Bartolucci, Francesco and Pigini, Claudia and Valentini, Francesco (2021): MCMC Conditional Maximum Likelihood for the twoway fixedeffects logit.

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Abstract
We propose a Markov chain Monte Carlo Conditional Maximum Likelihood (MCMCCML) estimator for twoway fixedeffects logit models for dyadic data. The proposed MCMC approach, based on a Metropolis algorithm, allows us to overcome the computational issues of evaluating the probability of the outcome conditional on nodes in and out degrees, which are sufficient statistics for the incidental parameters. Under mild regularity conditions, the MCMCCML estimator converges to the exact CML one and is asymptotically normal. Moreover, it is more efficient than the existing pairwise CML estimator. We study the finite sample properties of the proposed approach by means of a simulation study and three empirical applications, where we also show that the MCMCCML estimator can be applied to binary logit models for panel data with both subject and time fixed effects. Results confirm the expected theoretical advantage of the proposed approach, especially with small and sparse networks or with rare events in panel data.
Item Type:  MPRA Paper 

Original Title:  MCMC Conditional Maximum Likelihood for the twoway fixedeffects logit 
Language:  English 
Keywords:  Directed network, Fixed effects, Link formation, Metropolis algorithm, Panel data 
Subjects:  C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C23  Panel Data Models ; Spatiotemporal Models C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C25  Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling 
Item ID:  110034 
Depositing User:  Francesco Valentini 
Date Deposited:  07 Oct 2021 04:48 
Last Modified:  07 Oct 2021 04:48 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/110034 