5 citations · 6 across the 5 of their papers we have counts for
7 papers · 1 filter
Scalable likelihood-based inference for limited dependent variable models
David T. Frazier, Ruben Loaiza-Maya, Didier Nibbering
Limited dependent variable models are central to empirical economics, but likelihood-based inference is infeasible when likelihoods involve high-dimensional integration over latent…
A Multinomial Probit Model for Asymmetric Choice Responses
Cash Looi, Ruben Loaiza-Maya, Didier Nibbering
Standard multinomial probit (MNP) models specify symmetric latent utility distributions, implying that choice probabilities respond symmetrically to positive and negative covariate…
Hybrid unadjusted Langevin methods for high-dimensional latent variable models
Ruben Loaiza-Maya, Didier Nibbering, Dan Zhu
The exact estimation of latent variable models with big data is known to be challenging. The latents have to be integrated out numerically, and the dimension of the latent variable…
Bayesian Forecasting in Economics and Finance: A Modern Review
Gael M. Martin, David T. Frazier, Worapree Maneesoonthorn +6
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting problem…
Efficient variational approximations for state space models
Rubén Loaiza-Maya, Didier Nibbering
Variational Bayes methods are a potential scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state…
Fast variational Bayes methods for multinomial probit models
Rubén Loaiza-Maya, Didier Nibbering
The multinomial probit model is often used to analyze choice behaviour. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which l…