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Large panels with common factors and spatial correlations
 IZA DISCUSSION PAPER
, 2007
"... This paper considers the statistical analysis of large panel data sets where even after conditioning on common observed effects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common effects and/or if there are spi ..."
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Cited by 52 (5 self)
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This paper considers the statistical analysis of large panel data sets where even after conditioning on common observed effects the cross section units might remain dependently distributed. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the concepts of timespecific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix.
Weak and Strong Cross Section Dependence and Estimation of Large Panels
, 2009
"... This paper introduces the concepts of timespecific weak and strong cross section dependence. A doubleindexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic ..."
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Cited by 44 (22 self)
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This paper introduces the concepts of timespecific weak and strong cross section dependence. A doubleindexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.
Panel Cointegration with Global Stochastic Trends: Supplementary Appendix
, 2006
"... Part of the Econometrics Commons This Working Paper is brought to you for free and open access by the Maxwell School of Citizenship and Public Affairs at SURFACE. It has been accepted for inclusion in Center for Policy Research by an authorized administrator of SURFACE. For more information, please ..."
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Cited by 38 (2 self)
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Part of the Econometrics Commons This Working Paper is brought to you for free and open access by the Maxwell School of Citizenship and Public Affairs at SURFACE. It has been accepted for inclusion in Center for Policy Research by an authorized administrator of SURFACE. For more information, please contact
Exports and Wage Premia: Evidence from Mexican EmployerEmployee Data ∗
, 2009
"... This paper draws on a new combination of employeremployee and plantlevel data from Mexico to investigate the relationship between exports and wage premia, defined as wages above what workers would receive elsewhere in the labor market. We first use detailed information on individual workers ’ wage ..."
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Cited by 33 (0 self)
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This paper draws on a new combination of employeremployee and plantlevel data from Mexico to investigate the relationship between exports and wage premia, defined as wages above what workers would receive elsewhere in the labor market. We first use detailed information on individual workers ’ wage histories to decompose plantlevel average wages into a component reflecting skill composition and a component reflecting wage premia. Our estimating procedure allows for changes in the return to ability and feedback from current idiosyncratic shocks to future mobility. We then use the peso devaluation of late 1994, which we argue generated an exogenous differential inducement to export within industries, to estimate the effect of export incentives on the two components. Comparing across plants within industries, we find that approximately twothirds of the higher level of wages in larger, more productive plants is explained by higher levels of wage premia, and that nearly all of the differential withinindustry wage change due to the export shock is explained by changes in wage premia. The findings argue against the hypothesis that sorting on individual ability is solely responsible for the welldocumented correlation between exporting and wages.
2007), “Panel Data Models with Multiple TimeVarying Individual Effects
 Journal of Productivity Analysis
"... This paper considers a panel data model with timevarying individual effects. The data are assumed to contain a large number of crosssectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixedeffects model in that the effects are assumed to be corr ..."
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Cited by 26 (2 self)
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This paper considers a panel data model with timevarying individual effects. The data are assumed to contain a large number of crosssectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixedeffects model in that the effects are assumed to be correlated with the regressors. The unobservable individual effects are assumed to have a factor structure. For consistent estimation of the model, it is important to estimate the true number of factors. We propose a generalized methods of moments procedure by which both the number of factors and the regression coefficients can be consistently estimated. Some important identification issues are also discussed. Our simulation results indicate that the proposed methods produce reliable estimates.
Identifying Distributional Characteristics in Random Coefficients Panel Data Models
, 2009
"... We study the identification of panel models with linear individualspecific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a tradeoff between heterogeneity a ..."
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Cited by 19 (3 self)
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We study the identification of panel models with linear individualspecific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a tradeoff between heterogeneity and error dynamics. We show identification of the density of individual effects when errors follow an ARMA process under conditional independence. We discuss GMM estimation of moments of effects and errors, and introduce a simple density estimator of a slope effect in a special case. As an application we estimate the effect that a mother smokes during pregnancy on child’s birth weight.
Eigenvalue Ratio Test for the Number of Factors’. Econometrica, forthcoming
, 2012
"... This paper proposes two new estimators for determining the number of factors in approximate factor models. We exploit the well known fact that the r eigenvalues of the variancecovariance matrix of N response variables, where r is the number of comment factors in the variables, grow unboundedly as N ..."
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Cited by 18 (1 self)
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This paper proposes two new estimators for determining the number of factors in approximate factor models. We exploit the well known fact that the r eigenvalues of the variancecovariance matrix of N response variables, where r is the number of comment factors in the variables, grow unboundedly as N increases. The criterion functions used for the two estimators are related to the ratio of two adjacent eigenvalues. An important advantage of the estimators is that they do not require the use of penalty functions. The estimators can be viewed as a reformulation of the well known scree test. We show that the estimators are consistent under the general conditions of Bai and Ng (2002). Our simulation results show that the estimators have good finite sample properties unless the signaltonoiseratio of each factor is too low. They perform much better than the BaiNg estimators do when either the number of the response variables analyzed or the number of time series observations, T, is small.
2014b): “Unemployment Benefits and Unemployment in the Great Recession: The Role of Micro Effects,” mimeo
"... We exploit a policy discontinuity at U.S. state borders to identify the labor market implications of unemployment benefit extensions. In contrast to the existing literature that focused on estimating the effects of benefit duration on job search decisions by the unemployed – the micro effect – we ar ..."
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Cited by 18 (2 self)
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We exploit a policy discontinuity at U.S. state borders to identify the labor market implications of unemployment benefit extensions. In contrast to the existing literature that focused on estimating the effects of benefit duration on job search decisions by the unemployed – the micro effect – we are guided by equilibrium labor market theory and focus on measuring the general equilibrium macro effect that operates through the response of job creation to benefit extensions. After developing a new methodology to measure the macro effect, we find that it is this effect that is very important quantitatively. In particular, benefit extensions raise equilibrium wages and lead to a sharp contraction in vacancy creation, employment, and a rise in unemployment.
Linear regression for panel with unknown number of factors as interactive fixed effects. manuscript
, 2011
"... Linear regression for panel with unknown number of factors as interactive fixed effects ..."
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Cited by 17 (1 self)
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Linear regression for panel with unknown number of factors as interactive fixed effects
A new panel data treatment for heterogeneity in time trends. Econometric Theory 28
, 2012
"... Abstract Our paper introduces a new estimation method for arbitrary temporal heterogeneity in panel data models. The paper provides a semiparametric method for estimating general patterns of crosssectional specific time trends. The methods proposed in the paper are related to principal component a ..."
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Cited by 15 (2 self)
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Abstract Our paper introduces a new estimation method for arbitrary temporal heterogeneity in panel data models. The paper provides a semiparametric method for estimating general patterns of crosssectional specific time trends. The methods proposed in the paper are related to principal component analysis and estimate the timevarying trend effects using a small number of common functions calculated from the data. An important application for the new estimator is in the estimation of timevarying technical efficiency considered in the stochastic frontier literature. Finite sample performance of the estimators is examined via Monte Carlo simulations. We apply our methods to the analysis of productivity trends in the U.S. banking industry. * Earlier versions of this paper under the title "On Estimating the Mixed Effects Model" were presented at North