Timothy Christensen, Hyungsik Roger Moon, Frank Schorfheide
arXiv 6 Nov 2020 · Econometrics · 3 citations (OpenAlex)
arXiv:2011.03153 · PDF · DOI · OpenAlex · Extracted main text
We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspecification or structural breaks. We derive "robust" forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for dynamic discrete choice, the robust forecasts depend in a natural way on a small number of convex optimization problems which can be simplified using duality methods. Finally, we derive "efficient robust" forecasts to deal with the problem of first having to estimate the set of forecast distributions and develop a suitable asymptotic efficiency theory. Forecasts obtained by replacing nuisance parameters that characterize the set of forecast distributions with efficient first-stage estimators can be strictly dominated by our efficient robust forecasts.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hirano, K. and J. R. Porter (2009) Asymptotics for statistical treatment rules | 1.000 | 7 | 4 | 100% |
| 2 | Honoré, B. E. and E. Tamer (2006) Bounds on parameters in panel dynamic discrete choice models | 1.000 | 6 | 3 | 100% |
| 3 | van der Vaart, A (2000) Asymptotic Statistics | 0.811 | 4 | 2 | 100% |
| 4 | Chernozhukov, V., I. Fernández-Val, J. Hahn, and W. Newey (2013) Average and quantile effects in nonseparable panel models | 0.737 | 3 | 2 | 100% |
| 5 | Christensen, T. and B. Connault (2019) Counterfactual sensitivity and robustness self | 0.737 | 3 | 2 | 100% |
| 6 | Csiszár, I. and F. Matús (2012) Generalized minimizers of convex integral functionals, Bregman distance, Pythagorean identities | 0.644 | 2 | 2 | 100% |
| 7 | Giacomini, R. and T. Kitagawa (2018) Robust bayesian inference for set-identified models | 0.644 | 2 | 2 | 100% |
| 8 | Kitagawa, T (2012) Estimation and inference for set-identified parameters using posterior lower probabilities | 0.644 | 2 | 2 | 100% |
| 9 | Moon, H. R. and F. Schorfheide (2012) Bayesian and frequentist inference in partially identified models self | 0.644 | 2 | 2 | 100% |
| 10 | Bonhomme, S. and M. Weidner (2019) Minimizing sensitivity to model misspecification | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 55 scored citations.