Bias corrections for two-step fixed effects panel data estimators

This paper introduces large-T bias-corrected estimators for nonlinear panel data models with both time invariant and time varying heterogeneity. These models include systems of equations with limited dependent variables and unobserved individual effects, and sample selection models with unobserved individual effects. Our two-step approach first estimates the reduced form by fixed effects procedures to obtain estimates of the time varying heterogeneity underlying the endogeneity/selection bias. We then estimate the primary equation by fixed effects including an appropriately constructed control variable from the reduced form estimates as an additional explanatory variable. The fixed effects approach in this second step captures the time invariant heterogeneity while the control variable accounts for the time varying heterogeneity. Since either or both steps might employ nonlinear fixed effects procedures it is necessary to bias adjust the estimates due to the incidental parameters problem. This problem is exacerbated by the two-step nature of the procedure. As these two-step approaches are not covered in the existing literature we derive the appropriate correction thereby extending the use of large-T bias adjustments to an important class of models. Simulation evidence indicates our approach works well in finite samples and an empirical example illustrates the applicability of our estimator.     

The endogeneity bias in the relation between cost-of-debt capital and corporate disclosure policy

Abstract

The purpose of this paper is twofold. First, we provide a discussion of the problems associated with endogeneity in empirical accounting research. We emphasize problems arising when endogeneity is caused by (1) unobservable firm-specific factors and (2) omitted variables, and discuss the merits and drawbacks of using panel data techniques to address these causes. Second, we investigate the magnitude of endogeneity bias in Ordinary Least Squares (OLS) regressions of cost-of-debt capital on firm disclosure policy. We document how including a set of variables which theory suggests to be related with both cost-of-debt capital and disclosure and using fixed effects estimation in a panel data-set reduces the endogeneity bias and produces consistent results. This analysis reveals that the effect of disclosure policy on cost-of-debt capital is 200% higher than what is found in OLS estimation. Finally, we provide direct evidence that disclosure is impacted by unobservable firm-specific factors that are also correlated with cost of capital.     

Estimating panel data models in the presence of endogeneity and selection

We consider estimation of panel data models with sample selection when the equation of interest contains endogenous explanatory variables as well as unobserved heterogeneity. Assuming that appropriate instruments are available, we propose several tests for selection bias and two estimation procedures that correct for selection in the presence of endogenous regressors. The tests are based on the fixed effects two-stage least squares estimator, thereby permitting arbitrary correlation between unobserved heterogeneity and explanatory variables. The first correction procedure is parametric and is valid under the assumption that the errors in the selection equation are normally distributed. The second procedure estimates the model parameters semiparametrically using series estimators. In the proposed testing and correction procedures, the error terms may be heterogeneously distributed and serially dependent in both selection and primary equations. Because these methods allow for a rather flexible structure of the error variance and do not impose any nonstandard assumptions on the conditional distributions of explanatory variables, they provide a useful alternative to the existing approaches presented in the literature.     

Binary response panel data models with sample selection and self‐selection

We consider estimating binary response models on an unbalanced panel, where the outcome of the dependent variable may be missing due to nonrandom selection, or there is self‐selection into a treatment. In the present paper, we first consider estimation of sample selection models and treatment effects using a fully parametric approach, where the error distribution is assumed to be normal in both primary and selection equations. Arbitrary time dependence in errors is permitted. Estimation of both coefficients and partial effects, as well as tests for selection bias, are discussed. Furthermore, we consider a semiparametric estimator of binary response panel data models with sample selection that is robust to a variety of error distributions. The estimator employs a control function approach to account for endogenous selection and permits consistent estimation of scaled coefficients and relative effects.     

计量模型中的加总偏误与内生性：一种数值模拟方法

理论文献已经证明了细分数据模型的内生性和加总过程产生的内生性是加总偏误的根本原因。但是由于内生性涉及误差项与回归量之间的相关性问题,试图通过实证方法审视这两类内生性对加总偏误的影响变得比较困难,而数值模拟却是一个较为理想的方法。在数值模拟中,我们通过控制随机变量的分布形式以及随机变量之间的相关程度,进而对两类内生性因素产生的加总偏误进行全面细致的考察。本文的研究将为加总偏误的内生性解释提供有力证据。     

The impact of international outsourcing on individual employment security: A micro-level analysis

The paper analyzes how international outsourcing affected individual employment security. The analysis is carried out at the micro-level, combining monthly spell data from household panel data and industry-level outsourcing measures. By utilizing micro-level data, problems such as aggregation and potential endogeneity bias, as well as crude skill approximations that regularly hamper industry level displacement studies, can be reduced considerably. The main finding is that international outsourcing significantly lowers individual employment security. Interestingly, the effect does, however, not differ between high-, medium-, and low-skilled workers but only varies with job duration.     

The nonrandom selection of don't knows in binary and ordinal responses: Corrections with the bivariate probit model with sample selection

On social surveysdon't knows are a common answer to attitudinal questions, which often have binary or ordinal response categories.Don't knows can be nonrandomly selected according to certain demographic or socioeconomic characteristics of the respondent. To model the sample selection and correct for its bias, this paper discusses two types of bivariate models —binary-probit and the ordinal probit model with sample selection. The difference between parameter estimates and predicted probabilities from the analysis modelling the sample selection bias ofdon't knows and those from the analysis not modellingdon't knows is emphasized. Two empirical examples using the 1989 General Social Survey data demonstrate the necessity to correct for the bias in the nonrandom selection ofdon't knows for binary and ordinal attitudinal response variables. A replication of the analyses using the 1990 and 1991 General Social Survey data helps demonstrate the reliability of the sample selection bias ofdon't knows.     

Does the famine experience of board chair hamper innovation?

This study examines whether and how the famine experiences of board chairs influence the innovation of their firms. Results using a sample comprising 8882 firm-year observations from Chinese firms during the period 2003 to 2017 reveal that the board chair's famine experience has a negative effect on innovation. This negative effect is strengthened by famine intensity and high uncertainty. The obtained results are robust to alternative measures, endogeneity issues, omitted variables, and sample selection bias. Additional analyses showed that the relationship between board chair's famine experience and innovation is mediated by cash holdings and R&D investment. The overall results contribute to imprinting theory by explaining that early-life famine experiences of board chairs create survival threat imprints among them, eventually affecting their later-life behaviors. The findings also provide implications by highlighting how the early-life traumatic experiences of executives adversely influence their firms outcomes.     

Return to education in Finland

This study presents estimates of the return to education in Finland using an individual-level data set that also includes ability measures and information on family background. It is found that ability test scores have a strong effect on the choice of education and on subsequent earnings. Estimating the return to education with no information on ability leads to an upward bias in the estimates. However, this bias is more than offset by a downward bias caused by endogeneity or measurement error. Instrumental variables estimates that utilize family background variables as instruments produce estimates of the return to schooling that are approximately 60% higher than the least squares estimates.     

Can environmental governance lower toxic emissions? A panel study of U.S. high‐polluting industries

Firms have increasingly adopted environmental governance mechanisms in the form of environmental compensation and environmental board committees. The current study examines the argument that such environmental governance mechanisms contribute to lower toxic emissions in high‐polluting industries. The sample comprises firms that were part of the S&P 500 from the years 2006 to 2011 and were mandated to report toxic emissions to the U.S. Environmental Protection Agency under the Toxic Release Inventory program. A panel regression model with propensity score matching was employed to minimize endogeneity bias. The results indicate that environmental compensation is a compelling incentive to motivate managers to invest in long‐term and highly uncertain environmental projects. Likewise, the presence of an environmental board committee appears to be significant, suggesting that directors contribute to a firm's strategy with their expertise and political influence. This research also found evidence supporting the cumulative adoption of both environmental governance mechanisms in enhancing environmental performance and the firm's legitimacy.     

Self‐employment among women: Do children matter more than we previously thought?

This paper presents an estimation approach that addresses the problems of sample selection and endogeneity of fertility decisions when estimating the effect of young children on women's self‐employment. Using data from the National Longitudinal Survey of Youth 1979, 1982–2006, we find that ignoring self‐selection and endogeneity leads to underestimating the effect of young children. Once both sources of biases are accounted for, the estimated effect of young children roughly triples when compared to uncorrected results. This finding is robust to several changes in the specification and to the use of a different dataset.     

Choice of employment status and the wages of employees and the self-employed: Some further evidence

Using US data drawn from the 1978 young men NLS sample, this paper replicates a key finding reported by Rees and Shah in their analysis of self-employment using large-scale British data. A wage equation, which allows for sample selectivity, is estimated for subsamples of employees and the self-employed. Despite differences between the data sets and the variables analysed, the NLS results support Rees and Shah's conclusion that there is positive selection bias in the observed earnings of employees.     

Individual effects and dynamics in count data models

In this paper we examine the panel data estimation of dynamic models for count data that include correlated fixed effects and predetermined variables. Use of a linear feedback model is proposed. A quasi-differenced GMM estimator is consistent for the parameters in the dynamic model, but when series are highly persistent, there is a problem of weak instrument bias. An estimator is proposed that utilises pre-sample information of the dependent count variable, which is shown in Monte Carlo simulations to possess desirable small sample properties. The models and estimators are applied to data on US patents and R&D expenditure.     

Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on single‐agent dynamic discrete‐choice models and dynamic games of incomplete information. We propose a full‐solution maximum likelihood procedure and a two‐step method and use them to estimate an extended version of the capital replacement model of Rust with the original data and in a Monte Carlo study. Copyright © 2015 John Wiley & Sons, Ltd.     

School quality and housing prices: Empirical evidence from a natural experiment in Shanghai,China

The endogeneity of education quality and quantity accounts for difficulties in appropriately identifying the causal relationship between education and housing prices. To determine how education quality is capitalized into housing prices, we deal with endogeneity bias by employing a natural experiment occasioned by China’s education reforms. Based on monthly panel data for 52 residential areas in Shanghai, we conducted a natural experiment based on the exogenous designation of specific high-quality schools as Experimental Model Senior High Schools (EMSHS). Our natural experiment proved useful in analyzing how new information affected housing prices in China’s developing housing market. We found evidence that housing prices included allowances for these new EMSHS designations. In general, the presence of an additional EMSHS (of the best quality) per square kilometer increases housing prices by 17.1%. If one additional, previously non-designated high school is designated as EMSHS in a residential area, housing prices will be 6.9% higher.     

Multiplicative-error models with sample selection

This paper presents a simple approach to deal with sample selection in models with multiplicative errors. Models for non-negative limited dependent variables such as counts fit this framework. The approach builds on a specification of the conditional mean of the outcome only and is, therefore, semiparametric in nature. GMM estimators are constructed for both cross-section data and for panel data. We derive distribution theory and present Monte Carlo evidence on the finite-sample performance of the estimators.     

固定交互效应面板截取模型的ECM算法

交互效应面板数据模型在社会经济问题的实证分析中具有很强的适用性,但现有研究主要集中于线性面板模型。本文将交互效应引入非线性的面板截取模型,并基于ECM算法,建立了有效估计量和识别程序。基于不同因子类型的仿真实验结果显示,ECM算法可以很好地识别面板截取样本中的非观测因子。ECM估计量具有良好的有限样本性质,与其他估计量相比具有更小的偏误和更快的收敛速度。尤其是当共同因子为低频平滑因子时,其表现最为理想。     

Public sector corruption and the probability of technological disasters

A growing number of studies have explored the influence of institution on the outcomes of disasters and accidents from the viewpoint of political economy. This paper focuses on the probability of the occurrence of disasters rather than disaster outcomes. Using panel data from 98 countries, this paper examines how public sector corruption is associated with the probability of technological disasters. It was found that public sector corruption raises the probability of technological disasters. This result is robust when endogeneity bias is controlled.     

Nonparametric identification in nonseparable panel data models with generalized fixed effects

This paper extends the familiar notion of fixed effects to nonlinear structures with infinite-dimensional unobservables, like preferences. The main result is that a generalized version of differencing identifies local average responses (LARs) in nonseparable structures. In contrast to existing results, this does not require either substantial restrictions on functional form or independence between the persistent unobservables and the explanatory variables of interest, and it requires only two time periods. On the other hand, the results are confined to the subpopulation of “stayers” (Chamberlain, 1982), i.e., the population for which the explanatory variables do not change over time. We extend the basic framework to include time trends and dynamics in the explanatory variables, and we show how distributional effects as well as average partial effects are identified. Our approach also allows endogeneity in the transitory unobservables. Furthermore, we show that this new identification principle can be applied to well-known objects like the slope coefficient in the semiparametric panel data binary choice model with fixed effects. Finally, we suggest estimators for the local average response and average partial effect, and we analyze their large- and finite-sample behavior.     

Fixed effects instrumental variables estimation in correlated random coefficient panel data models

We provide a set of conditions sufficient for consistency of a general class of fixed effects instrumental variables (FE-IV) estimators in the context of a correlated random coefficient panel data model, where one ignores the presence of individual-specific slopes. We discuss cases where the assumptions are met and violated. Monte Carlo simulations verify that the FE-IV estimator of the population averaged effect performs notably better than other standard estimators, provided a full set of period dummies is included. We also propose a simple test of selection bias in unbalanced panels when we suspect the slopes may vary by individual.