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 Bias correction methods for dynamic panel data models with fixed effects

2017 ◽  
Vol 6 (2) ◽  
pp. 58
Author(s):  
Mohamed Abonazel
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Estimation Methods ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Cross Sectional ◽  
Cross Sectional Dimension ◽  
Ls Estimator

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although consistent estimates can be obtained by GMM procedures, the inconsistent LS estimator has a relatively low variance and hence can lead to an estimator with lower root mean square error after the bias is removed. Therefore, we discuss in this paper the different methods to correct the bias of LS and GMM estimations. The analytical expressions for the asymptotic biases of the LS and GMM estimators have been presented for large N and finite T. Finally; we display new estimators that presented by Youssef and Abonazel [40] as more efficient estimators than the conventional estimators.

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.

Empirical and theoretical analyses of correction of time-series bias in stock-recruitment relationships of sockeye salmon (Oncorhynchus nerka)

1995 ◽  
Vol 52 (10) ◽  
pp. 2174-2189 ◽  
Author(s):  
Josh Korman ◽  
Randall M. Peterman ◽  
Carl J. Walters
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Bias Correction ◽  
Sockeye Salmon ◽  
Oncorhynchus Nerka ◽  
Empirical Relationship ◽  
Estimation Method ◽  
Estimation Methods ◽  
Theoretic Approach ◽  
Stock Recruitment

Using data from 30 sockeye salmon (Oncorhynchus nerka) stocks and Monte Carlo simulations, we examined the importance of time-series bias on estimates of optimal harvest rate, optimal escapement, and sustainable yield. We compared the performance of the least-squares procedure for fitting a Ricker curve with an existing bias-correction method. Simulations showed that the effect of time-series bias is greatest for low-productivity stocks that exhibit a high degree of autocorrelation among residuals of the stock-recruitment relationship. A strong inverse empirical relationship between autocorrelation and stock productivity among the 30 stocks suggests that time-series bias is a more important concern for low-productivity northern stocks than for more productive southern stocks. The corrected method reduced bias in optimal escapement estimates under a limited set of conditions but at the price of increased variance in the estimates. For a constant escapement goal policy, using the bias correction thus resulted in sustainable yields slightly lower than or equal to expected values for 28 of the 30 stocks compared with yields obtained using the standard least-squares estimation method. We demonstrate the value of using a decision theoretic approach to evaluate the performance of estimation methods.

scholarly journals A Crook is a Crook . . . But is He Still a Crook Abroad? On the Effect of Immigration on Destination-Country Corruption

2015 ◽  
Vol 16 (4) ◽  
pp. 464-489 ◽  
Author(s):  
Eugen Dimant ◽  
Margarete Redlin ◽  
Tim Krieger
Keyword(s):  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Destination Country ◽  
Estimation Methods ◽  
Econometric Methodology ◽  
Migration Flows ◽  
The Difference ◽  
Method Of Moments Estimator ◽  
The Impact

AbstractThis paper analyzes the impact of migration on destination-country corruption levels. Capitalizing on a comprehensive dataset consisting of annual immigration stocks of OECD countries from 207 countries of origin for the period 1984-2008, we explore different channels through which corruption might migrate. We employ different estimation methods using fixed effects and Tobit regressions in order to validate our findings. Moreover, we also address the issue of endogeneity by using the Difference- Generalized Method of Moments estimator. Independent of the econometric methodology, we consistently find that while general migration has an insignificant effect on the destination country’s corruption level, immigration from corruption-ridden origin countries boosts corruption in the destination country. Our findings provide a more profound understanding of the socioeconomic implications associated with migration flows.

scholarly journals The Impact of Inflation Volatility on an Enterprise’s Innovation Strategy

2019 ◽  
Vol 8 (12) ◽  
pp. 193-197
Keyword(s):  
Fixed Effects ◽  
Negative Impact ◽  
Generalized Method Of Moments ◽  
Estimation Method ◽  
Innovation Strategy ◽  
Potential Output ◽  
Economic Agents ◽  
Inflation Volatility ◽  
High Inflation ◽  
The Impact

The objective of the study was to determine the effect of inflation volatility on an enterprise's innovation strategy. The study showed that increasing inflation leads to a decrease in the stationary level of potential output, as well as to a decrease in the rate of economic growth in the process of transition to a stationary state. A formula is proposed for calculating the total effect of inflation on the level of enterprise output. The negative impact of the inflation rate on the welfare of economic agents was revealed, which is expressed in the fall in their equilibrium consumption level. Higher-income countries have been shown to suffer more from high inflation than poorer countries. All conclusions made in the analysis of the dynamic model of the impact of inflation on potential output are verified based on econometric modelling using methods and models for panel data: models with fixed effects, models with random effects, and a generalized method of moments. Moreover, the obtained empirical results are stable concerning changes in the specification of the equation and estimation method

EXPRESS: Handling Endogenous Regressors using Copulas: A Generalization to Linear Panel Models with Fixed Effects and Correlated Regressors

2021 ◽  
pp. 002224372110708
Author(s):  
Rouven E. Haschka
Keyword(s):  
Fixed Effects ◽  
Estimation Method ◽  
Copula Function ◽  
Small Time ◽  
Joint Estimation ◽  
Gaussian Copula ◽  
Correlated Errors ◽  
Finite Sample ◽  
Panel Models ◽  
Endogenous Regressors

This paper proposes a panel data generalization for a recently suggested IVfree estimation method that builds on joint estimation. The author shows how the method can be extended to linear panel models by combining fixed-effects transformations with the common GLS transformation to allow for heterogeneous intercepts. To account for between-regressor dependence, the author proposes determining the joint distribution of the error term and all explanatory variables using a Gaussian copula function, with the distinction that some variables are endogenous and the others are exogenous. The identification does not require any instrumental variables if the regressor-error relation is nonlinear. With a normally distributed error, nonnormally distributed endogenous regressors are therefore required. Monte Carlo simulations assess the finite sample performance of the proposed estimator and demonstrate its superiority to conventional instrumental variable estimation. A specific advantage of the proposed method is that the estimator is unbiased in dynamic panel models with small time dimensions and serially correlated errors; therefore, it is a useful alternative to GMM-style instrumentation. The practical applicability of the proposed method is demonstrated via an empirical example.

Shadow Economy, Corruption, and Economic Growth: An Empirical Analysis

2019 ◽  
Vol 47 (3) ◽  
pp. 276-294 ◽  
Author(s):  
Nedra Baklouti ◽  
Younes Boujelbene
Keyword(s):  
Economic Growth ◽  
Least Squares ◽  
Public Administration ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Shadow Economy ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Negative Effects ◽  
Considerable Debate

There is considerable debate over the effects of both corruption and shadow economy on growth, but few studies have considered how the interaction between them might affect economic growth. We study how corruption levels in public administration affect economic growth and how this effect depends on the shadow economy. Using Ordinary Least Squares (OLS), fixed effects, and system generalized method of moments (GMM) on a dataset of 34 OECD countries over the period 1995-2014. The estimation results indicate that increased corruption and a larger shadow economy lead to decrease in economic growth. Results additionally indicate that the shadow economy magnifies the effect of corruption on economic growth. These results imply significant complementarities between corruption and the shadow economy, suggesting that the reduction of corruption will lead to a fall in the size of the shadow economy and will also reduce the negative effects of corruption on economic growth through the underground economy.

scholarly journals GMM ESTIMATION AND INFERENCE IN DYNAMIC PANEL DATA MODELS WITH PERSISTENT DATA

2009 ◽  
Vol 25 (5) ◽  
pp. 1348-1391 ◽  
Author(s):  
Hugo Kruiniger
Keyword(s):  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Dynamic Panel Data ◽  
Panel Data Models ◽  
Finite Sample ◽  
Time Dimension ◽  
Dynamic Panel ◽  
Weak Instruments ◽  
Distributional Properties ◽  
Persistent Data

In this paper we consider generalized method of moments–based (GMM-based) estimation and inference for the panel AR(1) model when the data are persistent and the time dimension of the panel is fixed. We find that the nature of the weak instruments problem of the Arellano–Bond (Arellano and Bond, 1991,Review of Economic Studies58, 277–297) estimator depends on the distributional properties of the initial observations. Subsequently, we derive local asymptotic approximations to the finite-sample distributions of the Arellano–Bond estimator and the System estimator, respectively, under a variety of distributional assumptions about the initial observations and discuss the implications of the results we obtain for doing inference. We also propose two Lagrange multiplier–type (LM-type) panel unit root tests.

Sign in / Sign up

Export Citation Format

Share Document