Laura Liu, Hyungsik Roger Moon, Frank Schorfheide
arXiv 28 Sep 2017 · Econometrics · publishedEconometrica (2020) · 62 citations (OpenAlex)
arXiv:1709.10193 · PDF · DOI · OpenAlex · Extracted main text
This paper considers the problem of forecasting a collection of short time series using cross sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross-sectional information to transform the unit-specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a non-parametric estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated-random-effects distribution as known (ratio-optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.
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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 | Brown and Greenshtein (2009) Nonparametric empirical Bayes and compound decision approaches to estimation of a high-dimensional vector of normal means | 1.000 | 7 | 3 | 100% |
| 2 | Arellano and Bover (1995) Another look at the instrumental variable estimation of error-components models | 0.843 | 3 | 3 | 100% |
| 3 | Arellano and Bond (1991) Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations | 0.644 | 2 | 2 | 100% |
| 4 | Blundell and Bond (1998) Initial conditions and moment restrictions in dynamic panel data models | 0.644 | 2 | 2 | 100% |
| 5 | Efron (2011) Tweedie's Formula and Selection Bias | 0.644 | 2 | 2 | 100% |
| 6 | Robbins (1951) Asymptocially Subminimax Solutions of Compound Decision Problems | 0.644 | 2 | 2 | 100% |
| 7 | Covas, Rump, and Zakrajsek (2014) Stress-Testing U.S. Bank Holding Companies: A Dynamic Panel Quantile Regression Approach | 0.511 | 3 | 2 | 33% |
| 8 | Arellano and Bonhomme (2012) Identifying distributional characteristics in random coefficients panel data models | 0.511 | 2 | 2 | 50% |
| 9 | Alvarez and Arellano (2003) The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators | 0.405 | 1 | 1 | 100% |
| 10 | Anderson and Hsiao (1981) Estimation of dynamic models with error components | 0.405 | 1 | 1 | 100% |
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