# A two-step indirect inference approach to estimate the long-run risk asset pricing model

*Joachim Grammig* and
*Eva-Maria Küchlin*

*Journal of Econometrics*, 2018, vol. 205, issue 1, 6-33

**Abstract:**
The long-run consumption risk model provides a theoretically appealing explanation for prominent asset pricing puzzles, but its intricate structure presents a challenge for econometric analysis. This paper proposes a two-step indirect inference approach that disentangles the estimation of the model’s macro-economic dynamics and the investor’s preference parameters. A Monte Carlo study explores the feasibility and efficiency of the estimation strategy. We apply the method to recent U.S. data and provide a critical re-assessment of the long-run risk model’s ability to reconcile the real economy and financial markets. This two-step indirect inference approach is potentially useful for the econometric analysis of other prominent consumption-based asset pricing models that are equally difficult to estimate.

**Keywords:** Indirect inference estimation; Asset pricing; Long-run risk (search for similar items in EconPapers)

**JEL-codes:** C58 G10 G12 (search for similar items in EconPapers)

**Date:** 2018

**References:** View references in EconPapers View complete reference list from CitEc

**Citations** Track citations by RSS feed

**Downloads:** (external link)

http://www.sciencedirect.com/science/article/pii/S0304407618300423

Full text for ScienceDirect subscribers only

**Related works:**

This item may be available elsewhere in EconPapers: Search for items with the same title.

**Export reference:** BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text

**Persistent link:** https://EconPapers.repec.org/RePEc:eee:econom:v:205:y:2018:i:1:p:6-33

Access Statistics for this article

Journal of Econometrics is currently edited by *T. Amemiya*, *A. R. Gallant*, *J. F. Geweke*, *C. Hsiao* and *P. M. Robinson*

More articles in Journal of Econometrics from Elsevier

Bibliographic data for series maintained by Dana Niculescu ().