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Conditional evaluation of predictive models: The cspa command

Author

Listed:
  • Jia Li

    (Singapore Management University)

  • Zhipeng Liao

    (University of California–Los Angeles)

  • Rogier Quaedvlieg

    (European Central Bank)

  • Wenyu Zhou

    (Zhejiang University)

Abstract

In this article, we introduce a new command, cspa, that implements the conditional superior predictive ability test developed in Li, Liao, and Quaed- vlieg (2022, Review of Economic Studies 89: 843–875). With the conditional per- formance of predictive methods measured nonparametrically by the conditional expectation functions of their predictive losses, we test the null hypothesis that a benchmark model weakly outperforms a collection of competitors uniformly across the conditioning space. The proposed command can implement this test for both independent cross-sectional data and serially dependent time-series data. Confi- dence sets for the most superior model can be obtained by inverting the test, for which the cspa command also offers a convenient implementation.

Suggested Citation

  • Jia Li & Zhipeng Liao & Rogier Quaedvlieg & Wenyu Zhou, 2022. "Conditional evaluation of predictive models: The cspa command," Stata Journal, StataCorp LP, vol. 22(4), pages 924-940, December.
    • Handle: RePEc:tsj:stataj:v:22:y:2022:i:4:p:924-940
    DOI: 10.1177/1536867X221141014
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