Econometrics Commons^™

The Mundlak Estimator In A Panel Data Model With Serially Correlated Error Component Disturbances, Badi H. Baltagi, Long Liu

Center for Policy Research

This paper shows that the classic Mundlak (1978) result where the random effects estimator reduces to the fixed effects estimator when the regressors are all correlated with the individual effects, may not hold if the remainder disturbances have a general serial correlation variance-covariance matrix. This includes the popular AR(1), MA(1) and ARMA(p, q) processes for serial correlation. This is illustrated with an empirical example for the AR(1) case.

Time Invariant Variables In The Mundlak And Hausman-Taylor Panel Data Models, Badi H. Baltagi, Long Liu

Center for Policy Research

This paper shows that the classic augmented Mundlak (1978) regression yields the between estimator for time-invariant variables. It is well known that the estimates of the time varying variables yield the fixed effects estimates. While the latter are consistent for this correlated random effects model, the between estimates are not. The between estimator is consistent only when the Hausman (1978) test does not reject the null based on between versus fixed effects. An alternative modified Hausman and Taylor (1981) estimator is proposed. Monte Carlo experiments are performed to compare various estimators under a correlated random effects as well as a …

A Panel Quantile Model Via Correlated Random Effects Approach For Testing Pecking Order Theory, Zongwu Cai, Meng Shi, Wuqing Wu, Yue Zhao

Student Publications

This article investigates the relative importance of internal and external sources of funds in financing activities across different levels of investment activities by proposing a panel data quantile regression model with correlated random effects, accounting for heteroscedasticity in both firm-specific individuals and distribution of investment. A new estimation method, which takes the influence of disturbance into account, is proposed by using the integrated quasi-likelihood function for the conditional quantile model and Laplace approximation. The large sample theory for the proposed estimator and the corresponding asymptotic χ² test are investigated. A Monte Carlo simulation is conducted to examine the finite …

Efficient Estimation Of Generalized Nonparametric Model Under Additive Structure, Ying Xia

Dissertations and Theses Collection (Open Access)

In this thesis, we develop novel nonparametric estimation techniques for two distinct classes of models: (1) Generalized Additive Models with Unknown Link Functions (GAMULF) and (2) Generalized Panel Data Transformation Models with Fixed Effects. Both models avoid parametric assumptions on their respective link or transformation functions, as well as the distribution of the idiosyncratic error terms.

The first chapter aims to provide an in-depth and systematic introduction to cross- sectional and panel-data nonparametric transformation models, encompassing practical applications, a diverse range of estimation techniques, and the study of asymptotic properties. We discuss the advantages and limitations of these models and …

Essays On Socioeconomic Shocks And Policies In Agriculture, Wilman Iglesias

Department of Agricultural Economics: Dissertations, Theses, and Student Research

The three chapters of this doctoral dissertation estimate the responses of agricultural productivity, production value of agriculture, and crop supply to some external shocks and policies. Using unique panel datasets for Colombia and the United States, this research provides new insights regarding the responsiveness of agriculture to some socioeconomic effects and related market policies. Chapter 1 studies the impact of armed conflicts in rural areas on legal agricultural productivity in Colombia by using a production function that includes violence shocks such as the forced intra-national displacement of the rural population from 1995 to 2017. Chapter 2 investigates the effect of …

Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin

Research Collection School Of Economics

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle …

Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin

Research Collection School Of Economics

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle …

Nonstationary Panels With Unobserved Heterogeneity, Wenxin Huang

Dissertations and Theses Collection (Open Access)

This dissertation develops several econometric techniques to address the unobserved heterogeneity in nonstationary panels, namely identifying latent group structures in cointegrated panels, studying nonstationary panels with both cross-sectional dependence and latent group structures, and estimating panel error-correction model with unobserved dynamic common factors.

Chapter 1 considers a panel cointegration model with latent group structures that allows for heterogeneous long-run relations across groups. We extend Su et al. (2013) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the dimension of the …

Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White

Research Collection School Of Economics

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G-) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G-non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G-non-causality to test …

An Investigation Into The Relative Importance Of Civil Institutions On Subjective Well-Being, Samuel Fleming

Graduate Student Theses, Dissertations, & Professional Papers

Poverty is not a material problem; in fact, it is a much greater problem, the problem of social, natural, and economic disconnect from the institutions of stability, wealth, justice, and well-being. When considering human flourishing, we must look beyond the material at “lives that are not necessarily morally good, but good for us” (Tiberius, 2006, p. 493). In the vein of Sen’s Capabilities Theory, this paper uses subjective well-being as a comprehensive measure of an individual’s life and circumstances examines how civil institutions impact an individual’s non-monetary welfare function. This paper conducts a series of regression models in an attempt …

Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su

Research Collection School Of Economics

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to …

The Effects Of Borrowing Rates On Intra-Firm Disequilibria Between Equity Prices And Cds Premiums – Evidence From Dynamic Panel Analysis., Robert J. Brown

Undergraduate Economic Review

Cointegration techniques are used to estimate the long run equilibrium relationship between a firm’s CDS premium and its equity price, for a panel of large-cap US firms. From these results, the estimated disequilibrium in daily CDS premiums, with respect to equity prices, is constructed. Dynamic panel methods are employed to show the importance of lagged changes in libor rates as determinants of the estimated disequilibrium. Evidence is found that the extent to which the markets deviate from equilibrium will increase as one-month libor rates rise, but, counter-intuitively, will decrease (return towards equilibrium) as longer term libor rates rise.

A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei

Research Collection School Of Economics

This paper provides a practical test for strict exogeneity in linear panel data models with fixed effects when the number of individuals N goes to infinity while the number of time periods T is fixed. The test is based on the supremum of a sequence of Wald test statistics. Under suitable conditions, we establish the asymptotic distribution of the test statistic and consistency of the test. A bootstrap procedure is proposed to improve the finite sample performance and the validity of the procedure is justified. We investigate the finite sample performance of the test via a small set of Monte …

Shrinkage Estimation Of Common Breaks In Panel Data Models Via Adaptive Group Fused Lasso, Junhui Qian, Liangjun Su

Research Collection School Of Economics

In this paper we consider estimation and inference of common breaks in panel data models via adaptive group fused Lasso. We consider two approaches—penalized least squares (PLS) for first-differenced models without endogenous regressors, and penalized GMM (PGMM) for first-differenced models with endogeneity. We show that with probability tending to one, both methods can correctly determine the unknown number of breaks and estimate the common break dates consistently. We establish the asymptotic distributions of the Lasso estimators of the regression coefficients and their post Lasso versions. We also propose and validate a data-driven method to determine the tuning parameter used in …

Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshino

Research Collection School Of Economics

In this paper we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We estimate the functional coefficients by the sieve-IVQR technique and establish the uniform consistency and asymptotic normality of the estimators. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients and study its asymptotic. We conduct simulations to evaluate the finite sample behavior of our estimator and test statistic, and apply our method to study the estimation of quantile Engel curves.

Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White

Research Collection School Of Economics

Granger non-causality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G-) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G- non-causality follows from structural non-causality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G- non-causality …

Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su

Research Collection School Of Economics

In this paper we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to …

Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin

Research Collection School Of Economics

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon andWeidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to …

Sieve Instrumental Variable Quantile Regression Estimation Of Functional Coefficient Models, Liangjun Su, Tadao Hoshina

Research Collection School Of Economics

In this paper, we consider sieve instrumental variable quantile regression (IVQR) estimation of functional coefficient models where the coefficients of endogenous regressors are unknown functions of some exogenous covariates. We approximate the unknown functional coefficients by some basis functions and estimate them by the IVQR technique. We establish the uniform consistency and asymptotic normality of the estimators of the functional coefficients. Based on the sieve estimates, we propose a nonparametric specification test for the constancy of the functional coefficients, study its asymptotic properties under the null hypothesis, a sequence of local alternatives and global alternatives, and propose a wild-bootstrap procedure …

Do Expected Marginal Revenue Products For National Hockey League Players Equal Their Price In Daily Fantasy Games?, Benjamin Goldman

Award Winning Economics Papers

The equality between wages and marginal revenue products is a backbone of competitive labor markets. This study will seek to test the congruity between the two in the market for players in daily fantasy hockey games. Any observed and statistically significant incongruity would lead to the conclusion that an individual can earn long run profit playing daily fantasy games. Both fixed effects and pooled regressions are employed to isolate inequalities between prices and expected marginal revenue products for players in daily fantasy hockey games. Any deviation of such could potentially be explained by utility maximizing gamblers or incomplete information. Robust …

Three Essays On Large Panel Data Models With Cross-Sectional Dependence, Yonghui Zhang

Dissertations and Theses Collection (Open Access)

My dissertation consists of three essays which contribute new theoretical results to large panel data models with cross-sectional dependence. These essays try to answer or partially answer some prominent questions such as how to detect the presence of cross-sectional dependence and how to capture the latent structure of cross-sectional dependence and estimate parameters efficiently by removing its effects. Chapter 2 introduces a nonparametric test for cross-sectional contemporaneous dependence in large dimensional panel data models based on the squared distance between the pair-wise joint density and the product of the marginals. The test can be applied to either raw observable data …

Sieve Estimation Of Panel Data Models With Cross Section Dependence, Liangjun Su, Sainan Jin

Research Collection School Of Economics

In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.

Testing For Common Trends In Semi-Parametric Panel Data Models With Fixed Effects, Yonghui Zhang, Liangjun Su, Peter C. B. Phillips

Research Collection School Of Economics

This paper proposes a non-parametric test for common trends in semi-parametric panel data models with fixed effects based on a measure of non-parametric goodness-of-fit (R2). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null. Then we run a local linear regression of the augmented residuals on a time trend and calculate the non-parametric R2 for each cross-section unit. The proposed test statistic is obtained by averaging all cross-sectional non-parametric R2s, …

Asymptotic Variance And Extensions Of A Denisty-Weighted-Response Semiparametric Estimator, Myoung-Jae Lee, Fali Huang, Young-Sook Kim

Research Collection School Of Economics

Building on some early works, Lewbel (2000) proposed estimators for binary and ordered discrete response models with endogenous regressors. These estimators have been extended for panel data and for truncated and censored models by later papers. The estimators are particularly innovative in that the latent linear regression functions are pulled out of the nonlinear limited dependent variable models, which are then treated as if they were the usual linear models. But understanding the estimators and their applications have been “hampered” by less-than-ideal expositions and assumptions. For this problem, this short note reviews the estimators and makes the following three points. …

Testing For Heteroskedasticity And Spatial Correlation In A Random Effects Panel Data Model, Badi H. Baltagi, Seuck Heun Song, Jae Hyeok Kwon

Center for Policy Research

A panel data regression model with heteroskedastic as well as spatially correlated disturbance is considered, and a joint LM test for homoskedasticity and no spatial correlation is derived. In addition, a conditional LM test for no spatial correlation given heteroskedasticity, as well as a conditional LM test for homoskedasticity given spatial correlation, are also derived. These LM tests are compared with marginal LM tests that ignore heteroskedasticity in testing for spatial correlation, or spatial correlation in testing for homoskedasticity. Monte Carlo results show that these LM tests as well as their LR counterparts perform well even for small N and …

Testing For Heteroskedasticity And Serial Correlation In A Random Effects Panel Data Model, Badi H. Baltagi, Byoung Cheol Jung, Seuck Heun Song

Center for Policy Research

This paper considers a panel data regression model with heteroskedastic as well as serially correlated disturbances, and derives a joint LM test for homoskedasticity and no first order serial correlation. The restricted model is the standard random individual error component model. It also derives a conditional LM test for homoskedasticity given serial correlation, as well as a conditional LM test for no first order serial correlation given heteroskedasticity, all in the context of a random effects panel data model. Monte Carlo results show that these tests, along with their likelihood ratio alternatives, have good size and power under various forms …

Testing For Random Effects And Spatial Lag Dependence In Panel Data Models, Badi H. Baltagi, Long Liu

Center for Policy Research

This paper derives a joint Lagrande Multiplier (LM) test which simultaneously tests for the absence of spatial lag dependence and random individual effects in a panel data regression model. It turns out that this LM statistic is the sum of two standard LM statistics. The first one tests for the absence of spatial lag dependence ignoring the random individual effects, and the second one tests for the absence of random individual effects ignoring the spatial lag dependence. This paper also derives two conditional LM tests. The first one tests for the absence of random individual effects without ignoring the possible …

A Monte Carlo Study For Pure And Pretest Estimators Of A Panel Data Model With Spatially Auto Correlated Disturbances, Badi H. Baltagi, Peter Egger, Michael Pfaffermayr

Center for Policy Research

This paper examines the consequences of model misspecification using a panel data model with spatially auto correlated disturbances. The performance of several maximum likelihood estimators assuming different specifications for this model are compared using Monte Carlo experiments. These include (i) MLE of a random effects model that ignore the spatial correlation; (ii) MLE described in Anselin (1988) which assumes that the individual effects are not spatially auto correlated; (iii) MLE described in Kapoor et al. (2006) which assumes that both the individual effects and the remainder error are governed by the same spatial autocorrelation; (iv) MLE described in Baltagi et …

Copula-Based Tests For Cross-Sectional Independence In Panel Models, Chihwa Kao, Giovanni Urga

Center for Policy Research

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Profile Likelihood Estimation Of Partially Linear Panel Data Models With Fixed Effects, Liangjun Su, Aman Ullah

Research Collection School Of Economics

We consider consistent estimation of partially linear panel data models with fixed effects. We propose profile-likelihood-based estimators for both the parametric and nonparametric components in the models and establish convergence rates and asymptotic normality for both estimators.