The Experts below are selected from a list of 144153 Experts worldwide ranked by ideXlab platform
Badi H. Baltagi - One of the best experts on this subject based on the ideXlab platform.
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testing for shifts in a time trend Panel Data Model with serially correlated error component disturbances
Econometric Reviews, 2020Co-Authors: Badi H. Baltagi, Chihwa Kao, Long LiuAbstract:This paper studies testing of shifts in a time trend Panel Data Model with serially correlated error component disturbances, without any prior knowledge of whether the error term is stationary or n...
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testing for shifts in a time trend Panel Data Model with serially correlated error component disturbances
Research Papers in Economics, 2019Co-Authors: Badi H. Baltagi, Chihwa Kao, Long LiuAbstract:This paper studies testing of shifts in a time trend Panel Data Model with serially correlated error component disturbances, without any prior knowledge of whether the error term is sta- tionary or nonstationary. This is done in case the shift is known as well as unknown. Following Vogelsang (1997) in the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator derived in Baltagi, et al. (2014). The proposed test has a Chi-square limiting distribution and is valid for both J(O) and J(l) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations.
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a time space dynamic Panel Data Model with spatial moving average errors
Research Papers in Economics, 2018Co-Authors: Badi H. Baltagi, Bernard Fingleton, Alain PirotteAbstract:This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic Panel Data Model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001–2012, with the last two years reserved for prediction.
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random effects fixed effects and hausman s test for the generalized mixed regressive spatial autoregressive Panel Data Model
Econometric Reviews, 2016Co-Authors: Badi H. Baltagi, Long LiuAbstract:This article suggests random and fixed effects spatial two-stage least squares estimators for the generalized mixed regressive spatial autoregressive Panel Data Model. This extends the generalized spatial Panel Model of Baltagi et al. (2013) by the inclusion of a spatial lag term. The estimation method utilizes the Generalized Moments method suggested by Kapoor et al. (2007) for a spatial autoregressive Panel Data Model. We derive the asymptotic distributions of these estimators and suggest a Hausman test a la Mutl and Pfaffermayr (2011) based on the difference between these estimators. Monte Carlo experiments are performed to investigate the performance of these estimators as well as the corresponding Hausman test.
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Prediction in a Generalized Spatial Panel Data Model with Serial Correlation
Journal of Forecasting, 2016Co-Authors: Badi H. Baltagi, Long LiuAbstract:This paper considers the generalized spatial Panel Data Model with serial correlation proposed by Lee and Yu (Spatial Panels: random components versus fixed effects. International Economic Review 2012; 53: 1369–1412.), which encompasses many of the spatial Panel Data Models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that Model. This in turn provides valuable BLUP for several spatial Panel Models as Special Cases. Copyright © 2016 John Wiley & Sons, Ltd.
Long Liu - One of the best experts on this subject based on the ideXlab platform.
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A bias-corrected fixed effects estimator in the dynamic Panel Data Model
Empirical Economics, 2021Co-Authors: Long Liu, Chihwa Kao, Rui SunAbstract:In this paper, we propose a biased-corrected FE estimator for the dynamic Panel Data Model that works for the autoregressive coefficient $$\rho \in (-1,1]$$ ρ ∈ ( - 1 , 1 ] . We further derive the asymptotic result of the suggested bias-corrected FE estimator. We show that when $$\rho =1$$ ρ = 1 , the suggested estimator is super-consistent and is more efficient than the existing estimators that also work for $$\rho \in (-1,1]$$ ρ ∈ ( - 1 , 1 ] . In addition, when the initial condition is nonstationary, many of the existing dynamic estimators become inconsistent; however, the consistency of the bias-corrected FE estimator we propose does not depend on the stationarity of the initial condition. We also compare the finite sample performances of these estimators using Monte Carlo simulations.
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testing for shifts in a time trend Panel Data Model with serially correlated error component disturbances
Econometric Reviews, 2020Co-Authors: Badi H. Baltagi, Chihwa Kao, Long LiuAbstract:This paper studies testing of shifts in a time trend Panel Data Model with serially correlated error component disturbances, without any prior knowledge of whether the error term is stationary or n...
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testing for shifts in a time trend Panel Data Model with serially correlated error component disturbances
Research Papers in Economics, 2019Co-Authors: Badi H. Baltagi, Chihwa Kao, Long LiuAbstract:This paper studies testing of shifts in a time trend Panel Data Model with serially correlated error component disturbances, without any prior knowledge of whether the error term is sta- tionary or nonstationary. This is done in case the shift is known as well as unknown. Following Vogelsang (1997) in the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator derived in Baltagi, et al. (2014). The proposed test has a Chi-square limiting distribution and is valid for both J(O) and J(l) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations.
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random effects fixed effects and hausman s test for the generalized mixed regressive spatial autoregressive Panel Data Model
Econometric Reviews, 2016Co-Authors: Badi H. Baltagi, Long LiuAbstract:This article suggests random and fixed effects spatial two-stage least squares estimators for the generalized mixed regressive spatial autoregressive Panel Data Model. This extends the generalized spatial Panel Model of Baltagi et al. (2013) by the inclusion of a spatial lag term. The estimation method utilizes the Generalized Moments method suggested by Kapoor et al. (2007) for a spatial autoregressive Panel Data Model. We derive the asymptotic distributions of these estimators and suggest a Hausman test a la Mutl and Pfaffermayr (2011) based on the difference between these estimators. Monte Carlo experiments are performed to investigate the performance of these estimators as well as the corresponding Hausman test.
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Prediction in a Generalized Spatial Panel Data Model with Serial Correlation
Journal of Forecasting, 2016Co-Authors: Badi H. Baltagi, Long LiuAbstract:This paper considers the generalized spatial Panel Data Model with serial correlation proposed by Lee and Yu (Spatial Panels: random components versus fixed effects. International Economic Review 2012; 53: 1369–1412.), which encompasses many of the spatial Panel Data Models considered in the literature, and derives the best linear unbiased predictor (BLUP) for that Model. This in turn provides valuable BLUP for several spatial Panel Models as Special Cases. Copyright © 2016 John Wiley & Sons, Ltd.
Chihwa Kao - One of the best experts on this subject based on the ideXlab platform.
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A bias-corrected fixed effects estimator in the dynamic Panel Data Model
Empirical Economics, 2021Co-Authors: Long Liu, Chihwa Kao, Rui SunAbstract:In this paper, we propose a biased-corrected FE estimator for the dynamic Panel Data Model that works for the autoregressive coefficient $$\rho \in (-1,1]$$ ρ ∈ ( - 1 , 1 ] . We further derive the asymptotic result of the suggested bias-corrected FE estimator. We show that when $$\rho =1$$ ρ = 1 , the suggested estimator is super-consistent and is more efficient than the existing estimators that also work for $$\rho \in (-1,1]$$ ρ ∈ ( - 1 , 1 ] . In addition, when the initial condition is nonstationary, many of the existing dynamic estimators become inconsistent; however, the consistency of the bias-corrected FE estimator we propose does not depend on the stationarity of the initial condition. We also compare the finite sample performances of these estimators using Monte Carlo simulations.
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testing for shifts in a time trend Panel Data Model with serially correlated error component disturbances
Econometric Reviews, 2020Co-Authors: Badi H. Baltagi, Chihwa Kao, Long LiuAbstract:This paper studies testing of shifts in a time trend Panel Data Model with serially correlated error component disturbances, without any prior knowledge of whether the error term is stationary or n...
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testing for shifts in a time trend Panel Data Model with serially correlated error component disturbances
Research Papers in Economics, 2019Co-Authors: Badi H. Baltagi, Chihwa Kao, Long LiuAbstract:This paper studies testing of shifts in a time trend Panel Data Model with serially correlated error component disturbances, without any prior knowledge of whether the error term is sta- tionary or nonstationary. This is done in case the shift is known as well as unknown. Following Vogelsang (1997) in the time series literature, we propose a Wald type test statistic that uses a fixed effects feasible generalized least squares (FE-FGLS) estimator derived in Baltagi, et al. (2014). The proposed test has a Chi-square limiting distribution and is valid for both J(O) and J(l) errors. The finite sample size and power of this Wald test is investigated using Monte Carlo simulations.
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a lagrange multiplier test for cross sectional dependence in a fixed effects Panel Data Model
Journal of Econometrics, 2012Co-Authors: Badi H. Baltagi, Qu Feng, Chihwa KaoAbstract:Abstract It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR Model is not appropriate for testing cross-sectional dependence in Panel Data Models when the number of cross-sectional units ( n ) is large and the number of time periods ( T ) is small. In fact, a scaled version of this LM test was proposed by Pesaran (2004) and its finite sample bias was corrected by Pesaran et al. (2008) . This was done in the context of a heterogeneous Panel Data Model. This paper derives the asymptotic bias of this scaled version of the LM test in the context of a fixed effects homogeneous Panel Data Model. This asymptotic bias is found to be a constant related to n and T , which suggests a simple bias corrected LM test for the null hypothesis. Additionally, the paper carries out some Monte Carlo experiments to compare the finite sample properties of this proposed test with existing tests for cross-sectional dependence.
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a lagrange multiplier test for cross sectional dependence in a fixed effects Panel Data Model
Research Papers in Economics, 2012Co-Authors: Badi H. Baltagi, Qu Feng, Chihwa KaoAbstract:It is well known that the standard Breusch and Pagan (1980) LM test for cross-equation correlation in a SUR Model is not appropriate for testing cross-sectional dependence in Panel Data Models when the number of cross-sectional units (n) is large and the number of time periods (T) is small. In fact, a scaled version of this LM test was proposed by Pesaran (2004) and its finite sample bias was corrected by Pesaran, Ullah and Yamagata (2008). This was done in the context of a heterogeneous Panel Data Model. This paper derives the asymptotic bias of this scaled version of the LM test in the context of a fixed effects homogeneous Panel Data Model.This asymptotic bias is found to be a constant related to n and T, which suggests a simple bias corrected LM test for the null hypothesis. Additionally, the paper carries out some Monte Carlo experiments to compare the finite sample properties of this proposed test with existing tests for cross-sectional dependence.
Alain Pirotte - One of the best experts on this subject based on the ideXlab platform.
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a time space dynamic Panel Data Model with spatial moving average errors
Research Papers in Economics, 2018Co-Authors: Badi H. Baltagi, Bernard Fingleton, Alain PirotteAbstract:This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic Panel Data Model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001–2012, with the last two years reserved for prediction.
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a multidimensional spatial lag Panel Data Model with spatial moving average nested random effects errors
Post-Print, 2018Co-Authors: Bernard Fingleton, Julie Le Gallo, Alain PirotteAbstract:This paper focuses on a three-dimensional Model that combines two different types of spatial interaction effects, i.e. endogenous interaction effects via a spatial lag on the dependent variable and interaction effects among the disturbances via a spatial moving average (SMA) nested random effects errors. A three-stage procedure is proposed to estimate the parameters. In a first stage, the spatial lag Panel Data Model is estimated using an instrumental variable (IV) estimator. In a second stage, a generalized moments (GM) approach is developed to estimate the SMA parameter and the variance components of the disturbance process using IV residuals from the first stage. In a third stage, to purge the equation of the specific structure of the disturbances a Cochrane–Orcutt-type transformation is applied combined with the IV principle. This leads to the GM spatial IV estimator and the regression parameter estimates. Monte Carlo simulations show that our estimators are not very different in terms of root mean square error from those produced by maximum likelihood. The approach is applied to European Union regional employment Data for regions nested within countries.
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estimating and forecasting with a dynamic spatial Panel Data Model
Research Papers in Economics, 2011Co-Authors: Badi H. Baltagi, Bernard Fingleton, Alain PirotteAbstract:This paper focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag Panel Data Model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial GMM estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the Spatial AutoRegressive (SAR) error Model. The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic Model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non-spatial estimators and illustrate our approach with an application to new economic geography.
Michael Pfaffermayr - One of the best experts on this subject based on the ideXlab platform.
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a generalized spatial Panel Data Model with random effects
Econometric Reviews, 2013Co-Authors: Badi H. Baltagi, Peter Egger, Michael PfaffermayrAbstract:This paper proposes a generalized Panel Data Model with random effects and first-order spatially autocorrelated residuals that encompasses two previously suggested specifications. The first one is described in Anselin's (1988) book and the second one by Kapoor et al. (2007). Our encompassing specification allows us to test for these Models as restricted specifications. In particular, we derive three Lagrange multiplier (LM) and likelihood ration (LR) tests that restrict our generalized Model to obtain (i) the Anselin Model, (ii) the Kapoor, Kelejian, and Prucha Model, and (iii) the simple random effects Model that ignores the spatial correlation in the residuals. For two of these three tests, we obtain closed form solutions and we derive their large sample distributions. Our Monte Carlo results show that the suggested tests are powerful in testing for these restricted specifications even in small and medium sized samples.
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a generalized spatial Panel Data Model with random effects
Social Science Research Network, 2009Co-Authors: Badi H. Baltagi, Peter Egger, Michael PfaffermayrAbstract:This paper proposes a generalized Panel Data Model with random effects and first-order spatially autocorrelated residuals that encompasses two previously suggested specifications. The first one is described in Anselin's (1988) book and the second one by Kapoor, Kelejian, and Prucha (2007). Our encompassing specification allows us to test for these Models as restricted specifications. In particular, we derive three LM and LR tests that restrict our generalized Model to obtain (i) the Anselin Model, (ii) the Kapoor, Kelejian, and Prucha Model, and (iii) the simple random effects Model that ignores the spatial correlation in the residuals. For two of these three tests, we obtain closed form solutions and we derive their large sample distributions. Our Monte Carlo results show that the suggested tests are powerful in testing for these restricted specifications even in small and medium sized samples.
