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A model for ordinal responses with an application to policy interest rate

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  • Sirchenko Andrei

Abstract

The decisions to reduce, leave unchanged, or increase (the price, rating, policy interest rate, etc.) are often characterized by abundant no-change outcomes that are generated by different processes. Moreover, the positive and negative responses can also be driven by distinct forces. To capture the heterogeneity of the data-generating process this paper develops a two-stage cross-nested model, combining three ordered probit equations. In the policy rate setting context, the first stage, a policy inclination decision, determines a latent policy stance (loose, neutral or tight), whereas the two latent amount decisions, conditional on a loose or tight stance, fine-tune the rate at the second stage. The model allows for the possible correlation among the three latent decisions. This approach identifies the driving factors and probabilities of three types of zeros: the "neutral" zeros, generated directly by a neutral policy stance, and two kinds of "offset" zeros, the "loose" and "tight" zeros, generated by a loose or tight stance, offset at the second stage. Monte Carlo experiments show good performance in small samples. Both the simulations and empirical applications to the panel data on individual policymakers' votes for the interest rate demonstrate the superiority with respect to the conventional and two-part models.

Suggested Citation

  • Sirchenko Andrei, 2012. "A model for ordinal responses with an application to policy interest rate," EERC Working Paper Series 12/13e, EERC Research Network, Russia and CIS.
    • Handle: RePEc:eer:wpalle:12/13e
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    4. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.
    5. Hamza Bennani & Etienne Farvaque & Piotr Stanek, 2015. "FOMC members’ incentives to disagree: regional motives and background influences," NBP Working Papers 221, Narodowy Bank Polski.
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    7. Tobias A. Möller & Christian H. Weiß & Hee-Young Kim & Andrei Sirchenko, 2018. "Modeling Zero Inflation in Count Data Time Series with Bounded Support," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 589-609, June.

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    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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