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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.

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  • 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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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. Sirchenko, Andrei, 2010. "Policymakers' Votes and Predictability of Monetary Policy," University of California at San Diego, Economics Working Paper Series qt8qj3z3qg, Department of Economics, UC San Diego.
    3. James D. Hamilton & Oscar Jorda, 2002. "A Model of the Federal Funds Rate Target," Journal of Political Economy, University of Chicago Press, vol. 110(5), pages 1135-1167, October.
    4. Small, Kenneth A, 1987. "A Discrete Choice Model for Ordered Alternatives," Econometrica, Econometric Society, vol. 55(2), pages 409-424, March.
    5. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    6. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    7. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    8. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    9. William Greene, 2004. "Convenient estimators for the panel probit model: Further results," Empirical Economics, Springer, vol. 29(1), pages 21-47, January.
    10. Cragg, John G, 1971. "Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods," Econometrica, Econometric Society, vol. 39(5), pages 829-844, September.
    11. Schmidt, Peter & Witte, Ann Dryden, 1989. "Predicting criminal recidivism using 'split population' survival time models," Journal of Econometrics, Elsevier, vol. 40(1), pages 141-159, January.
    12. Leung, Siu Fai & Yu, Shihti, 1996. "On the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 197-229.
    13. Gronau, Reuben, 1974. "Wage Comparisons-A Selectivity Bias," Journal of Political Economy, University of Chicago Press, vol. 82(6), pages 1119-1143, Nov.-Dec..
    14. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    15. Brooks, Robert & Harris, Mark N. & Spencer, Christopher, 2012. "Inflated ordered outcomes," Economics Letters, Elsevier, vol. 117(3), pages 683-686.
    16. Harris, Mark N. & Zhao, Xueyan, 2007. "A zero-inflated ordered probit model, with an application to modelling tobacco consumption," Journal of Econometrics, Elsevier, vol. 141(2), pages 1073-1099, December.
    17. Eichengreen, Barry & Watson, Mark W & Grossman, Richard S, 1985. "Bank Rate Policy under the Interwar Gold Standard: A Dynamic Probit Model," Economic Journal, Royal Economic Society, vol. 95(379), pages 725-745, September.
    18. Madden, David, 2008. "Sample selection versus two-part models revisited: The case of female smoking and drinking," Journal of Health Economics, Elsevier, vol. 27(2), pages 300-307, March.
    19. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    20. William H. Greene, 1994. "Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models," Working Papers 94-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    21. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    22. Sirchenko Andrey, 2008. "Modeling monetary policy in real time:Does discreteness matter?," EERC Working Paper Series 08/07e, EERC Research Network, Russia and CIS.
    23. Petra Gerlach‐Kristen, 2004. "Is the MPC's Voting Record Informative about Future UK Monetary Policy?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 106(2), pages 299-313, June.
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    Cited by:

    1. 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.
    2. 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.
    3. Hamza Bennani & Etienne Farvaque & Piotr Stanek, 2015. "FOMC members’ incentives to disagree: regional motives and background influences," NBP Working Papers 221, Narodowy Bank Polski.
    4. Bennani, Hamza & Farvaque, Etienne & Stanek, Piotr, 2018. "Influence of regional cycles and personal background on FOMC members’ preferences and disagreement," Economic Modelling, Elsevier, vol. 68(C), pages 416-424.
    5. 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.
    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

    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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