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

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  • Andrei Sirchenko

    (European University Institute, Florence, Italy)

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 di¤erent processes. Moreover, the positive and negative responses can also be driven by distinct forces. To capture the unobserved heterogeneity this paper develops a two- stage cross-nested model, combining three ordered probit equations. In the policy rate setting context, the …rst 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, …ne-tune the rate at the second stage. The model allows for the possible correlation among the three latent decisions. This approach identi…es the driving factors and probabilities of three types of zeros: the ”neutral” zeros, generated directly by the neutral policy stance, and two kinds of ”o¤set” zeros, the ”loose” and ”tight” zeros, generated by the loose or tight stance, o¤set 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. Only a quarter of observed zeros appears to be generated by the neutral policy stance, suggesting a high degree of deliberate interest-rate smoothing by the central bank.

Suggested Citation

  • Andrei Sirchenko, 2013. "A model for ordinal responses with an application to policy interest rate," NBP Working Papers 148, Narodowy Bank Polski.
    • Handle: RePEc:nbp:nbpmis:148
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    2. Hamza Bennani, 2016. "Measuring Monetary Policy Stress for Fed District Representatives," Scottish Journal of Political Economy, Scottish Economic Society, vol. 63(2), pages 156-176, May.
    3. 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.
    4. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LP, vol. 21(1), pages 3-38, March.
    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.
    6. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.

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    More about this item

    Keywords

    ordinal responses; zero-in‡ated outcomes; three-part model; cross- nested model; policy interest rate; MPC votes; real-time data; panel data.;
    All these keywords.

    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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