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- Panel data (3)
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Articles 1 - 8 of 8
Full-Text Articles in Physical Sciences and Mathematics
Working Children On Java Island 2017, Yuniarti
Working Children On Java Island 2017, Yuniarti
English Language Institute
Children's wellbeing has currently become a global concern as many of them are engaged in the labor force. A small area estimation (SAE) technique, EBLUP under Fey Herriot model, is employed to reveal their number in regencies of Java Island. Statistics have been disaggregated by geographical location (urban/rural) and gender. These statistics are required by the government as the basis for policy making.
Stationary Points For Parametric Stochastic Frontier Models, William C. Horrace, Ian A. Wright
Stationary Points For Parametric Stochastic Frontier Models, William C. Horrace, Ian A. Wright
Center for Policy Research
The results of Waldman (1982) on the Normal-Half Normal stochastic frontier model are generalized using the theory of the Dirac delta (Dirac, 1930), and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and "wrong skew" results are derived or simulated for common parametric assumptions on the model. Identification is discussed.
Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi
Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi
Center for Policy Research
This paper extends Pesaran's (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997a, 2010) is applied to estimate the common change points, and the consistency …
Estimation And Identification Of Change Points In Panel Models With Nonstationary Or Stationary Regressors And Error Term, Badi H. Baltagi, Chihwa Kao, Long Liu
Estimation And Identification Of Change Points In Panel Models With Nonstationary Or Stationary Regressors And Error Term, Badi H. Baltagi, Chihwa Kao, Long Liu
Center for Policy Research
This paper studies the estimation of change point in panel models. We extend Bai (2010) and Feng, Kao and Lazarová (2009) to the case of stationary or nonstationary regressors and error term, and whether the change point is present or not. We prove consistency and derive the asymptotic distributions of the Ordinary Least Squares (OLS) and First Difference (FD) estimators. We find that the FD estimator is robust for all cases considered.
Policy Relevant Heterogeneity In The Value Of Statistical Life: New Evidence From Panel Data Quantile Regressions, Thomas J. Kniesner, W. Kip Viscusi
Policy Relevant Heterogeneity In The Value Of Statistical Life: New Evidence From Panel Data Quantile Regressions, Thomas J. Kniesner, W. Kip Viscusi
Center for Policy Research
We examine differences in the value of statistical life (VSL) across potential wage levels in panel data using quantile regressions with intercept heterogeneity. Latent heterogeneity is econometrically important and affects the estimated VSL. Our findings indicate that a reasonable average cost per expected life saved cut-off for health and safety regulations is $7 million to $8 million per life saved, but the VSL varies considerably cross the labor force. Our results reconcile the previous discrepancies between hedonic VSL estimates and the values implied by theories linked to the coefficient of relative risk aversion. Because the VSL varies elastically with income, …
The Value Of Statistical Life: Pursuing The Deadliest Catch, Kurt E. Schnier, William C. Horrace, Ronald G. Felthoven
The Value Of Statistical Life: Pursuing The Deadliest Catch, Kurt E. Schnier, William C. Horrace, Ronald G. Felthoven
Center for Policy Research
Observed tradeoffs between monetary returns and fatality risk identify estimates of the value of a statistical life (VSL), inform public policy and quantity preferences for environmental quality, health and safety. To date, few investigations have estimated the VSL associated with tradeoffs between returns from natural resource extraction activities and the fatality risks they involve. Furthermore researchers have been unable to determine whether or not one's VSL is stable across multiple decision environments using revealed preference methods. Understanding these tradeoffs (and the VSL that they imply) may be used to inform resource management policy and safety regulations, as well as our …
Testing For Sphericity In A Fixed Effects Panel Data Model (Revised July 2009), Badi H. Baltagi, Qu Feng, Chihwa Kao
Testing For Sphericity In A Fixed Effects Panel Data Model (Revised July 2009), Badi H. Baltagi, Qu Feng, Chihwa Kao
Center for Policy Research
This paper proposes a test for sphericity in a fixed effects panel data model. It uses the Random Matrix Theory based approach of Ledoit and Wolf (2002) to test for sphericity of the error terms in a fixed effects panel model with a large number of cross-sectional units and time series observations. Since the errors are unobservable, the residuals from the fixed effects regression are used. The limiting distribution of the proposed test statistic is derived. Additionally, its finite sample properties are examined using Monte Carlo simulations.
Joint Lm Test For Homoskedasticity In A One-Way Error Component Model, Badi H. Baltagi, Georges Bresson, Alain Pirotte
Joint Lm Test For Homoskedasticity In A One-Way Error Component Model, Badi H. Baltagi, Georges Bresson, Alain Pirotte
Center for Policy Research
This paper considers a general heteroskedastic error component model using panel data, and derives a joint LM test for homoskedasticity against the alternative of heteroskedasticity in both error components. It contrasts this joint LM test with marginal LM tests that ignore the heteroskedasticity in one of the error components. Monte Carlo results show that misleading inference can occur when using marginal rather than joint tests when heteroskedasticity is present in both components.