Essays On Nonlinear Panel Data Models
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Essays On Nonlinear Panel Data Models
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Author : Center for Economic Research (Tilburg)
language : en
Publisher:
Release Date : 2014
Essays On Nonlinear Panel Data Models written by Center for Economic Research (Tilburg) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Three Essays On Nonlinear Panel Data Models And Quantile Regression Analysis
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Author : Iván Fernández-Val
language : en
Publisher:
Release Date : 2005
Three Essays On Nonlinear Panel Data Models And Quantile Regression Analysis written by Iván Fernández-Val and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.
This dissertation is a collection of three independent essays in theoretical and applied econometrics, organized in the form of three chapters. In the first two chapters, I investigate the properties of parametric and semiparametric fixed effects estimators for nonlinear panel data models. The first chapter focuses on fixed effects maximum likelihood estimators for binary choice models, such as probit, logit, and linear probability model. These models are widely used in economics to analyze decisions such as labor force participation, union membership, migration, purchase of durable goods, marital status, or fertility. The second chapter looks at generalized method of moments estimation in panel data models with individual-specific parameters. An important example of these models is a random coefficients linear model with endogenous regressors. The third chapter (co-authored with Joshua Angrist and Victor Chernozhukov) studies the interpretation of quantile regression estimators when the linear model for the underlying conditional quantile function is possibly misspecified.
Essays On Nonlinear Panel Data Models And Conditional Quantiles
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Author : Deniz Baglan
language : en
Publisher:
Release Date : 2010
Essays On Nonlinear Panel Data Models And Conditional Quantiles written by Deniz Baglan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Nonparametric statistics categories.
Chapter 3 extends a linear stochastic production frontier model with time-varying individual effects to a nonparametric model in which the functional form of the production frontier is unspecified. We derive the kernel estimator for such a frontier in fixed effects framework and implement Monte Carlo simulations to investigate finite sample performances of our estimator. Lastly, we apply the estimator proposed in this chapter to estimate the production function and time-varying technical efficiency of private manufacturing establishments in Egypt over the period 1988 to 1996.
Essays On Semiparametric And Nonparametric Estimation Of Nonlinear Panel Data Models
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Author : Wang, Xi
language : en
Publisher:
Release Date : 2015
Essays On Semiparametric And Nonparametric Estimation Of Nonlinear Panel Data Models written by Wang, Xi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Estimation theory categories.
Essays On Nonlinear Panel Models With Unobserved Heterogeneity
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Author : Robert Martin
language : en
Publisher:
Release Date : 2017
Essays On Nonlinear Panel Models With Unobserved Heterogeneity written by Robert Martin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Electronic dissertations categories.
Three Essays On Panel Data Models With Interactive And Unobserved Effects
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Author : Nicholas Lynn Brown
language : en
Publisher:
Release Date : 2022
Three Essays On Panel Data Models With Interactive And Unobserved Effects written by Nicholas Lynn Brown and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Electronic dissertations categories.
Chapter 1: More Efficient Estimation of Multiplicative Panel Data Models in the Presence of Serial Correlation (with Jeffrey Wooldridge)We provide a systematic approach in obtaining an estimator asymptotically more efficient than the popular fixed effects Poisson (FEP) estimator for panel data models with multiplicative heterogeneity in the conditional mean. In particular, we derive the optimal instrumental variables under appealing `working' second moment assumptions that allow underdispersion, overdispersion, and general patterns of serial correlation. Because parameters in the optimal instruments must be estimated, we argue for combining our new moment conditions with those that define the FEP estimator to obtain a generalized method of moments (GMM) estimator no less efficient than the FEP estimator and the estimator using the new instruments. A simulation study shows that the GMM estimator behaves well in terms of bias, and it often delivers nontrivial efficiency gains -- even when the working second-moment assumptions fail.Chapter 2: Information equivalence among transformations of semiparametric nonlinear panel data modelsI consider transformations of nonlinear semiparametric mean functions which yield moment conditions for estimation. Such transformations are said to be information equivalent if they yield the same asymptotic efficiency bound. I first derive a unified theory of algebraic equivalence for moment conditions created by a given linear transformation. The main equivalence result states that under standard regularity conditions, transformations which create conditional moment restrictions in a given empirical setting need only to have an equal rank to reach the same efficiency bound. Example applications are considered, including nonlinear models with multiplicative heterogeneity and linear models with arbitrary unobserved factor structures.Chapter 3: Moment-based Estimation of Linear Panel Data Models with Factor-augmented ErrorsI consider linear panel data models with unobserved factor structures when the number of time periods is small relative to the number of cross-sectional units. I examine two popular methods of estimation: the first eliminates the factors with a parameterized quasi-long-differencing (QLD) transformation. The other, referred to as common correlated effects (CCE), uses the cross-sectional averages of the independent and response variables to project out the space spanned by the factors. I show that the classical CCE assumptions imply unused moment conditions which can be exploited by the QLD transformation to derive new linear estimators which weaken identifying assumptions and have desirable theoretical properties. I prove asymptotic normality of the linear QLD estimators under a heterogeneous slope model which allows for a tradeoff between identifying conditions. These estimators do not require the number of cross-sectional variables to be less than T-1, a strong restriction in fixed-$T$ CCE analysis. Finally, I investigate the effects of per-student expenditure on standardized test performance using data from the state of Michigan.
Essays On Panel And Nonlinear Time Series Analysis
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Author : Namwon Hyung
language : en
Publisher:
Release Date : 1999
Essays On Panel And Nonlinear Time Series Analysis written by Namwon Hyung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Interest rates categories.
Essays In Econometrics
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Author : Xueyuan Liu
language : en
Publisher:
Release Date : 2022
Essays In Econometrics written by Xueyuan Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The dissertation consists of three chapters on different econometric topics. The first chapter studies jackknife bias reduction for simulated maximum likelihood estimator of discrete choice models. We propose to reduce asymptotic biases of simulated maximum likelihood estimators (SMLE) by using a jackknife method similar to Dhaene and Jochmans (2015), which was originally proposed to reduce bias in nonlinear panel models. Lee (1995) investigates the asymptotic bias of the SMLE, and derives the analytical formula of higher order bias due to simulation. However, implementation of Lee (1995)'s method requires analytical characterization of the higher order bias, which may not be convenient for practice. Because the jackknife method does not require an explicit characterization of the bias, it may be a practically attractive alternative to Lee (1995)'s estimator. The second chapter studies estimation of average treatment effects for massively unbalanced binary outcomes. The maximum likelihood estimator (MLE) of the average treatment effects (ATE) in the logit model for binary outcomes may have a significant second order bias if the event has a low probability. The analysis of rare events is relevant for economics because some of the big data sets are collected from online sources where the number of events (such as " clicks" and " purchases") is much smaller than the number of nonevents. The literature about rare events (King and Zeng, 2001; Chen and Giles, 2012; Rilstone, 1996; Wang, 2020) does not shed light on the finite sample behavior of logit MLE and ATE if events are rare. In this chapter, we also derive the second order bias of the logit ATE estimator and propose bias-corrected estimators of the ATE. We also propose a variation on the logit model with parameters that are elasticities. Finally, we propose a computational trick that avoids numerical instability in the case of estimation for rare events. The third chapter studies a Vuong test (Vuong, 1989) for panel data models with fixed effects. This chapter generalizes the Vuong test to nonlinear panel models where the dimension of incidental parameters grows with the sample size. The incidental parameters (Neyman and Scott, 1948) that affect the unbiasedness of the parameters of interest are also important for panel data models as they capture unobserved heterogeneity. The discrepancy in incidental parameters plays an important role in model selection; for example, as noted by MacKinnon et al. (2020), there is a vast literature on the cluster-robust inference that assumes the structure of the clusters is correctly specified, which is often violated. In the presence of incidental parameters, we cannot easily apply the classical Vuong test to select a panel data model. This chapter proposes a new model selection test for panel data models by extending the classical Vuong test, which selects from two parametric likelihood models based on their Kullback-Leibler information criterion (KLIC). This chapter proposes three different test statistics for researchers who need to deal with all possible relationships between candidate models: overlapping models, nested models, and strictly nonnested models. These three model relationships are classified according to the structure of low-dimensional parameter of interest and high-dimensional incidental parameters. We allow for disagreements about incidental parameters and obtain specification tests based on a modified likelihood function.
Nonlinear Statistical Modeling
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Author : Takeshi Amemiya
language : en
Publisher: Cambridge University Press
Release Date : 2001-01-08
Nonlinear Statistical Modeling written by Takeshi Amemiya and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-01-08 with Business & Economics categories.
This collection investigates parametric, semiparametric, nonparametric, and nonlinear estimation techniques in statistical modeling.
Essays In Econometrics
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Author : Daniel Wilhelm
language : en
Publisher:
Release Date : 2012
Essays In Econometrics written by Daniel Wilhelm and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
This dissertation consists of three parts. The first chapter provides a nonparametric identification argument for nonlinear panel data models with measurement error. The second proposes a simple model selection procedure based on a test statistic that has a chi-square one distribution regardless of whether the models are correctly specified, misspecified, overlapping, nested, or nonnested. The third part develops a data-driven bandwidth selection procedure for nonstationary time series processes.