“Panelizing” Repeated Cross Sections

This paper considers the implementation of a non-stationary, heterogeneous Markov model for the analysis of binary dependent variables in a time series of repeated cross-sectional (RCS) surveys. The model offers the opportunity to estimate entry and exit transition probabilities and to examine the effects of time-constant and time-varying covariates on the hazards. We show how maximum likelihood estimates of the parameters can be obtained by Fishers method-of-scoring and how to estimate both fixed and time-varying covariate effects. The model is exemplified with an analysis of the labor force participation decision of Dutch and West German women using ISSP (and other) data from 10 annual Dutch surveys conducted between 1987 and 1996 and 7 annual West German surveys conducted between 1988 and 1994. Some open problems concerning the application of the model are discussed.     

Does Service Bundling Reduce Churn?

We examine whether bundling in telecommunications services reduces churn using a series of large, independent cross sections of household decisions. To identify the effect of bundling, we construct a pseudo‐panel dataset and utilize a linear, dynamic panel‐data model, supplemented by nearest‐neighbor matching. We find bundling does reduce churn for all three “triple‐play” services. The effect is only “visible” during times of turbulent demand. We also find evidence that broadband was substituting for pay television in 2009. This analysis highlights that bundling helps with customer retention in service industries, and may play an important role in preserving contracting markets.     

Firm efficiency: Domestic owners,coalitions, and FDI

In this paper we analyze the evolution of firm efficiency in the Czech Republic. Using a large panel of more than 190,000 Czech firm/years we study whether firms fully utilize their resources, how firm efficiency evolves over time, and how firm efficiency is determined by ownership structure. We employ a panel version of a stochastic production frontier model for the period 1996–2007 with time-varying efficiency. We differentiate among various degrees of ownership concentration and domestic or foreign origin. In a two-stage set-up we first estimate the degree of firm inefficiency and then the effect of ownership structure on the distance from the efficiency frontier. Our results support the hypothesis that concentrated ownership is positively related to efficiency. FDI has beneficial effects at the microeconomic level. However, we show that a simple majority is not necessarily the best structure to improve efficiency. We further analyze the effects of ownership coalitions and shed light on many other subtleties of how ownership and the specific industry affect firm efficiency.     

How well do urban growth boundaries contain development? Results for Oregon using a difference-in-difference estimator

Urban containment policies, including urban growth boundaries (UGBs), are a common tool used by city planners to promote compact development. We analyze how well UGBs do in containing development using fine-scale GIS data on cities in Oregon. Earlier studies on UGBs yield mixed results, with some authors finding no effects of UGBs on housing market variables and urbanization rates and others finding significant effects. A challenge in measuring these effects is that the location of the UGB is unlikely to be an exogenous determinant of a land parcel's value for development. The panel structure of our dataset allows us to estimate the UGB's effect on the probability of development using a difference-in-difference estimator applied to a narrow band of plots along each city's UGB. This estimator controls for time-invariant unobservable variables and common temporal effects among plots, thereby mitigating the potential for biased estimates due to the endogeneity of the UGB's location. We also pursue a novel approach to controlling for time-varying factors that exploits our fine-scale data. We find that UGBs contain development in many of the Oregon cities we examine, although there are some cities in which development rates are the same inside and outside of the UGB. Our results reveal that, in most cities, the effect of the UGB is small relative to pre-existing differences in development probabilities. This suggests that it may be difficult to identify UGB effects with cross-sectional data, the approach commonly taken in previous studies.     

Are regional trading agreements beneficial?: Static and dynamic panel gravity models

This paper evaluates the effects of preferential agreements on trade between trade group members and non-members using a static and a dynamic gravity model. The gravity model is estimated using recent panel data techniques which account for the endogeneity of the integration effects and the existence of dynamic effects. We incorporate into the static model the time-varying, multilateral resistance terms to obtain unbiased estimates. In addition, we estimate a dynamic version of the gravity model using a number of GMM estimators. The results show that dynamics are significant and robust and that the new wave of regionalism in the 1990s has had larger positive effects on intra and extra-bloc trade on developed countries (EU and NAFTA) than on developing countries.     

Technical efficiency in competing panel data models: a study of Norwegian grain farming

Estimation of technical efficiency is widely used in empirical research using both cross-sectional and panel data. Although several stochastic frontier models for panel data are available, only a few of them are normally applied in empirical research. In this article we chose a broad selection of such models based on different assumptions and specifications of heterogeneity, heteroskedasticity and technical inefficiency. We applied these models to a single dataset from Norwegian grain farmers for the period 2004–2008. We also introduced a new model that disentangles firm effects from persistent (time-invariant) and residual (time-varying) technical inefficiency. We found that efficiency results are quite sensitive to how inefficiency is modeled and interpreted. Consequently, we recommend that future empirical research should pay more attention to modeling and interpreting inefficiency as well as to the assumptions underlying each model when using panel data.     

Testing for time-invariant unobserved heterogeneity in generalized linear models for panel data

Recent literature on panel data emphasizes the importance of accounting for time-varying unobservable individual effects, which may stem from either omitted individual characteristics or macro-level shocks that affect each individual unit differently. In this paper, we propose a simple specification test of the null hypothesis that the individual effects are time-invariant against the alternative that they are time-varying. Our test is an application of Hausman (1978) testing procedure and can be used for any generalized linear model for panel data that admits a sufficient statistic for the individual effect. This is a wide class of models which includes the Gaussian linear model and a variety of nonlinear models typically employed for discrete or categorical outcomes. The basic idea of the test is to compare two alternative estimators of the model parameters based on two different formulations of the conditional maximum likelihood method. Our approach does not require assumptions on the distribution of unobserved heterogeneity, nor it requires the latter to be independent of the regressors in the model. We investigate the finite sample properties of the test through a set of Monte Carlo experiments. Our results show that the test performs well, with small size distortions and good power properties. We use a health economics example based on data from the Health and Retirement Study to illustrate the proposed test.     

Panel data models with multiple time-varying individual effects

This paper considers a panel data model with time-varying individual effects. The data are assumed to contain a large number of cross-sectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixed-effects 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 individual effects. We propose a generalized methods of moments procedure by which both the number of individual effects 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.     

Fitting dynamic factor models to non-stationary time series

Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infinite-length filtering of the factors via dynamic loadings. In this paper, motivated from economic data observed over long time periods which show smooth transitions over time in their covariance structure, we allow the dynamic structure of the factor model to be non-stationary over time by proposing a deterministic time variation of its loadings. In this respect we generalize the existing recent work on static factor models with time-varying loadings as well as the classical, i.e. stationary, dynamic approximate factor model. Motivated from the stationary case, we estimate the common components of our dynamic factor model by the eigenvectors of a consistent estimator of the now time-varying spectral density matrix of the underlying data-generating process. This can be seen as a time-varying principal components approach in the frequency domain. We derive consistency of this estimator in a “double-asymptotic” framework of both cross-section and time dimension tending to infinity. The performance of the estimators is illustrated by a simulation study and an application to a macroeconomic data set.     

Structural FECM: Cointegration in large‐scale structural FAVAR models

Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor‐augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross‐section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.     

Estimating systematic continuous-time trends in recidivism using a non-Gaussian panel data model

We model panel data of crime careers of juveniles from a Dutch Judicial Juvenile Institution. The data are decomposed into a systematic and an individual-specific component, of which the systematic component reflects the general time-varying conditions including the criminological climate. Within a model-based analysis, we treat (1) shared effects of each group with the same systematic conditions, (2) strongly non-Gaussian features of the individual time series, (3) unobserved common systematic conditions, (4) changing recidivism probabilities in continuous time and (5) missing observations. We adopt a non-Gaussian multivariate state-space model that deals with all these issues simultaneously. The parameters of the model are estimated by Monte Carlo maximum likelihood methods. This paper illustrates the methods empirically. We compare continuous time trends and standard discrete-time stochastic trend specifications. We find interesting common time variation in the recidivism behaviour of the juveniles during a period of 13 years, while taking account of significant heterogeneity determined by personality characteristics and initial crime records.     

Estimation of average treatment effects using panel data when treatment effect heterogeneity depends on unobserved fixed effects

This paper proposes a new panel data approach to identify and estimate the time-varying average treatment effect (ATE). The approach allows for treatment effect heterogeneity that depends on unobserved fixed effects. In the presence of this type of heterogeneity, existing panel data approaches identify the ATE for limited subpopulations only. In contrast, the proposed approach identifies and estimates the ATE for the entire population. The approach relies on the linear fixed effects specification of potential outcome equations and uses exogenous variables that are correlated with the fixed effects. I apply the approach to study the impact of a mother's smoking during pregnancy on her child's birth weight.     

A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies

This paper presents a metafrontier production function model for firms in different groups having different technologies. The metafrontier model enables the calculation of comparable technical efficiencies for firms operating under different technologies. The model also enables the technology gaps to be estimated for firms under different technologies relative to the potential technology available to the industry as a whole. The metafrontier model is applied in the analysis of panel data on garment firms in five different regions of Indonesia, assuming that the regional stochastic frontier production function models have technical inefficiency effects with the time-varying structure proposed by Battese and Coelli (1992).     

Nonparametric fixed effects model for panel data with locally stationary regressors

We develop methods for inference in nonparametric time-varying fixed effects panel data models that allow for locally stationary regressors and for the time series length $T$ and cross-section size $N$ both being large. We first develop a pooled nonparametric profile least squares dummy variable approach to estimate the nonparametric function, and establish the optimal convergence rate and asymptotic normality of the resultant estimator. We then propose a test statistic to check whether the bivariate nonparametric function is time-varying or the time effect is separable, and derive the asymptotic distribution of the proposed test statistic. We present several simulated examples and two real data analyses to illustrate the finite sample performance of the proposed methods.     

Fixed and Random Effects in Stochastic Frontier Models

Received stochastic frontier analyses with panel data have relied on traditional fixed and random effects models. We propose extensions that circumvent two shortcomings of these approaches. The conventional panel data estimators assume that technical or cost inefficiency is time invariant. Second, the fixed and random effects estimators force any time invariant cross unit heterogeneity into the same term that is being used to capture the inefficiency. Inefficiency measures in these models may be picking up heterogeneity in addition to or even instead of inefficiency. A fixed effects model is extended to the stochastic frontier model using results that specifically employ the nonlinear specification. The random effects model is reformulated as a special case of the random parameters model. The techniques are illustrated in applications to the U.S. banking industry and a cross country comparison of the efficiency of health care delivery.JEL classification: C1, C4     

Continuous time modeling of panel data: SEM versus filter techniques

Although convincing arguments have been put forward for continuous-time modeling, its use in panel research is rare. In one approach, classical N  = 1 state-space modeling procedures are adapted for panel analysis to estimate the exact discrete model (EDM) by means of filter techniques. Based on earlier less satisfactory indirect methods, a more recent approach uses structural equation modeling (SEM) to get the maximum likelihood estimate of the EDM by the direct method. After an introduction into continuous-time state-space modeling for panel data and the EDM, a thorough comparison is made between the two distinct approaches with quite different histories by means of Monte Carlo simulation studies. The model used in the simulation studies is the damped linear oscillator with and without random subject effects.     

A PANEL DATA APPROACH FOR PROGRAM EVALUATION: MEASURING THE BENEFITS OF POLITICAL AND ECONOMIC INTEGRATION OF HONG KONG WITH MAINLAND CHINA

We propose a simple‐to‐implement panel data method to evaluate the impacts of social policy. The basic idea is to exploit the dependence among cross‐sectional units to construct the counterfactuals. The cross‐sectional correlations are attributed to the presence of some (unobserved) common factors. However, instead of trying to estimate the unobserved factors, we propose to use observed data. We use a panel of 24 countries to evaluate the impact of political and economic integration of Hong Kong with mainland China. We find that the political integration hardly had any impact on the growth of the Hong Kong economy. However, the economic integration has raised Hong Kong's annual real GDP by about 4%. Copyright © 2011 John Wiley & Sons, Ltd.     

Durations in Panel Data Subject to Attrition: A Note on Estimation in the Case of a Stock Sample1

In the empirical analysis of unemployment durations or job durations, it is generally assumed that the stochastic processes underlying labour market behaviour and the behaviour concerning participation in a panel survey are independent. As a consequence, spells that are incomplete due to attrition can be treated as spells that are subjected to independent right censoring. However, if the assumption of independence is violated, i.e. if for example the probability of dropping out of the panel is related to the rate at which a job is found, then attrition may have to be modelled and estimated jointly with the unemployment duration distribution to avoid biased estimates of the rate at which individuals become employed. A way to model the joint dependence is by means of stochastically related unobserved determinants. We discuss some properties of these kinds of models and state conditions needed to estimate such models in the case of stock sampled duration data.     

面板数据分析中含两阶段自回归残差的单因素误差分量模型研究

在面板数据分析中残差自相关使得参数估计更加复杂。本文提出了含两阶段自回归残差的单因素误差分量模型,并推导了在这一模型中如何计算广义最小二乘估计量及其相关性质。     

Productive efficiency of French grain producers: A latent variable model

In this article we propose to implement a covariance structure analysis to deal with the estimation of a stochastic frontier production function on panel data and the measurement of a time-varying technical efficiency. First, this method solves the potential problem of correlations between input quantities and individual effects. Second, individual effects and efficiency measures can be recovered as a byproduct of the analysis through the so-called factor scores. We implement this approach by fitting to a balanced panel of French grain producers, a parsimonious version of the Cornwell, Schmidt, and Sickles [1990]'s model where technical efficiencies are individual-specific linear functions of time. A specification search shows that this model is preferred to the traditional production function. Results shed light on the temporal pattern of efficiency in the French grain production sector.The authors thank Jacques Mairesse, Quang Vuong, two referees, the editor, and session participants at the Econometric Society European Meeting, Cambridge, September 1991, at the Second European Workshop on Efficiency and Productivity Analysis, Louvain-la-Neuve, October 1991, at the Conference on Current Issues in Productivity, Newark, December 1991, and at the ENSAE-EHESS seminar, Paris, March 1992, for helpful comments and suggestions.