Ridge type estimators are used to estimate regression parameters in a multiple linear regression model when multicolinearity exists among predictor variables. When different estimators are available, preliminary test ...Ridge type estimators are used to estimate regression parameters in a multiple linear regression model when multicolinearity exists among predictor variables. When different estimators are available, preliminary test estimation procedure is adopted to select a suitable estimator. In this paper, two ridge estimators, the Stochastic Restricted Liu Estimator and Liu Estimator are combined to define a new preliminary test estimator, namely the Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE). The stochastic properties of the proposed estimator are derived, and the performance of PTSRLE is compared with SRLE in the sense of mean square error matrix (MSEM) and scalar mean square error (SMSE) for the two cases in which the stochastic restrictions are correct and not correct. Moreover the SMSE of PTSRLE based on Wald (WA), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are derived, and the performance of PTSRLE is compared using WA, LR and LM tests as a function of the shrinkage parameter d with respect to the SMSE. Finally a numerical example is given to illustrate some of the theoretical findings.展开更多
Researchers must understand that naively relying on the reliability of statistical software packages may result in suboptimal, biased, or erroneous results, which affects applied economic theory and the conclusions an...Researchers must understand that naively relying on the reliability of statistical software packages may result in suboptimal, biased, or erroneous results, which affects applied economic theory and the conclusions and policy recommendations drawn from it. To create confidence in a result, several software packages should be applied to the same estimation problem. This study examines the results of three software packages (EViews, R, and Stata) in the analysis of time-series econometric data. The time-series data analysis which presents the determinants of macroeconomic growth of Sri Lanka from 1978 to 2020 has been used. The study focuses on testing for stationarity, cointegration, and significant relationships among the variables. The Augmented Dickey-Fuller and Phillips Perron tests were employed in this study to test for stationarity, while the Johansen cointegration test was utilized to test for cointegration. The study employs the vector error correction model to assess the short-run and long-term dynamics of the variables in an attempt to determine the relationship between them. Finally, the Granger Causality test is employed in order to examine the linear causation between the concerned variables. The study revealed that the results produced by three software packages for the same dataset and the same lag order vary significantly. This implies that time series econometrics results are sensitive to the software that is used by the researchers while providing different policy implications even for the same dataset. The present study highlights the necessity of further analysis to investigate the impact of software packages in time series analysis of economic scenarios.展开更多
The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed e...The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.展开更多
In this paper we compare recently developed preliminary test estimator called Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE) with Ordinary Least Square Estimator (OLSE) and Mixed Estimator (ME) in the M...In this paper we compare recently developed preliminary test estimator called Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE) with Ordinary Least Square Estimator (OLSE) and Mixed Estimator (ME) in the Mean Square Error Matrix (MSEM) sense for the two cases in which the stochastic restrictions are correct and not correct. Finally a numerical example and a Monte Carlo simulation study are done to illustrate the theoretical findings.展开更多
文摘Ridge type estimators are used to estimate regression parameters in a multiple linear regression model when multicolinearity exists among predictor variables. When different estimators are available, preliminary test estimation procedure is adopted to select a suitable estimator. In this paper, two ridge estimators, the Stochastic Restricted Liu Estimator and Liu Estimator are combined to define a new preliminary test estimator, namely the Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE). The stochastic properties of the proposed estimator are derived, and the performance of PTSRLE is compared with SRLE in the sense of mean square error matrix (MSEM) and scalar mean square error (SMSE) for the two cases in which the stochastic restrictions are correct and not correct. Moreover the SMSE of PTSRLE based on Wald (WA), Likelihood Ratio (LR) and Lagrangian Multiplier (LM) tests are derived, and the performance of PTSRLE is compared using WA, LR and LM tests as a function of the shrinkage parameter d with respect to the SMSE. Finally a numerical example is given to illustrate some of the theoretical findings.
文摘Researchers must understand that naively relying on the reliability of statistical software packages may result in suboptimal, biased, or erroneous results, which affects applied economic theory and the conclusions and policy recommendations drawn from it. To create confidence in a result, several software packages should be applied to the same estimation problem. This study examines the results of three software packages (EViews, R, and Stata) in the analysis of time-series econometric data. The time-series data analysis which presents the determinants of macroeconomic growth of Sri Lanka from 1978 to 2020 has been used. The study focuses on testing for stationarity, cointegration, and significant relationships among the variables. The Augmented Dickey-Fuller and Phillips Perron tests were employed in this study to test for stationarity, while the Johansen cointegration test was utilized to test for cointegration. The study employs the vector error correction model to assess the short-run and long-term dynamics of the variables in an attempt to determine the relationship between them. Finally, the Granger Causality test is employed in order to examine the linear causation between the concerned variables. The study revealed that the results produced by three software packages for the same dataset and the same lag order vary significantly. This implies that time series econometrics results are sensitive to the software that is used by the researchers while providing different policy implications even for the same dataset. The present study highlights the necessity of further analysis to investigate the impact of software packages in time series analysis of economic scenarios.
文摘The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.
文摘In this paper we compare recently developed preliminary test estimator called Preliminary Test Stochastic Restricted Liu Estimator (PTSRLE) with Ordinary Least Square Estimator (OLSE) and Mixed Estimator (ME) in the Mean Square Error Matrix (MSEM) sense for the two cases in which the stochastic restrictions are correct and not correct. Finally a numerical example and a Monte Carlo simulation study are done to illustrate the theoretical findings.
