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TIME-VARYING COEFFICIENT MODELS: A PROPOSAL FOR SELECTING THE COEFFICIENT DRIVER SETS

Published online by Cambridge University Press:  20 January 2016

Stephen G. Hall ,
P. A. V. B. Swamy  and
George S. Tavlas
Stephen G. Hall
Affiliation:
Leicester University, Bank of Greece and NIESR
P. A. V. B. Swamy
Affiliation:
Federal Reserve Board (Retired)
George S. Tavlas*
Affiliation:
Bank of Greece and Leicester University
*
Address correspondence to: George S. Tavlas, Member, Monetary Policy Council, Bank of Greece, 21 El. Venizelos Avenue, 102 50 Athens, Greece; e-mail: GTavlas@bankofgreece.gr.
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Abstract

Coefficient drivers are observable variables that feed into time-varying coefficients (TVCs) and explain at least part of their movement. To implement the TVC approach, the drivers are split into two subsets, one of which is correlated with the bias-free coefficient that we want to estimate and the other with the misspecification in the model. This split, however, can appear to be arbitrary. We provide a way of splitting the drivers that takes account of any nonlinearity that may be present in the data, with the aim of removing the arbitrary element in driver selection. We also provide an example of the practical use of our method by applying it to modeling the effect of ratings on sovereign-bond spreads.

Keywords

Time-Varying Coefficient ModelCoefficient DriverSpecification ProblemCorrect Interpretation of Coefficients
Type
Articles
Information
Macroeconomic Dynamics , Volume 21 , Special Issue 5: Recent Topics in Computational Macroeconomics , July 2017 , pp. 1158 - 1174
Copyright
Copyright © Cambridge University Press 2016 

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Footnotes

This paper was presented at the third ISCEF (Paris, April 10–12, 2014, www.iscef.com). The views expressed in this paper are the authors' own and do not necessarily represent those of their respective institutions.

References

REFERENCES

Ahmad, Yamin and Lo, Ming Chien (2014) Nonlinear time series models and model selection. In Ma, J. and Wohar, M. (eds.), Recent Advances in Estimating Nonlinear Models with Applications in Economics and Finance, pp. 99–121. New York: Springer.Google Scholar
Basmann, Robert L. (1988) Causality tests and observationally equivalent representations of econometric models. Journal of Econometrics 39, 69–104.CrossRefGoogle Scholar
Chang, I-Lok, Hallahan, Charles, and Swamy, P.A.V.B. (1992) Efficient computation of stochastic coefficient models. In Amman, H.M., Belsley, D.A., and Pau, L.F. (eds.), Computational Economics and Econometrics, pp. 43–53. Boston: Kluwer Academic Publishers.Google Scholar
Chang, I-Lok, Swamy, P.A.V.B., Hallahan, Charles, and Tavlas, George S. (2000) A computational approach to finding causal economic laws. Computational Economics 16, 105–136.Google Scholar
Granger, Clive W.J. (2008) Non-linear models: Where do we go next—Time varying parameter models. Studies in Nonlinear Dynamics and Econometrics 12, 1–10.Google Scholar
Hall, Stephen G., Hondroyiannis, George, Swamy, P.A.V.B., and Tavlas, George S. (2008) A Portfolio Balance Approach to Euro-area Money Demand in a Time-Varying Environment. Paper presented at Bank of England/MMF workshop on money and macromodels.Google Scholar
Hall, Stephen G., Hondroyiannis, George, Swamy, P.A.V.B., and Tavlas, George S. (2009a) The New Keynesian Phillips curve and lagged inflation: A case of spurious correlation? Southern Economic Journal 76, 467–481.Google Scholar
Hall, Stephen G., Hondroyiannis, George, Swamy, P.A.V.B., and Tavlas, George S. (2009b) Where has all the money gone? Wealth and the demand for money in South Africa. Journal of African Economies 18, 84–112.Google Scholar
Hall, Stephen G., Kenjegaliev, Amangeldi, Swamy, P.A.V.B., and Tavlas, George S. (2013) The forward rate premium puzzle: A case of misspecification? Studies in Nonlinear Dynamics and Econometrics 17, 265–279.Google Scholar
Hall, Stephen G., Swamy, P.A.V.B., and Tavlas, George S. (2012) Generalized cointegration: A new concept with an application to health expenditure and health outcomes. Empirical Economics 42, 303–318.Google Scholar
Hall, Stephen G., Swamy, P.A.V.B., and Tavlas, George S. (2015) A note on generalizing the concept of cointegration. Macroeconomic Dynamics 19 (7), 1633–1646.Google Scholar
Harvey, Andrew C. (1989) Forecasting Structural Time Series Models and the Kalman Filter. Cambridge, UK: Cambridge University Press.Google Scholar
Judge, George G., Griffiths, William E., Hill, R. Carter, Lütkepohl, Helmut, and Lee, Tsoung-Chao (1985) The Theory and Practice of Econometrics, 2nd ed. New York: Wiley.Google Scholar
Lehmann, Erich L. (1999) Elements of Large-Sample Theory. Berlin: Springer.Google Scholar
Lehmann, Erich L. and Casella, George (1998) Theory of Point Estimation, 2nd ed. Berlin: Springer.Google Scholar
Skyrms, Brian (1988) Probability and causation. Journal of Econometrics 39, 53–68.Google Scholar
Swamy, P.A.V.B. (1970) Efficient inference in a random coefficient regression model. Econometrica 38, 311–323.Google Scholar
Swamy, P.A.V.B. (1974) Linear models with random coefficients. In Zarembka, P. (ed.), Frontiers in Econometrics, pp. 143–168. New York: Academic Press.Google Scholar
Swamy, P.A.V.B., Hall, Stephen G., and Tavlas, George S. (2015) Microproduction functions with unique coefficients and errors. Macroeconomic Dynamics 19, 311–333.Google Scholar
Swamy, P.A.V.B. and Mehta, Jatinder S. (1975) Bayesian and non-Bayesian analysis of switching regressions and a random coefficient regression model. Journal of the American Statistical Association 70, 593–602.Google Scholar
Swamy, P.A.V.B., Mehta, Jatinder S., Tavlas, George S., and Hall, Stephen G. (2014) Small area estimation with correctly specified linking models. In Ma, Jun and Wohar, Mark (eds.), Recent Advances in Estimating Nonlinear Models with Applications in Economics and Finance, pp. 193–228. New York: Springer.Google Scholar
Swamy, P.A.V.B. and Tavlas, George S. (2001) Random coefficient models. In Baltagi, Badi H. (ed.), Companion to Theoretical Econometrics, pp. 410–428. Malden, MA: Blackwell.Google Scholar
Swamy, P.A.V.B. and Tavlas, George S. (2007) The New Keynesian Phillips curve and inflation expectations: Re-specification and interpretation. Economic Theory 31, 293–306.Google Scholar
Swamy, P.A.V.B., Tavlas, George S., Hall, Stephen G., and Hondroyiannis, George (2010) Estimation of parameters in the presence of model misspecification and measurement error. Studies in Nonlinear Dynamics and Econometrics 14, 1–33.Google Scholar
White, Halbert (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817–838.Google Scholar