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Tilting Approximate Models

Andreas Tryphonides

arXiv 28 May 2018 · Econometrics

arXiv:1805.10869 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Model approximations are common practice when estimating structural or quasi-structural models. The paper considers the econometric properties of estimators that utilize projections to reimpose information about the exact model in the form of conditional moments. The resulting estimator efficiently combines the information provided by the approximate law of motion and the moment conditions. The paper develops the corresponding asymptotic theory and provides simulation evidence that tilting substantially reduces the mean squared error for parameter estimates. It applies the methodology to pricing long-run risks in aggregate consumption in the US, whereas the model is solved using the Campbell and Shiller (1988) approximation. Tilting improves empirical fit and results suggest that approximation error is a source of upward bias in estimates of risk aversion and downward bias in the elasticity of intertemporal substitution.

Citation extraction

54
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80
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appendix boundary found by appendix_titled_section at “Appendix A” · 55% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Pohl, Schmedders, and Wilms (2018) Higher-Order Effects in Asset Pricing Models with Long-Run Risks1.00063100%
2Schennach (2007) Point Estimation with Exponentially Tilted Likelihood0.9285380%
3Komunjer and Ragusa (2016) Existence and Characterization of Conditional Density Projections0.92843100%
4Campbell and Shiller (1988) Stock Prices, Earnings, and Expected Dividends0.92843100%
5Schorfheide, Song, and Yaron (2018) Identifying Long Run Risks: A Bayesian Mixed-Frequency Approach0.64441100%
6Ackerberg, Geweke, and Hahn (2009) Comments on "Convergence Properties of the Likelihood of Computed Dynamic Models0.64422100%
7Bansal and Yaron (2004) Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles0.64422100%
8Chen, Christensen, and Tamer (2018) Monte Carlo Confidence Sets for Identified Sets0.64422100%
9Giacomini and Ragusa (2014) Theory-coherent forecasting0.5112250%
10Newey and McFadden (1994) Chapter 36 Large sample estimation and hypothesis testing0.5112250%

Showing the top 10 of 54 scored citations.