Robust iInference in linear asset pricing models

Arbeitspapier

We derive new results on the asymptotic behavior of the estimated parameters of a linear asset pricing model and their associated t-statistics in the presence of a factor that is independent of the returns. The inclusion of this useless factor in the model leads to a violation of the full rank (identification) condition and renders the inference nonstandard. We show that the estimated parameter associated with the useless factor diverges with the sample size but the misspecificationrobust t-statistic is still well-behaved and has a standard normal limiting distribution. The asymptotic distributions of the estimates of the remaining parameters and the model specification test are also affected by the presence of a useless factor and are nonstandard. We propose a robust and easy-to-implement model selection procedure that restores the standard inference on the parameters of interest by identifying and removing the factors that do not contribute to improved pricing. The finitesample properties of our asymptotic approximations and the practical relevance of our results are illustrated using simulations and an empirical application.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 2012-17

Classification and Topics

• Classification: Wirtschaft ; Asset Pricing; Trading Volume; Bond Interest Rates ; Estimation: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
• Subject: asset pricing models ; Hansen-Jagannathan distance ; model selection ; model misspecification

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Gospodinov, Nikolay ; Kan, Raymond ; Robotti, Cesare
• Event: Veröffentlichung
• (who): Federal Reserve Bank of Atlanta
• (where): Atlanta, GA
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/70612