Estimation Of Panel Data Models Research Articles

Convergence rate of estimators of clustered panel models with misclassification

We study kmeans clustering estimation of panel data models with a latent group structure and N units and T time periods under long panel asymptotics. We show that the group-specific coefficients can be estimated at the parametric NT-rate even if error variances diverge as T→∞ and consequently some units are asymptotically misclassified. This limit case approximates empirically relevant settings and is not covered by existing asymptotic results.

The impact of institutions on economic growth in selected developing countries: An analysis based on Bayesian panel estimation

This paper presents an empirical analysis of the impact of institutions on the economic growth of 27 developing countries during the period 1990-2014. Many creative models of panel data allow variations in slope coefficients both across time and cross-sectional units. All models were established in a Bayesian structure and their performance was tested by using an interesting application of the effect of institution on GDP. Technical details of all these models are given and tools are presented to compare their performance in the Bayesian system. Besides, panel data models and posterior model pools are provided for an insight into the institution's relationship with economic development. The derivation of Bayesian panel data models is included. The previous data has been used in this study and normal gamma prior is used for the models of panel data. 2SLS estimation technique has been used to analyze the classical estimation of panel data models. In the paper, developing countries were viewed as a whole. The study's evaluated results have shown that panel data models are valid Bayesian methodology models. In the Bayesian approach, the results of all independent variables affect the dependent variable significantly and positively. Based on all model standard defects, it is necessary to say that the Fixed Effect Model is the best in Bayesian panel data estimation methods. It was also shown that in comparison to other models, the fixed-effect model has the lowest standard error value.

Recursive estimation in large panel data models: Theory and practice

Bai (2009) proposes recursive estimation for panel data models with interactive effects. We study the behaviours of this recursive estimator. The recursive formula is established that shows the behaviours of recursive estimators depend on the initial estimator, the population structure and the iterative steps. Under some general scenarios, we find that the recursive estimator becomes consistent after the first iteration from any initials. We also obtain the optimal number of iterative steps under some prescribed conditions. The central limit theorem of the recursive estimator is established when the initial estimator is OLS. Various simulations are conducted to support our theoretical findings.

Estimating heterogeneous reactions to experimental treatments

Frequently in experiments there is not only variance in the reaction of participants to treatment. The heterogeneity is patterned: discernible types of participants react differently. In principle, a finite mixture model is well suited to simultaneously estimate the probability that a given participant belongs to a certain type, and the reaction of this type to treatment. Yet finite mixture models may need more data than the experiment provides. The approach in principle requires ex ante knowledge about the number of types. Finite mixture models make distributional assumptions that one may not feel comfortable with. They are hard to estimate for panel data, which is what experiments often generate. For experiments with repeated measurements, this paper offers a simple two-step alternative that is much less data hungry, that allows to find the number of types in the data, that does not make distributional assumptions about the type space, and that allows for the estimation of panel data models. It combines machine learning methods with classic frequentist statistics.

Capital regulation and ownership structure on bank risk

Purpose This paper aims to examine the impact of capital regulation, ownership structure and the degree of ownership concentration on the risk of commercial banks. Design/methodology/approach This study uses a sample of 565 commercial banks from 52 countries over the period of 2011-2015. A dynamic panel data model estimation using the maximum likelihood with structural equation modelling (SEM) was followed considering the panel nature of this study. Findings The study found that the increase of capital ratio decreases bank risk and the regulatory pressure increases the risk-taking of the bank. No statistically significant relationship between banks’ ownership structure and risk-taking was found. The concentration of ownership was found negatively associated with bank risk. Finally, the study found that in the long term, bank increases the capital level that decreases the default risk. Originality/value This study presents an empirical analysis on the global banking system focusing on the Basel Committee member and non-member countries that reflect the implementation of Basel II and Basel III. Therefore, it helps fill the gap in the banking literature on the effect of recent changes in the capital regulation on bank risk. Maximum likelihood with SEM addresses the issue of endogeneity, efficiency and time-invariant variables. Moreover, this study measures the risk by different proxy variables that address total, default and liquidity risks of the banks. Examining from a different perspective of risk makes the study more robust.

Modeling effects of banking regulations and supervisory practices on capital adequacy state transition in developing countries

Purpose The FDIC Improvement Act of 1991 sets out five categories of capital and mandates corrective action for banks. Each bank based on its capital amount fall in the certain categories or states. The purpose of this paper is to consider the effect of banking regulations and supervisory practices on capital state transition. Design/methodology/approach First, the authors investigate how much the practices influence banks' capital adequacy using a dynamic panel data method, the generalized method of moments. Then, to scrutinize the results of the first phase, the authors estimate the effect of practices on some characteristics of capital state transition such as transition intensity, transition probability and state sojourn time using multi-state models for panel data in 107 developing countries over the period 2000 to 2012. Findings The dynamic regression results show that capital guidelines, supervisory power and supervisory structure can have significantly positive effects on the capital adequacy state. Moreover, the multi-state Markov panel data model estimation results show that the significantly positive-effect practices can change the capital state transition intensity considerably; for example, they can transmit the critical-under-capitalized (the lowest) capital state of banks directly to a well or the adequate-capitalized (the highest) capital state without passing through middle states (under-capitalized and significantly-undercapitalized). Moreover, the results present some new evidence on transition probability and state sojourn time. Originality/value The main contribution of this paper, unlike the existing literature, is to consider the power of banking regulations and supervisory practices to improve the capital state using a multi-state Markov panel data model.

Mexican states' business cycles co‐movement over the period 2000–2014. A panel data model estimation

AbstractThis paper examines the business cycle co‐movement in Mexican states over the period 2000–2014 by estimating an extended gravitational panel data model. Two different de‐trending filters are used to check the robustness of our results. The estimates suggest that the co‐movement increases as the size of the states’ economies does so as well as with the productive structure similarities and the relative level of development; however, the co‐movement decreases at a diminishing rate with geographical distance. There is also evidence of time‐dependent effects. In addition, the existence of moderate co‐movements among the states’ cycles suggests that common economic policies may not be suitable for all states, which implies there is a need for specific countercyclical policies to mitigate idiosyncratic shocks.

Double filter instrumental variable estimation of panel data models with weakly exogenous variables

In this article, we propose instrumental variables (IV) and generalized method of moments (GMM) estimators for panel data models with weakly exogenous variables. The model is allowed to include heterogeneous time trends besides the standard fixed effects (FE). The proposed IV and GMM estimators are obtained by applying a forward filter to the model and a backward filter to the instruments in order to remove FE, thereby called the double filter IV and GMM estimators. We derive the asymptotic properties of the proposed estimators under fixed T and large N, and large T and large N asymptotics where N and T denote the dimensions of cross section and time series, respectively. It is shown that the proposed IV estimator has the same asymptotic distribution as the bias corrected FE estimator when both N and T are large. Monte Carlo simulation results reveal that the proposed estimator performs well in finite samples and outperforms the conventional IV/GMM estimators using instruments in levels in many cases.

Panel data model estimation: the effect of managerial ownership, capital structure, and company size on corporate value

Previous studies have been found that managerial ownership, capital structure, and firm size affect the firm’s value. This research uses panel data regression method. By using the panel data of Property and Real Estate firms listed on the Indonesia Stock Exchange, there are 117 observations that combine cross-section data of 39 companies with time-series data during the period 2014 to 2016. From the model selection results, it was found that the fixed effect model is the best model compared with common effect model and random effect model. By applying the best model base, the researchers found an evidence to suggest that the managerial ownership significantly positively affects firm’s value. The results also showed that firm size significantly positively affects firm’s value. However, the empirical results do not show evidence that capital structure significantly affects the firm’s value. The results of this study are consistent with the stated literature that managerial ownership and firm size are the determinants of firm’s value. These factors must be considered by the owners of capital and managers in maximizing the value of the company.

Estimating Heterogeneous Reactions to Experimental Treatments

Frequently in experiments there is not only variance in the reaction of participants to treatment. The heterogeneity is patterned: discernible types of participants react differently. In principle, a finite mixture model is well suited to simultaneously estimate the probability that a given participant belongs to a certain type, and the reaction of this type to treatment. Yet often, finite mixture models need more data than the experiment provides. The approach requires ex ante knowledge about the number of types. Finite mixture models are hard to estimate for panel data, which is what experiments often generate. For repeated experiments, this paper offers a simple two-step alternative that is much less data hungry, that allows to find the number of types in the data, and that allows for the estimation of panel data models. It combines machine learning methods with classic frequentist statistics.

A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment

This paper introduces a quantile regression estimator for panel data models with individual heterogeneity and attrition. The method is motivated by the fact that attrition bias is often encountered in Big Data applications. For example, many users sign-up for the latest program but few remain active users several months later, making the evaluation of such interventions inherently very challenging. Building on earlier work by Hausman and Wise (1979), we provide a simple identification strategy that leads to a two-step estimation procedure. In the first step, the coefficients of interest in the selection equation are consistently estimated using parametric or nonparametric methods. In the second step, standard panel quantile methods are employed on a subset of weighted observations. The estimator is computationally easy to implement in Big Data applications with a large number of subjects. We investigate the conditions under which the parameter estimator is asymptotically Gaussian and we carry out a series of Monte Carlo simulations to investigate the finite sample properties of the estimator. Lastly, using a simulation exercise, we apply the method to the evaluation of a recent Time-of-Day electricity pricing experiment inspired by the work of Aigner and Hausman (1980).

Nonparametric estimation of the marginal effect in fixed-effect panel data models

This paper considers multivariate local linear least squares estimation of panel data models when fixed effects are present. One-step estimation of the local marginal effect is of prime interest. A within-group nonparametric estimator is developed, where the fixed effects are eliminated by subtracting individual-specific locally weighted time average, i.e., the local-within-transformation. It is shown that the local-within-transformation-based estimator satisfies the standard properties of the local linear estimator. In comparison, nonparametric estimators based on the conventional (global) within-transformation or first difference result in estimators which are biased, even in large samples. The new estimator is used to examine the nonlinear relationship between income and nitrogen-oxide level (i.e., the environmental Kuznets curve) based on US state-level panel data.

Fixed Effects Estimation of Large-TPanel Data Models

This article reviews recent advances in fixed effects estimation of panel data models for long panels, where the number of time periods is relatively large. We focus on semiparametric models with unobserved individual and time effects, where the distribution of the outcome variable, conditional on covariates and unobserved effects, is specified parametrically while the distribution of the unobserved effects is left unrestricted. In contrast to existing reviews on long panels, we discuss models with both individual and time effects, split-panel jackknife bias corrections, unbalanced panels, distribution and quantile effects, and other extensions. Understanding and correcting the incidental parameter bias caused by the estimation of many fixed effects are our main focuses, and the unifying theme is that the order of this bias is given by the simple formula p/ n for all models discussed, with p being the number of estimated parameters and n the total sample size.

Alternative over-identifying restriction test in the GMM estimation of panel data models

A new over-identifying restriction test in the generalized method of moments (GMM) estimation of panel data models is proposed. In contrast to the conventional over-identifying restriction test, where the sample covariance matrix of the moment conditions is used in the weighting matrix, the proposed test uses a block diagonal weighting matrix constructed from the efficient optimal weighting matrix. It is shown that the proposed test statistic asymptotically follows the weighted sum of the chi-square distribution with one degree of freedom. A detailed local power analysis is provided for dynamic panel data models, and it is demonstrated that the new test has a comparable power to the conventional J test in many cases. The Monte Carlo simulations reveal that the proposed test has a substantially better size property than the conventional test does.

Simultaneous Generalized Method of Moments Estimator for Panel Data Models with Spatially Correlated Error Components

This paper develops estimators for simultaneous equations with spatial autoregressive or spatial moving average error components. We derive a limited information estimator and a full information estimator. We give the simultaneous generalized method of moments to get each component of the variance covariance of the disturbance in spatial autoregressive case as well as spatial moving average case. The results of our Monte Carlo suggest that our estimators are consistent. When we estimate the coefficient of spatial dependence it seems better to use instrumental variables estimator that takes into account simultaneity. We also apply these set of estimators on real data.

Efficiency Comparisons of Different Estimators for Panel Data Models with Serially Correlated Errors: A Stochastic Parameter Regression Approach

This paper considers panel data models when the errors are first-order serially correlated as well as with stochastic regression parameters. The generalized least squares (GLS) estimators for these models have been derived and examined in this paper. Moreover, an alternative estimator for GLS estimators in small samples has been proposed, this estimator is called simple mean group (SMG). The efficiency comparisons for GLS and SMG estimators have been carried out. The Monte Carlo studies indicate that SMG estimator is more reliable in most situations than the GLS estimators, especially when the model includes one or more non-stochastic parameter.

Impact of CEO’s characteristics on investment decisions of Indian listed firms: Does crisis make any difference?

Using a sample of listed Indian manufacturing companies, this study examines the role of chief executive officer’s (CEO’s) personal characteristics like age, tenure, education, and career experience in the determination of investment decisions of the firm. The dynamic panel data model estimation, more specifically the system generalized method of moments estimation results reveal a negative relation between CEO’s age and corporate investment. CEO’s financial education is positively associated with investment decisions. The investment cash flow sensitivity analysis posits that CEO’s age and financial education reduce the sensitivity of investment with respect to cash flow. The results are robust across different periods, defined on the basis of crises. In times of financial crisis, we document that firm’s liquidity and age, CEO’s career experience and tenure turn out to be significant determinants of corporate investment. This paper provides an out-of-sample evidence of the role of CEO’s personal characteristics on the determination of corporate investment, which is an unexplored issue from an emerging market perspective.

Robust estimation and empirical likelihood inference with exponential squared loss for panel data models

This paper introduces a robust estimation for panel data models using the exponential squared loss function. We propose the method by constructing the robust empirical likelihood ratio function. The Monte Carlo simulations show that the proposed estimator is robust in the fixed and random effects models.

Inferences in panel data with interactive effects using large covariance matrices

We consider efficient estimation of panel data models with interactive effects, which relies on a high-dimensional inverse covariance matrix estimator. By using a consistent estimator of the error covariance matrix, we can take into account both cross-sectional correlations and heteroskedasticity. In the presence of cross-sectional correlations, the proposed estimator eliminates the cross-sectional correlation bias, and is more efficient than the existing methods. The rate of convergence is also improved. In addition, we find that when the statistical inference involves estimating a high-dimensional inverse covariance matrix, the minimax convergence rate on large covariance estimations is not sufficient for inferences. To address this issue, a new “doubly weighted convergence” result is developed. The proposed method is applied to the US divorce rate data. We find that our more efficient estimator identifies the significant effects of divorce-law reforms on the divorce rate, and provides tighter confidence intervals than existing methods. This provides a confirmation for the empirical findings of Wolfers (2006) under more general unobserved heterogeneity.

Double Filter Instrumental Variable Estimation of Panel Data Models with Weakly Exogenous Variables

In this paper, we propose instrumental variables (IV) and generalized method of moments (GMM) estimators for panel data models with weakly exogenous variables. The model is allowed to include heterogeneous time trends besides the standard fixed effects. The proposed IV and GMM estimators are obtained by applying a forward filter to the model and a backward filter to the instruments in order to remove fixed effects, thereby called the double filter IV and GMM estimators. We derive the asymptotic properties of the proposed estimators under fixed T and large N, and large T and large N asymptotics where N and T denote the dimensions of cross section and time series, respectively. It is shown that the proposed IV estimator has the same asymptotic distribution as the bias corrected fixed effects estimator when both N and T are large. Monte Carlo simulation results reveal that the proposed estimator performs well in finite samples and outperforms the conventional IV/GMM estimators using instruments in levels in many cases.