The Experts below are selected from a list of 290517 Experts worldwide ranked by ideXlab platform
Helga Kristjánsdóttir - One of the best experts on this subject based on the ideXlab platform.
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Estimating the Impact of Time-Invariant Variables on FDI with Fixed Effects
Review of World Economics, 2008Co-Authors: Ronald B. Davies, Delia Ionascu, Helga KristjánsdóttirAbstract:This paper applies the panel Fixed Effects with vector decomposition estimator to three FDI data sets to estimate the impact of time-invariant variables on FDI while including Fixed Effects. We find that the omission of Fixed Effects significantly biases the results, leading to contradictory predictions regarding the effect of trade costs and culture across data sets. After eliminating these biases, the differences across data sets largely disappear and many time-invariant variables consistently indicate the importance of vertical FDI. This suggests that some controversies in the literature may be driven by the extent to which unaccounted Fixed Effects biases vary across different data sets.
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Estimating the impact of time-invariant variables on FDI with Fixed Effects
Review of World Economics, 2008Co-Authors: Ronald B. Davies, Delia Baghdasaryan-ionascu, Helga KristjánsdóttirAbstract:This paper applies the panel Fixed Effects with vector decomposition estimator to three FDI (Foreign Direct Investment) data sets to estimate the impact of time-invariant variables on FDI while including Fixed Effects. We find that the omission of Fixed Effects significantly biases the results, leading to contradictory predictions regarding the effect of trade costs and culture across data sets. After eliminating these biases, the differences across data sets largely disappear and many time-invariant variables consistently indicate the importance of vertical FDI. This suggests that some controversies in the literature may be driven by the extent to which unaccounted Fixed Effects biases vary across different data sets.
Paul D Allison - One of the best experts on this subject based on the ideXlab platform.
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Asymmetric Fixed-Effects Models for Panel Data:
Socius: Sociological Research for a Dynamic World, 2019Co-Authors: Paul D AllisonAbstract:Standard Fixed-Effects methods presume that Effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite directi...
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Fixed Effects regression models
2009Co-Authors: Paul D AllisonAbstract:About the Author Series Editor's Introduction 1. Introduction 2. Linear Fixed Effects Models: Basics 3. Fixed Effects Logistic Models 4. Fixed Effects Models for Count Data 5. Fixed Effects Models for Events History Data 6. Structural Equation Models With Fixed Effects Appendix 1 Appendix 2 References Author Index Subject Index
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Fixed Effects negative binomial regression models
Sociological Methodology, 2002Co-Authors: Paul D Allison, Richard P WatermanAbstract:This paper demonstrates that the conditional negative binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true Fixed-Effects method. This method—which has been implemented in both Stata and LIMDEP—does not in fact control for all stable covariates. Three alternative methods are explored. A negative multinomial model yields the same estimator as the conditional Poisson estimator and hence does not provide any additional leverage for dealing with over-dispersion. On the other hand, a simulation study yields good results from applying an unconditional negative binomial regression estimator with dummy variables to represent the Fixed Effects. There is no evidence for any incidental parameters bias in the coefficients, and downward bias in the standard error estimates can be easily and effectively corrected using the deviance statistic. Finally, an approximate conditional method is found to perform at about the same level as the unconditional estimator.
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Bias in Fixed-Effects Cox Regression with Dummy Variables
2002Co-Authors: Paul D AllisonAbstract:One approach to doing Fixed-Effects regression analysis is simply to include dummy variables in the model for all the individuals (less one). Greene (2001) has recently introduced algorithms that make this computationally feasible even for nonlinear models with thousands of dummy variables. The dummy variable approach works well for linear regression and Poisson regression, but may suffer severe “incidental parameters bias” for logistic regression. The performance of the dummy variable method for Cox regression with repeated event data is unknown. I show by simulation that incidental parameters bias for Cox regression may be nearly as severe as that with logistic regression. Fortunately, as is well known, Fixed-Effects analysis of repeated event data is conveniently done by Cox regression combined with stratification on individuals, thereby eliminating the nuisance parameters.
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Fixed Effects negative binomial regression models
Sociological Methodology, 2002Co-Authors: Paul D Allison, Richard P WatermanAbstract:This paper demonstrates that the conditional negative binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true Fixed-Effects method. This method—which has been ...
Thibault Fally - One of the best experts on this subject based on the ideXlab platform.
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Structural Gravity and Fixed Effects
National Bureau of Economic Research, 2015Co-Authors: Thibault FallyAbstract:The gravity equation for trade flows is one of the most successful empirical models in economics and has long played a central role in the trade literature (Anderson, 2011). Different approaches to estimate the gravity equation, i.e. reduced-form or more structural, have been proposed. This paper examines the role of adding-up constraints as the key difference between structural gravity with "multilateral resistance" indexes and reduced-form gravity with simple Fixed Effects by exporter and importer. In particular, estimating gravity equations using the Poisson Pseudo-Maximum-Likelihood Estimator (Poisson PML) with Fixed Effects automatically satisfies these constraints and is consistent with the introduction of "multilateral resistance" indexes as in Anderson and van Wincoop (2003).
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structural gravity and Fixed Effects
Social Science Research Network, 2015Co-Authors: Thibault FallyAbstract:The gravity equation for trade flows is one of the most successful empirical models in economics and has long played a central role in the trade literature (Anderson, 2011). Different approaches to estimate the gravity equation, i.e. reduced-form or more structural, have been proposed. This paper examines the role of adding-up constraints as the key difference between structural gravity with "multilateral resistance" indexes and reduced-form gravity with simple Fixed Effects by exporter and importer. In particular, estimating gravity equations using the Poisson Pseudo-Maximum-Likelihood Estimator (Poisson PML) with Fixed Effects automatically satisfies these constraints and is consistent with the introduction of "multilateral resistance" indexes as in Anderson and van Wincoop (2003).Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Ronald B. Davies - One of the best experts on this subject based on the ideXlab platform.
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Estimating the Impact of Time-Invariant Variables on FDI with Fixed Effects
Review of World Economics, 2008Co-Authors: Ronald B. Davies, Delia Ionascu, Helga KristjánsdóttirAbstract:This paper applies the panel Fixed Effects with vector decomposition estimator to three FDI data sets to estimate the impact of time-invariant variables on FDI while including Fixed Effects. We find that the omission of Fixed Effects significantly biases the results, leading to contradictory predictions regarding the effect of trade costs and culture across data sets. After eliminating these biases, the differences across data sets largely disappear and many time-invariant variables consistently indicate the importance of vertical FDI. This suggests that some controversies in the literature may be driven by the extent to which unaccounted Fixed Effects biases vary across different data sets.
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Estimating the impact of time-invariant variables on FDI with Fixed Effects
Review of World Economics, 2008Co-Authors: Ronald B. Davies, Delia Baghdasaryan-ionascu, Helga KristjánsdóttirAbstract:This paper applies the panel Fixed Effects with vector decomposition estimator to three FDI (Foreign Direct Investment) data sets to estimate the impact of time-invariant variables on FDI while including Fixed Effects. We find that the omission of Fixed Effects significantly biases the results, leading to contradictory predictions regarding the effect of trade costs and culture across data sets. After eliminating these biases, the differences across data sets largely disappear and many time-invariant variables consistently indicate the importance of vertical FDI. This suggests that some controversies in the literature may be driven by the extent to which unaccounted Fixed Effects biases vary across different data sets.
William H Greene - One of the best experts on this subject based on the ideXlab platform.
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Fixed Effects vector decomposition a magical solution to the problem of time invariant variables in Fixed Effects models
Political Analysis, 2011Co-Authors: William H GreeneAbstract:In “Efficient Estimation of Time Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects,” Plumper and Troeger (2007), propose a three step procedure for the estimation of Fixed Effects models that, it is claimed, “provides the most reliable estimates under a wide variety of specifications common to real world data.” Their FEVD estimator is startlingly simple, involving three trivial steps, each requiring nothing more than ordinary least squares. Large gains in efficiency are claimed for cases of time invariant and slowly time varying regressors. A subsequent literature has compared the estimator to other estimators of Fixed Effects models, including Hausman and Taylor’s (1981) estimator, also (apparently) with impressive gains in efficiency. The article also claims to provide an efficient estimator for parameters on time invariant variables in the Fixed Effects model. None of the claims are correct. The FEVD estimator simply reproduces (identically) the linear Fixed Effects (dummy variable) estimator then substitutes an inappropriate covariance matrix for the correct one. The consistency result follows from the fact that OLS in the FE model is consistent. The “efficiency” gains are illusory. The claim that the estimator provides an estimator for the coefficients on time invariant variables in a Fixed Effects model is also incorrect. That part of the parameter vector remains unidentified. The “estimator” relies upon turning the Fixed Effects model into a random Effects model, in which case simple GLS estimation of all (now identified) parameters would be efficient among all estimators.
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The Behavior of the Fixed Effects Estimator in Nonlinear Models
2002Co-Authors: William H GreeneAbstract:The nonlinear Fixed Effects models in econometrics has often been avoided for two reasons one practical, one methodological. The practical obstacle relates to the difficulty of estimating nonlinear models with possibly thousands of coefficients. In fact, in a large number of models of interest to practitioners, estimation of the Fixed Effects model is feasible even in panels with very large numbers of groups. The more difficult, methodological question centers on the incidental parameters problem that raises questions about the statistical properties of the estimator. There is very little empirical evidence on the behavior of the Fixed Effects estimator. In this note, we use Monte Carlo methods to examine the small sample bias in the binary probit and logit models, the ordered probit model, the tobit model, the Poisson regression model for count data and the exponential regression model for a nonnegative random variable. We find three results of note: A widely accepted result that suggests that the probit estimator is actually relatively well behaved appears to be incorrect. Perhaps to some surprise, the tobit model, unlike the others, appears largely to be unaffected by the incidental parameters problem, save for a surprising result related to the disturbance variance estimator. Third, as apparently unexamined previously, the estimated asymptotic estimators for Fixed Effects estimators appear uniformly to be downward biased.
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Estimating Econometric Models With Fixed Effects
2001Co-Authors: William H GreeneAbstract:The application of nonlinear Fixed Effects models in econometrics has often been avoided for two reasons, one methodological, one practical. The methodological question centers on a incidental parametres problem that raises questions about the statistical properties of the estimator. The practical one relates to the difficulty of estimating nonlinear models with possibly thousands of coefficients. This note will demonstrate that the second is in fact, a nonissue, and that in a very large number models of interest to practioners, estimation of the Fixed Effects model is quite feasible even in panels with huge numbers of groups.
