Fixed Effects and Bias Due to the Incidental Parameters Problem in the Tobit Model

2004 ◽  
Vol 23 (2) ◽  
pp. 125-147 ◽  
Author(s):  
William Greene
Keyword(s):  
Fixed Effects ◽  
Tobit Model ◽  
Incidental Parameters

PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA

2009 ◽  
Vol 26 (3) ◽  
pp. 863-881 ◽  
Author(s):  
Jinyong Hahn ◽  
Hyungsik Roger Moon
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Multiple Equilibria ◽  
Finite Sample ◽  
Finite Support ◽  
Time Dimension ◽  
Cross Sectional ◽  
Incidental Parameters ◽  
Cross Sectional Dimension ◽  
Fixed Effects Estimator

We study a nonlinear panel data model in which the fixed effects are assumed to have finite support. The fixed effects estimator is known to have the incidental parameters problem. We contribute to the literature by making a qualitative observation that the incidental parameters problem in this model may not be not as severe as in the conventional case. Because fixed effects have finite support, the probability of correctly identifying the fixed effect converges to one even when the cross sectional dimension grows as fast as some exponential function of the time dimension. As a consequence, the finite sample bias of the fixed effects estimator is expected to be small.

scholarly journals LIKELIHOOD INFERENCE IN AN AUTOREGRESSION WITH FIXED EFFECTS

2015 ◽  
Vol 32 (5) ◽  
pp. 1178-1215 ◽  
Author(s):  
Geert Dhaene ◽  
Koen Jochmans
Keyword(s):  
Fixed Effects ◽  
Profile Likelihood ◽  
Estimating Equation ◽  
Parameter Estimates ◽  
Incidental Parameters ◽  
Global Maximizer ◽  
Profile Score ◽  
Profile Likelihoods ◽  
Adjusted Profile Likelihood ◽  
Adjusted Likelihood

We calculate the bias of the profile score for the regression coefficients in a multistratum autoregressive model with stratum-specific intercepts. The bias is free of incidental parameters. Centering the profile score delivers an unbiased estimating equation and, upon integration, an adjusted profile likelihood. A variety of other approaches to constructing modified profile likelihoods are shown to yield equivalent results. However, the global maximizer of the adjusted likelihood lies at infinity for any sample size, and the adjusted profile score has multiple zeros. Consistent parameter estimates are obtained as local maximizers inside or on an ellipsoid centered at the maximum likelihood estimator.

scholarly journals 7. Fixed-Effects Negative Binomial Regression Models

2002 ◽  
Vol 32 (1) ◽  
pp. 247-265 ◽  
Author(s):  
Paul D. Allison ◽  
Richard P. Waterman
Keyword(s):  
Fixed Effects ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Alternative Methods ◽  
Incidental Parameters ◽  
Binomial Regression ◽  
Downward Bias ◽  
Over Dispersion ◽  
Poisson Estimator ◽  
Deviance Statistic

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.

scholarly journals Estimation of Stochastic Frontier Models with Fixed Effects through Monte Carlo Maximum Likelihood

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Grigorios Emvalomatis ◽  
Spiro E. Stefanou ◽  
Alfons Oude Lansink
Keyword(s):  
Monte Carlo ◽  
Random Effects ◽  
Fixed Effects ◽  
Likelihood Function ◽  
Stochastic Frontier ◽  
General Context ◽  
Explanatory Variables ◽  
Monte Carlo Experiments ◽  
Incidental Parameters ◽  
Stochastic Frontier Models

Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are updated using information from the data and are robust to possible correlation of the group-specific constant terms with the explanatory variables. Monte Carlo experiments are performed in the specific context of stochastic frontier models to examine and compare the sampling properties of the proposed estimator with those of the random-effects and correlated random-effects estimators. The results suggest that the estimator is unbiased even in short panels. An application to a cross-country panel of EU manufacturing industries is presented as well. The proposed estimator produces a distribution of efficiency scores suggesting that these industries are highly efficient, while the other estimators suggest much poorer performance.

BOOTSTRAP AND k-STEP BOOTSTRAP BIAS CORRECTIONS FOR THE FIXED EFFECTS ESTIMATOR IN NONLINEAR PANEL DATA MODELS

2016 ◽  
Vol 32 (6) ◽  
pp. 1523-1568 ◽  
Author(s):  
Min Seong Kim ◽  
Yixiao Sun
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Markov Models ◽  
Model Parameters ◽  
Series Length ◽  
Cross Sectional ◽  
Incidental Parameters ◽  
Fixed Effects Estimator ◽  
Coverage Accuracy ◽  
Bias Corrections

Because of the incidental parameters problem, the fixed effects maximum likelihood estimator in a nonlinear panel data model is in general inconsistent when the time series length T is short and fixed. Even if T approaches infinity but at a rate not faster than the cross sectional sample size n, the fixed effects estimator is still asymptotically biased. This paper proposes using the standard bootstrap and k-step bootstrap to correct the bias. We establish the asymptotic validity of the bootstrap bias corrections for both model parameters and average marginal effects. Our results apply to static models as well as some dynamic Markov models. Monte Carlo simulations show that our procedures are effective in reducing the bias of the fixed effects estimator and improving the coverage accuracy of the associated confidence interval.

scholarly journals Incidental Parameters Problem: The Case of Gompertz Model

2021 ◽  
pp. 8-14
Author(s):  
Ismaila A. Bolarinwa ◽  
Bushirat T. Bolarinwa
Keyword(s):  
Monte Carlo ◽  
Fixed Effects ◽  
Gompertz Model ◽  
Performance Criteria ◽  
Monte Carlo Approach ◽  
Mean Squared Errors ◽  
Incidental Parameters

The order of bias of the fixed effects gompertz model is studied, using Monte Carlo approach. Performance criteria are bias and root mean squared errors. For fixed N, bias is found to decrease steadily between T=5 and T=20 but exhibits a mixture of increase and decline afterwards. At each value of T involved, bias steadily decreases with increased value of N. Bias is found to be at most 123%, due to the combination of minimum of each of N and T involved. Decrease in order of bias is found to be more definite with increased N at fixed T than with increased T at fixed N.

Das Sozio-Oekonomische Panel als Datenbasis für die Arbeits- und Organisationspsychologie

2018 ◽  
Vol 62 (3) ◽  
pp. 111-125 ◽  
Author(s):  
Marco Giesselmann ◽  
Mila Staneva ◽  
Jürgen Schupp ◽  
David Richter
Keyword(s):  
Fixed Effects

Zusammenfassung. Der Beitrag zeigt die Analysepotentiale der repräsentativen Mikrodaten des Sozio-oekonomischen Panels (SOEP) für die Arbeits- und Organisationspsychologie (A/O-Psychologie) auf. Dabei werden allgemeine Charakteristika von Stichprobe und Erhebung des SOEP vorgestellt, sowie Konstrukte mit besonderer Relevanz für die Psychologie eingeführt. Zudem diskutieren wir Analysemethoden für Paneldaten, mit denen sich die Potentiale des SOEP realisieren lassen. Neben den Möglichkeiten des SOEP für Stabilitäts- und Verlaufsanalysen stellen wir die Potentiale längsschnittlicher Daten für kausale Analysen heraus. Dabei erläutern wir insbesondere die Analyselogik längsschnittlicher Fixed Effects Modellierungen und vergleichen diese mit weiteren längsschnittlichen Analyseverfahren. Wir argumentieren, dass bei Anwendung akkurater Methoden Teilaspekte der experimentellen Analyselogik auf Grundlage längsschnittlicher Surveydaten angenähert werden können. Folglich stellen die Daten des SOEP immer dann eine wertvolle Ressource für die A/O-Psychologie dar, wenn a) unabhängige Merkmale aus ethischen oder praktischen Gründen nicht systematisch manipuliert werden können, b) die Kernbefunde experimenteller Primärstudien auf Grundlage eines repräsentativen Samples repliziert werden sollen oder c) Interesse am langfristigen Verlauf eines Indikators besteht.

Dampak Kebijakan Moneter Pada Output Di Negara-Negara ASEAN Tahun 1980-2014

2017 ◽  
Vol 4 (1) ◽  
pp. 58-70
Author(s):  
Nur Widiastuti
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Fixed Effects ◽  
Empirical Studies ◽  
Negative Relationship ◽  
Least Square ◽  
Fixed Effects Model ◽  
State Data ◽  
Ordinary Least Square ◽  
The Impact

The Impact of monetary Policy on Ouput is an ambiguous. The results of previous empirical studies indicate that the impact can be a positive or negative relationship. The purpose of this study is to investigate the impact of monetary policy on Output more detail. The variables to estimatate monetery poicy are used state and board interest rate andrate. This research is conducted by Ordinary Least Square or Instrumental Variabel, method for 5 countries ASEAN. The state data are estimated for the period of 1980 – 2014. Based on the results, it can be concluded that the impact of monetary policy on Output shown are varied.Keyword: Monetary Policy, Output, Panel Data, Fixed Effects Model

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