Linear Dynamic Panel Research Articles

The limited information maximum likelihood approach to dynamic panel structural equation models

We develop the panel-limited information maximum likelihood approach for estimating dynamic panel structural equation models. When there are dynamic effects and endogenous variables with individual effects at the same time, the LIML method for the filtered data does give not only a consistent estimator and asymptotic normality, but also attains the asymptotic bound when the number of orthogonal conditions is large. Our formulation includes Alvarez and Arellano (Econometrica 71:1121–1159, 2003), Blundell and Bond (Econ Rev 19-3:321–340, 2000) and other linear dynamic panel models as special cases.

Testing a linear dynamic panel data model against nonlinear alternatives

The most popular econometric models in the panel data literature are the class of linear panel data models with unobserved individual- and/or time-specific effects. The consistency of parameter estimators and the validity of their economic interpretations as marginal effects depend crucially on the correct functional form specification of the linear panel data model. In this paper, a new class of residual-based tests is proposed for checking the validity of dynamic panel data models with both large cross-sectional units and time series dimensions. The individual and time effects can be fixed or random, and panel data can be balanced or unbalanced. The tests can detect a wide range of model misspecifications in the conditional mean of a dynamic panel data model, including functional form and lag misspecification. They check a large number of lags so that they can capture misspecification at any lag order asymptotically. No common alternative is assumed, thus allowing for heterogeneity in the degrees and directions of functional form misspecification across individuals. Thanks to the use of panel data with large N and T, the proposed nonparametric tests have an asymptotic normal distribution under the null hypothesis without requiring the smoothing parameters to grow with the sample sizes. This suggests better nonparametric asymptotic approximation for the panel data than for time series or cross sectional data. This is confirmed in a simulation study. We apply the new tests to test linear specification of cross-country growth equations and found significant nonlinearities in mean for OECD countries’ growth equation for annual and quintannual panel data.

Political, Institutional, and Economic Factors Underlying Deficit Volatility

AbstractIt is well known that fiscal policy can counter‐cyclically smooth out the effect of unexpected shocks and public deficit volatility may reflect the (optimal) policy response to them. However, the welfare losses associated to fiscal instability are also an important challenge for many countries, as it typically implies an inefficient allocation of resources, higher sovereign risk premium and an inadequate provision of public services. In this paper, we empirically analyze the political, institutional, and economic sources of public deficit volatility. Using the system‐generalized method‐of‐moments (GMM) estimator for linear dynamic panel data models and a sample of 125 countries analyzed from 1980 to 2006, we show that higher public deficit volatility is typically associated with higher levels of political instability and less democracy. In addition, public deficit volatility tends to be magnified for small countries, in the outcome of hyper‐inflation episodes and for countries with a high degree of openness.

Robust estimation of dynamic fixed-effects panel data models

This paper extends an existing outlier-robust estimator of linear dynamic panel data models with fixed effects, which is based on the median ratio of two consecutive pairs of first-order differenced data. To improve its precision and robustness properties, a general procedure based on higher-order pairwise differences and their ratios is designed. The asymptotic distribution of this class of estimators is derived. Further, the breakdown point properties are obtained under contamination by independent additive outliers and by the patches of additive outliers, and are used to select the pairwise differences that do not compromise the robustness properties of the procedure. The proposed estimator is additionally compared with existing methods by means of Monte Carlo simulations.

Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects

In this article, we consider semiparametric estimation in a partially linear single-index panel data model with fixed effects. Without taking the difference explicitly, we propose using a semiparametric minimum average variance estimation (SMAVE) based on a dummy variable method to remove the fixed effects and obtain consistent estimators for both the parameters and the unknown link function. As both the cross-section size and the time series length tend to infinity, we not only establish an asymptotically normal distribution for the estimators of the parameters in the single index and the linear component of the model, but also obtain an asymptotically normal distribution for the nonparametric local linear estimator of the unknown link function. The asymptotically normal distributions of the proposed estimators are similar to those obtained in the random effects case. In addition, we study several partially linear single-index dynamic panel data models. The methods and results are augmented by simulation studies and illustrated by application to two real data examples. This article has online supplementary materials.

Dynamic Panels, Cross Sectional Correlation, and Arbitrage in Equities Market

This paper studies estimation in linear dynamic panel data models with multiple interactive effects when both N and T are large. We derive the bias term in an order p dynamic panel data model and the limiting distribution of the estimator. Simulation results show good finite sample properties of the estimator. An application to arbitrage in the U.S. equities market between 2000 and 2011 shows dynamic panel data model is an effective method to produce trading signals and generate profit and loss.

How does political instability affect economic growth?

The purpose of this paper is to empirically determine the effects of political instability on economic growth. By using the system-GMM estimator for linear dynamic panel data models on a sample covering up to 169 countries, and 5-year periods from 1960 to 2004, we find that higher degrees of political instability are associated with lower growth rates of GDP per capita. Regarding the channels of transmission, we find that political instability adversely affects growth by lowering the rates of productivity growth and, to a smaller degree, physical and human capital accumulation. Finally, economic freedom and ethnic homogeneity are beneficial to growth, while democracy may have a small negative effect.

Analysis of interactive fixed effects dynamic linear panel regression with measurement error

This paper studies a simple dynamic linear panel regression model with interactive fixed effects in which the variable of interest is measured with error. To estimate the dynamic coefficient, we consider the least-squares minimum distance (LS-MD) estimation method.

Detection of structural breaks in linear dynamic panel data models

A break detection testing procedure for the well-known AR(p) linear panel data model with exogenous or pre-determined regressors is developed. The proposed method can accommodate a structural break in the slope parameters as well as in the fixed effects. Breaks in the latter are not constrained by any type of cross-sectional homogeneity and are allowed to be correlated with all past information. Monte Carlo simulations indicate that the test performs satisfactorily even in the type of panel datasets with short time-dimension often encountered in practice. As an empirical illustration, the paper implements the test to detect the effects of the 1997 Asian crisis on the investment decisions of Asian companies.

Leadership Succession and Organizational Performance: Football Coaches and Organizational Issues

If the team changes the coach, does the team’s performance change? From the literature on leadership succession and organizational performance, three perspectives have emerged that seek to answer this question: common sense, vicious cycle, and ritual scapegoat. We extend these leadership perspectives by drawing on organizational theory to explain leadership succession and organizational performance in National Collegiate Athletic Association Division Football Bowl Subdivision football. We develop a model and use the Arellano and Bond (1991) linear dynamic panel data estimator to examine this relationship from the 1950–1951 season to the 2008–2009 season. Our results show that organizational performance decreases initially following a leadership change. However, as a coach’s tenure increases at the university, organizational performance improves. This offers some support for vicious cycle theory and suggests that sport managers should do a better job of managing performance expectations following a coaching change as our results show that coaching changes lead to a drop in performance.

A note on transformed likelihood approach in linear dynamic panel models

The transformed likelihood approach to estimation of fixed effects dynamic panel data models is shown to present very good inferential properties but it is not directly implemented in the most diffused statistical software. The present paper aims at showing how a simple model reformulation can be adopted to describe the problem in terms of classical linear mixed models. The transformed likelihood approach is based on the first differences data transformation, the following results derive from a convenient reformulation in terms of deviations from the first observations. Given the invariance to data transformation, the likelihood functions defined in the two cases coincide. Resulting in a classical random effect linear model form, the proposed approach significantly improves the number of available estimation procedures and provides a straightforward interpretation for the parameters. Moreover, the proposed model specification allows to consider all the estimation improvements typical of the random effects model literature. Simulation studies are conducted in order to study the robustness of the estimation method to mean stationarity violation.

How Does Political Instability Affect Economic Growth?

The purpose of this paper is to empirically determine the effects of political instability on economic growth. Using the system-GMM estimator for linear dynamic panel data models on a sample covering up to 169 countries, and 5-year periods from 1960 to 2004, we find that higher degrees of political instability are associated with lower growth rates of GDP per capita. Regarding the channels of transmission, we find that political instability adversely affects growth by lowering the rates of productivity growth and, to a smaller degree, physical and human capital accumulation. Finally, economic freedom and ethnic homogeneity are beneficial to growth, while democracy may have a small negative effect.

How Does Political Instability Affect Economic Growth?

The purpose of this paper is to empirically determine the effects of political instability on economic growth. Using the system-GMM estimator for linear dynamic panel data models on a sample covering up to 169 countries, and 5-year periods from 1960 to 2004, we find that higher degrees of political instability are associated with lower growth rates of GDP per capita. Regarding the channels of transmission, we find that political instability adversely affects growth by lowering the rates of productivity growth and, to a smaller degree, physical and human capital accumulation. Finally, economic freedom and ethnic homogeneity are beneficial to growth, while democracy may have a small negative effect.

THE ROBUSTNESS OF ESTIMATORS FOR DYNAMIC PANEL DATA MODELS TO MISSPECIFICATION

Transition from economic theory to a testable form of model invariably involves the use of certain "simplifying assumptions." If, however, these are not valid, misspecified models result. This article considers estimation of the dynamic linear panel data model, which often forms the basis of testable economic hypotheses. The estimators of such a model are frequently similarly based on certain assumptions which appear to be often untenable in practice. Here, the performance of these estimators is analyzed in scenarios where the theoretically required conditions are not met. Specifically, we consider three such instances of serial correlation of the idiosyncratic disturbance terms; correlation of the idiosyncratic disturbance terms and explanatory variables; and, finally, cross-sectional dependence (as a robustness check to these findings, we also consider correlations between observed and unobserved heterogeneity terms). The major findings are that the limited tests readily available tend to have poor power properties and that estimators' performance varies greatly across scenarios. In such a wide array of experiments, it is difficult to pick-out just one "winner." However, a robust estimator across all experiments and parameter settings was a variant of the Wansbeek–Bekker estimator. This is a significant finding, as this estimator is infrequently used in practice. When the experiments are extended to include correlations between observed and unobserved heterogeneity terms, one might also consider, for across-the-board performance, the Blundell and Bond estimator.

On the impact of error cross-sectional dependence in short dynamic panel estimation

This paper explores the impact of error cross‐sectional dependence (modelled as a factor structure) on a number of widely used IV and generalized method of moments (GMM) estimators in the context of a linear dynamic panel data model. It is shown that, under such circumstances, the standard moment conditions used by these estimators are invalid – a result that holds for any lag length of the instruments used. Transforming the data in terms of deviations from time‐specific averages helps to reduce the asymptotic bias of the estimators, unless the factor loadings have mean zero. The finite sample behaviour of IV and GMM estimators is investigated by means of Monte Carlo experiments. The results suggest that the bias of these estimators can be severe to the extent that the standard fixed effects estimator is not generally inferior anymore in terms of root median square error. Time‐specific demeaning alleviates the problem, although the effectiveness of this transformation decreases when the variance of the factor loadings is large.

A test of cross section dependence for a linear dynamic panel model with regressors

This paper proposes a new testing procedure for detecting error cross section dependence after estimating a linear dynamic panel data model with regressors using the generalised method of moments (GMM). The test is valid when the cross-sectional dimension of the panel is large relative to the time series dimension. Importantly, our approach allows one to examine whether any error cross section dependence remains after including time dummies (or after transforming the data in terms of deviations from time-specific averages), which will be the case under heterogeneous error cross section dependence. Finite sample simulation-based results suggest that our tests perform well, particularly the version based on the [Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143] system GMM estimator. In addition, it is shown that the system GMM estimator, based only on partial instruments consisting of the regressors, can be a reliable alternative to the standard GMM estimators under heterogeneous error cross section dependence. The proposed tests are applied to employment equations using UK firm data and the results show little evidence of heterogeneous error cross section dependence.

Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model

This paper considers Maximum Likelihood (ML) based estimation and inference procedures for linear dynamic panel data models with fixed effects.

Venture Capital and Innovation in Europe

This paper examines the direction of causality between Venture Capital (VC) and innovation (proxied by patents) in Europe. We test whether causality runs from patents to VC by estimating a linear dynamic panel model and causality from VC to patents by estimating a panel count model. Evidence from a European sample indicates that causality runs from patents to VC suggesting that, in Europe, innovation seems to create a demand for VC and not VC a supply of innovation. In this sense, innovative ideas seem to lack more than funds in Europe.

Political instability and inflation volatility

The purpose of this paper is to empirically analyze the effects of political instability, social polarization and the quality of institutions on inflation volatility over time and across countries. Using the system-GMM estimator for linear dynamic panel data models on a sample covering 160 countries, analyzed in the period from 1960 to 1999, this paper finds that higher degrees of political instability and social polarization, less democracy, and lower de facto central bank independence are associated with more volatile inflation rates. Furthermore, political instability has greater effects on inflation volatility in developing countries with lower degrees of central bank independence and economic freedom.

Nonparametric Estimation of Dynamic Panel Models with Fixed Effects

This paper considers nonparametric estimation of autoregressive panel models with fixed effects. A within-group type series estimator is developed and its convergence rate and the asymptotic normality are derived. It is found that the series estimator is asymptotically biased and the bias reduces the mean-square convergence rate compared with the cross section cases. A bias corrected nonparametric estimator is developed. As an extension, partially linear dynamic panel models are also studied.