators is more efficient than many other estimators, such as the usual variance estimator, ratio estimator, the Bahal and Tuteja (1991) exponential estimator, the traditional regression estimator, the Rao (1991) estimator, the Upadhyaya and Singh (1999) estimator, and the Kadilar and Cingi (2006) estimators. Four data sets are used for numerical comparison.
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Some Estimators of Finite Population Variance of Stratified Sample Mean
Communications in Statistics - Theory and Methods, 2010Co-Authors: Javid Shabbir, Sat GuptaAbstract:This article proposes a ratio-type estimator for estimating the variance of the stratified sample mean using auxiliary information. The proposed estimator is found to perform better than the usual unbiased variance estimator, stratified ratio and regression estimators, and stratified version of Prasad and Singh (1992) estimator. We use five data sets to compare the performances of all of the estimators considered here.
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on estimation of finite population variance
Journal of Interdisciplinary Mathematics, 2006Co-Authors: Javid Shabbir, Sat GuptaAbstract:Abstract Following Searls (1964), we propose an estimator for estimating the finite population variance. This estimator is the combination of Singh et al. (1973), and Prasad and Singh (1992) estimators and has an improvement over Singh et al. (1973), Prasad and Singh (1992), and several other estimators under certain conditions. Validity of proposed estimator is examined by using seven numerical examples.
Qiankun Zhou - One of the best experts on this subject based on the ideXlab platform.
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estimation of time invariant effects in static panel data models
Econometric Reviews, 2018Co-Authors: Hashem M Pesaran, Qiankun ZhouAbstract:ABSTRACTThis article proposes the Fixed Effects Filtered (FEF) and Fixed Effects Filtered instrumental variable (FEF-IV) estimators for estimation and inference in the case of time-invariant effects in static panel data models when N is large and T is fixed. The FEF-IV allows for endogenous time-invariant regressors but assumes that there exists a sufficient number of instruments for such regressors. It is shown that the FEF and FEF-IV estimators are -consistent and asymptotically normally distributed. The FEF estimator is compared with the Fixed Effects Vector Decomposition (FEVD) estimator proposed by Plumper and Troeger (2007) and conditions under which the two estimators are equivalent are established. It is also shown that the variance estimator proposed for FEVD estimator is inconsistent and its use could lead to misleading inference. Alternative variance estimators are proposed for both FEF and FEF-IV estimators which are shown to be consistent under fairly general conditions. The small sample prop...
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estimation of time invariant effects in static panel data models
Social Science Research Network, 2014Co-Authors: Hashem M Pesaran, Qiankun ZhouAbstract:This paper proposes the Fixed Effects Filtered (FEF) and Fixed Effects Filtered instrumental variable (FEF-IV) estimators for estimation and inference in the case of time-invariant effects in static panel data models when N is large and T is fixed. It is shown that the FEF and FEF-IV estimators are √N-consistent, and asymptotically normally distributed. The FEF estimator is compared with the Fixed Effects Vector Decomposition (FEVD) estimator proposed by Plumper and Troeger (2007) and conditions under which the two estimators are equivalent are established. It is also shown that the variance estimator proposed for FEVD estimator is inconsistent and its use could lead to misleading inference. Alternative variance estimators are proposed for both FEF and FEF-IV estimators which are shown to be consistent under fairly general conditions. The small sample properties of the FEF and FEF-IV estimators are investigated by Monte Carlo experiments, and it is shown that FEF has smaller bias and RMSE, unless an intercept is included in the second stage of the FEVD procedure which renders the FEF and FEVD estimators identical. The FEVD procedure, however, results in substantial size distortions since it uses incorrect standard errors. We also compare the FEF-IV estimator with the estimator proposed by Hausman and Taylor (1981), when one of the time-invariant regressors is correlated with the fixed effects. Both FEF and FEF-IV estimators are shown to be robust to error variance heteroskedasticity and residual serial correlation.
G Prabavathy - One of the best experts on this subject based on the ideXlab platform.
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median based modified ratio estimators with known skewness and correlation coefficient for the estimation of finite population mean
Journal of Reliability and Statistical Studies, 2015Co-Authors: Jambulingam Subramani, G PrabavathyAbstract:In this study, two new median based modified ratio estimators with the linear combinations of population correlation coefficient and skewness of an auxiliary variable have been proposed. The bias and mean squared error of the proposed estimators are obtained and the efficiencies of the proposed estimators are compared with that of the simple random sampling without replacement (SRSWOR) sample mean, the usual ratio estimator, the corresponding modified ratio estimators, the linear regression estimator and the median based ratio estimator for certain natural populations. It is shown from the numerical study that the proposed median based modified ratio estimators are outperformed all the existing estimators mentioned above including the linear regression estimator.
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Median Based Modified Ratio Estimators with Known Quartiles of an Auxiliary Variable
Journal of Modern Applied Statistical Methods, 2014Co-Authors: G PrabavathyAbstract:New median based modified ratio estimators for estimating a finite population mean using quartiles and functions of an auxiliary variable are proposed. The bias and mean squared error of the proposed estimators are obtained and the mean squared error of the proposed estimators are compared with the usual simple random sampling without replacement (SRSWOR) sample mean, ratio estimator, a few existing modified ratio estimators, the linear regression estimator and median based ratio estimator for certain natural populations. A numerical study shows that the proposed estimators perform better than existing estimators; in addition, it is shown that the proposed median based modified ratio estimators outperform the ratio and modified ratio estimators as well as the linear regression estimator.
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MEDIAN BASED MODIFIED RATIO ESTIMATORS WITH LINEAR COMBINATIONS OF POPULATION MEAN AND MEDIAN OF AN AUXILIARY VARIABLE
2013Co-Authors: G Prabavathy, E MailAbstract:In this paper two new median based modified ratio estimators for the estimation of finite population mean using the linear combinations of population mean and median of the auxiliary variable have been proposed. The bias and mean squared error of the proposed estimators are derived and the mean squared errors are compared with that of the SRSWOR sample mean, ratio estimator, linear regression estimator and median based ratio estimator for certain natural populations. It is observed from the numerical comparisons that the proposed median based modified ratio estimators have outperformed the existing estimators including the linear regression estimator
Jambulingam Subramani - One of the best experts on this subject based on the ideXlab platform.
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median based modified ratio estimators with known skewness and correlation coefficient for the estimation of finite population mean
Journal of Reliability and Statistical Studies, 2015Co-Authors: Jambulingam Subramani, G PrabavathyAbstract:In this study, two new median based modified ratio estimators with the linear combinations of population correlation coefficient and skewness of an auxiliary variable have been proposed. The bias and mean squared error of the proposed estimators are obtained and the efficiencies of the proposed estimators are compared with that of the simple random sampling without replacement (SRSWOR) sample mean, the usual ratio estimator, the corresponding modified ratio estimators, the linear regression estimator and the median based ratio estimator for certain natural populations. It is shown from the numerical study that the proposed median based modified ratio estimators are outperformed all the existing estimators mentioned above including the linear regression estimator.
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A NEW MODIFIED RATIO ESTIMATOR FOR ESTIMATION OF POPULATION MEAN WHEN MEDIAN OF THE AUXILIARY VARIABLE IS KNOWN
Pakistan Journal of Statistics and Operation Research, 2013Co-Authors: Jambulingam SubramaniAbstract:The present paper deals with a modified ratio estimator for estimation of population mean of the study variable when the population median of the auxiliary variable is known. The bias and mean squared error of the proposed estimator are derived and are compared with that of existing modified ratio estimators for certain known populations. Further we have also derived the conditions for which the proposed estimator performs better than the existing modified ratio estimators. From the numerical study it is also observed that the proposed modified ratio estimator performs better than the existing modified ratio estimators for certain known populations.
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estimation of variance using known coefficient of variation and median of an auxiliary variable
Journal of Modern Applied Statistical Methods, 2013Co-Authors: Jambulingam Subramani, G. Kumarapandiyan, R V NagarAbstract:A modified ratio type variance estimator for estimating population variance of a study variable when the population median and coefficient of variation of an auxiliary variable are known is proposed. The bias and mean squared error of the proposed estimator are derived and conditions under which the proposed estimator performs better than the traditional ratio type variance estimators and modified ratio type variance estimators are obtained. Using a numerical study results show that the proposed estimator performs better than the traditional ratio type variance estimator and existing modified ratio type variance estimators.
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variance estimation using quartiles and their functions of an auxiliary variable
International journal of statistics and applications, 2012Co-Authors: Jambulingam Subramani, G. KumarapandiyanAbstract:In this paper we have proposed a class of modified ratio type variance estimators for estimation of population variance of the study variable using Quartiles and their functions of the auxiliary variable are known. The biases and mean squared errors of the proposed estimators are obtained and also derived the conditions for which the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators. Further we have compared the proposed estimators with that of traditional ratio type variance estimator and existing modified ratio type variance estimators for certain known populations. From the numerical study it is observed that the proposed estimators perform better than the traditional ratio type variance estimator and existing modified ratio type variance estimators.
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variance estimation using median of the auxiliary variable
International Journal of Probability and Statistics, 2012Co-Authors: Jambulingam Subramani, G. KumarapandiyanAbstract:The present paper deals with a modified ratio type variance estimator for estimation of population variance of the study variable, when the population median of the auxiliary variable is known. The bias and the mean squared error of the proposed estimator are obtained and also derived the conditions for which the proposed estimator performs better than the traditional ratio type variance estimator suggested by Isaki[10] and the modified ratio type variance estimators suggested by Kadilar and Cingi[11]. Further we have compared the efficiencies of the proposed estimator with that of traditional ratio type variance estimator and existing modified ratio type variance estimators for certain known populations. From the numerical study it is observed that the proposed estimator performs better than the traditional ratio type variance estimator and existing modified ratio type variance estimators.
