Dynamic censored regression and the open market desk reaction function

Robert de Jong, Ana María Herrera

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The censored regression model and the Tobit model are standard tools in econometrics. This paper provides a formal asymptotic theory for dynamic time series censored regression when lags of the dependent variable have been included among the regressors. The central analytical challenge is to prove that the dynamic censored regression model satisfies stationarity and weak dependence properties if a condition on the lag polynomial holds. We show the formal asymptotic correctness of conditional maximum likelihood estimation of the dynamic Tobit model, and the correctness of Powell's least absolute deviations procedure for the estimation of the dynamic censored regression model. The paper is concluded with an application of the dynamic censored regression methodology to temporary purchases of the Open Market Desk. This article has supplementary material online.

Original languageEnglish
Pages (from-to)228-237
Number of pages10
JournalJournal of Business and Economic Statistics
Volume29
Issue number2
DOIs
StatePublished - Apr 2011

Keywords

  • Announcement effect
  • CLAD
  • Censored regression
  • Open market operations
  • Tobit

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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