scholarly journals GMM ESTIMATION FOR DYNAMIC PANELS WITH FIXED EFFECTS AND STRONG INSTRUMENTS AT UNITY

2009 ◽  
Vol 26 (1) ◽  
pp. 119-151 ◽  
Author(s):  
Chirok Han ◽  
Peter C. B. Phillips
Keyword(s):  
Time Series ◽  
Fixed Effects ◽  
Estimation Method ◽  
Dynamic Panel Data ◽  
Small Samples ◽  
Moment Condition ◽  
Dynamic Panel ◽  
Gmm Estimation ◽  
Unit Root Testing ◽  
Dynamic Panels

This paper develops new estimation and inference procedures for dynamic panel data models with fixed effects and incidental trends. A simple consistent GMM estimation method is proposed that avoids the weak moment condition problem that is known to affect conventional GMM estimation when the autoregressive coefficient (ρ) is near unity. In both panel and time series cases, the estimator has standard Gaussian asymptotics for all values of ρ ∈ (−1, 1] irrespective of how the composite cross-section and time series sample sizes pass to infinity. Simulations reveal that the estimator has little bias even in very small samples. The approach is applied to panel unit root testing.

scholarly journals X-DIFFERENCING AND DYNAMIC PANEL MODEL ESTIMATION

2013 ◽  
Vol 30 (1) ◽  
pp. 201-251 ◽  
Author(s):  
Chirok Han ◽  
Peter C. B. Phillips ◽  
Donggyu Sul
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Limit Distribution ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Estimation Method ◽  
Estimation Methods ◽  
Finite Sample ◽  
Dynamic Panel ◽  
Dynamic Panel Model

This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting “panel fully aggregated” estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, consistent for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as bias corrected least squares, generalized method of moments (GMM), and system GMM methods. The asymptotic theory holds as long as the cross section (n) or time series (T) sample size is large, regardless of then/Tratio, which makes the approach appealing for practical work. In the time series AR(1) case (n= 1), the FAE estimator has a limit distribution with smaller bias and variance than the maximum likelihood estimator (MLE) when the autoregressive coefficient is at or near unity and the same limit distribution as the MLE in the stationary case, so the advantages of the approach continue to hold for fixed and even smalln. Some simulation results are reported, giving comparisons with other dynamic panel estimation methods.

scholarly journals Labor Productivity Convergence in Indonesia: Spatially Dynamic Panel Data Analysis

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Ahmad Fajar Novianto ◽  
Waris Marsisno
Keyword(s):  
Panel Data ◽  
Labor Productivity ◽  
Panel Data Analysis ◽  
Estimation Method ◽  
Dynamic Panel Data ◽  
Tertiary Sector ◽  
Dynamic Panel ◽  
Productivity Convergence ◽  
Time Required ◽  
Dynamic Panel Data Analysis

The problem of labor productivity in Indonesia is a regional and sectoral inequality. To know the time required to remove inequality, can be measured by the level of convergence of labor productivity. The research would analyze the rate of sectoral labor productivity convergence among provinces in Indonesia spatially and identify the determinant factors of labor productivity. The analytical methods used is spatial dinamic panel data with Spatially Corrected Blundell-Bond (SCBB) estimation method. The results show that there are spatially sectoral labor productivity convergence. Primary sector takes the longest half-life convergence of 7-8 years, while secondary takes 1-2 years and tertiary sector takes 3-4 years. Furthermore, the Gross Capital Fixed Formation, Mean Years of Schooling, and real wage sectoral are significantly have positive affect to the labor productivity while Life Expectancy is significantly have negative affect to labor productivity.Keywords : convergence, spatial analysis, labor productivity

scholarly journals KETEGARAN UPAH NOMINAL UNTUK TURUN: KASUS UPAH NOMINAL PEKERJA PRODUKSI DI BAWAH MANDOR PADA INDUSTRI BESAR DAN SEDANG MAKANAN JADI, BAHAN PAKAIAN, KARET, DAN PLASTIK

2009 ◽  
Vol 10 (1) ◽  
pp. 15
Author(s):  
Joko Susanto
Keyword(s):  
Regression Analysis ◽  
Data Model ◽  
Estimation Method ◽  
Capital Intensity ◽  
Dynamic Panel Data ◽  
Nominal Wage ◽  
Production Worker ◽  
Dynamic Panel ◽  
Regression Parameters ◽  
Over Time

This aim of the research is to test whether the decreasing productivity of the workers results in decreasing of the nominal wage of the production worker under the supervisor. Statistical data of BPS was used in this research. The research data is consist of the nominal base and over time wage of the production worker under the supervisor, productivity of workers, and capital intensity. Furthermore, this research used regression analysis with OLS estimation method. This regression analysis was based on the dynamic panel data model. Finally, this study used redundant coefficient test to reduce several insignificant regression parameters in order to get a parsimony model. The results of the research as follow: (1). the decreasing productivity of the workers does not result in decreasing the nominal base wages of the production workers under the supervisor. (2). the decreasing productivity of the workers results in decreasing of the over time wages of the production workers under the supervisor.

THE INFORMATION BOUND OF A DYNAMIC PANEL LOGIT MODEL WITH FIXED EFFECTS

2001 ◽  
Vol 17 (5) ◽  
pp. 913-932 ◽  
Author(s):  
Jinyong Hahn
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Fixed Effects ◽  
Dynamic Panel Data ◽  
Consistent Estimator ◽  
Likelihood Estimator ◽  
Dynamic Panel ◽  
Conditional Maximum Likelihood ◽  
Information Bound ◽  
Conditional Maximum

In this paper, I calculate the semiparametric information bound in two dynamic panel data logit models with individual specific effects. In such a model without any other regressors, it is well known that the conditional maximum likelihood estimator yields a √n-consistent estimator. In the case where the model includes strictly exogenous continuous regressors, Honoré and Kyriazidou (2000, Econometrica 68, 839–874) suggest a consistent estimator whose rate of convergence is slower than √n. Information bounds calculated in this paper suggest that the conditional maximum likelihood estimator is not efficient for models without any other regressor and that √n-consistent estimation is infeasible in more general models.

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