18 citations · 67 across the 26 of their papers we have counts for
8 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…
Solving the Forecast Combination Puzzle
David T. Frazier, Ryan Covey, Gael M. Martin +1
We demonstrate that the forecasting combination puzzle is a consequence of the methodology commonly used to produce forecast combinations. By the combination puzzle, we refer to th…
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…
Weak Identification in Discrete Choice Models
David T. Frazier, Eric Renault, Lina Zhang +1
We study the impact of weak identification in discrete choice models, and provide insights into the determinants of identification strength in these models. Using these insights, w…
Optimal probabilistic forecasts: When do they work?
Gael M. Martin, Rubén Loaiza-Maya, David T. Frazier +2
Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Here…
Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects
Lina Zhang, David T. Frazier, D. S. Poskitt +1
This paper examines the identification power of instrumental variables (IVs) for average treatment effect (ATE) in partially identified models. We decompose the ATE identification…
Approximate Maximum Likelihood for Complex Structural Models
Veronika Czellar, David T. Frazier, Eric Renault
Indirect Inference (I-I) is a popular technique for estimating complex parametric models whose likelihood function is intractable, however, the statistical efficiency of I-I estima…
Indirect Inference for Locally Stationary Models
David Frazier, Bonsoo Koo
We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asy…