Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce


IMPACT FACTOR 2018: 0.448
5-years IMPACT FACTOR: 0.877

CiteScore 2018: 0.85

SCImago Journal Rank (SJR) 2018: 0.552
Source Normalized Impact per Paper (SNIP) 2018: 0.561

Mathematical Citation Quotient (MCQ) 2018: 0.07

Online
ISSN
1558-3708
See all formats and pricing
More options …
Volume 24, Issue 1

Issues

Volume 24 (2020)

A model for ordinal responses with heterogeneous status quo outcomes

Andrei SirchenkoORCID iD: https://orcid.org/0000-0003-0567-4170
Published Online: 2019-04-10 | DOI: https://doi.org/10.1515/snde-2018-0059

Abstract

The decisions to reduce, leave unchanged, or increase a choice variable (such as policy interest rates) are often characterized by abundant status quo outcomes that can be generated by different processes. The decreases and increases may also be driven by distinct decision-making paths. Neither conventional nor zero-inflated models for ordinal responses adequately address these issues. This paper develops a flexible endogenously switching model with three latent regimes, which create separate processes for interest rate hikes and cuts and overlap at a no-change outcome, generating three different types of status quo decisions. The model is not only favored by statistical tests but also produces economically more meaningful inference with respect to the existing models, which deliver biased estimates in the simulations.

This article offers supplementary material which is provided at the end of the article.

Keywords: MPC votes; ordinal responses; policy interest rate; regime switching; zero-inflated model

Article Note

The first draft of this paper was circulated in the proceedings of the 2nd Doctoral Workshop in Economic Theory and Econometrics (MOOD-2012) at Einaudi Institute for Economics and Finance in Rome in June 2012.

References

  • Bagozzi, B. E., and B. Mukherjee. 2012. “A Mixture Model for Middle Category Inflation in Ordered Survey Responses.” Political Analysis 20: 369–386.CrossrefWeb of ScienceGoogle Scholar

  • Basu, D., and R. M. de Jong. 2007. “Dynamic Multinomial Ordered Choice with an Application to the Estimation of Monetary Policy Rules.” Studies in Nonlinear Dynamics and Econometrics 11 (4): 1–35.Google Scholar

  • Blinder, A. S. 2004. The Quiet Revolution: Central Banking Goes Modern. New Haven, CT: Yale University Press.Google Scholar

  • Brooks, R., M. N. Harris, and C. Spencer. 2012. “Inflated Ordered Outcomes.” Economics Letters 117 (3): 683–686.CrossrefWeb of ScienceGoogle Scholar

  • Cragg, J. G. 1971. “Some Statistical Models for Limited Dependent Variables with Application to the Demand for Durable Goods.” Econometrica 39 (5): 829–844.CrossrefGoogle Scholar

  • Dolado, J., R. Maria-Dolores, and M. Naveira. 2005. “Are Monetary-Policy Reaction Functions Asymmetric?: The Role of Nonlinearity in the Phillips Curve.” European Economic Review 49 (2): 485–503.CrossrefGoogle Scholar

  • Greene, W. H. 2004. “Convenient Estimators for the Panel Probit Model.” Empirical Economics 29 (1): 21–47.CrossrefGoogle Scholar

  • Greene, W. H., and D. A. Hensher. 2010. Modeling Ordered Choices: A Primer. Cambridge University Press.Google Scholar

  • Hamilton, J. D. 1989. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57 (2): 357–384.CrossrefGoogle Scholar

  • Hamilton, J. D., and O. Jorda. 2002. “A Model for the Federal Funds Rate Target.” Journal of Political Economy 110 (5): 1135–1167.CrossrefGoogle Scholar

  • Harris, M. N., and X. Zhao. 2007. “A Zero-Inflated Ordered Probit Model, with an Application to Modelling Tobacco Consumption.” Journal of Econometrics 141 (2): 1073–1099.CrossrefWeb of ScienceGoogle Scholar

  • Hartman, R. S., M. Doane, and C.-K. Woo. 1991. “Consumer Rationality and the Status Quo.” Quarterly Journal of Economics 106: 141–162.CrossrefGoogle Scholar

  • Hu, L., and P. C. B. Phillips. 2004. “Dynamics of the Federal Funds Target Rate: A Nonstationary Discrete Choice Approach.” Journal of Applied Econometrics 19: 851–867.CrossrefGoogle Scholar

  • Kahneman, D., J. L. Knetsch, and R. H. Thaler. 1991. “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias.” Journal of Economic Perspectives 5 (1): 193–206.CrossrefGoogle Scholar

  • Kaminsky, G. L., and C. M. Reinhart. 1999. “The Twin Crises: The Causes of Banking and Balance-of-Payments Problems.” American Economic Review 89 (3): 473–500.CrossrefGoogle Scholar

  • Kauppi, H. 2012. “Predicting the Direction of the Fed’s Target Rate.” Journal of Forecasting 31: 47–67.Web of ScienceCrossrefGoogle Scholar

  • MacKinnon, J. G. 1996. “Numerical Distribution Functions for Unit Root and Cointegration Tests.” Journal of Applied Econometrics 11: 601–618.CrossrefGoogle Scholar

  • McKelvey, R. D., and W. Zavoina. 1975. “A Statistical Model for the Analysis of Ordinal Level Dependent Variables.” Journal of Mathematical Sociology 4: 103–120.CrossrefGoogle Scholar

  • Piazzesi, M. 2005. “Bond Yields and the Federal Reserve.” Journal of Political Economy 113 (2): 311–344.CrossrefGoogle Scholar

  • Poole, W. 2003. “Fed Transparency: How, not Whether.” Federal Reserve Bank of St. Louis Review November/December: 1–8.Google Scholar

  • Samuelson, W., and R. Zeckhauser. 1988. “Status Quo Bias in Decision Making.” Journal of Risk and Uncertainty 1: 7–59.CrossrefGoogle Scholar

  • Small, K. 1987. “A Discrete Choice Model for Ordered Alternatives.” Econometrica 55: 409–424.CrossrefGoogle Scholar

  • Van den Hauwe, R. Paap, and D. van Dijk. 2013. “Bayesian Forecasting of Federal Funds Target Rate Decisions.” Journal of Macroeconomics 37: 19–40.CrossrefWeb of ScienceGoogle Scholar

  • Vovsha, P. 1997. “Application of Cross-Nested Logit Model to Mode Choice in Tel Aviv, Israel, Metropolitan Area.” Transportation Research Record 1607: 6–15.CrossrefGoogle Scholar

  • Vuong, Q. 1989. “Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses.” Econometrica 57 (2): 307–333.CrossrefGoogle Scholar

  • Wen, C.-H., and F. Koppelman. 2001. “The Generalized Nested Logit Model.” Transportation Research B 35: 627–641.CrossrefGoogle Scholar

  • Wilde, J. 2000. “Identification of Multiple Equation Probit Models with Endogenous Dummy Regressors.” Economics Letters 69 (3): 309–312.CrossrefGoogle Scholar

  • Winkelmann, R. 2008. Econometric Analysis of Count Data. 5th edition. Springer.Google Scholar

About the article

Published Online: 2019-04-10


Funding Source: Global Development Network

Award identifier / Grant number: #R10-0221

Global Development Network, Grant Number: #R10-0221. Economics Education and Research Consortium, Grant Number: Zvi Griliches Excellence Award.


Citation Information: Studies in Nonlinear Dynamics & Econometrics, Volume 24, Issue 1, 20180059, ISSN (Online) 1558-3708, DOI: https://doi.org/10.1515/snde-2018-0059.

Export Citation

©2020 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Supplementary Article Materials

Comments (0)

Please log in or register to comment.
Log in