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Time-Varying Coefficient Models: A Proposal For Selecting The Coefficient Driver Sets

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  • Hall, Stephen G.
  • Swamy, P. A. V. B.
  • Tavlas, George S.

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.

Suggested Citation

  • Hall, Stephen G. & Swamy, P. A. V. B. & Tavlas, George S., 2017. "Time-Varying Coefficient Models: A Proposal For Selecting The Coefficient Driver Sets," Macroeconomic Dynamics, Cambridge University Press, vol. 21(5), pages 1158-1174, July.
    • Handle: RePEc:cup:macdyn:v:21:y:2017:i:05:p:1158-1174_00
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    References listed on IDEAS

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    1. Basmann, R. L., 1988. "Causality tests and observationally equivalent representations of econometric models," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 69-104.
    2. Cuthbertson, Keith & Taylor, Mark P., 1990. ""The case of the missing money" and the Lucas critique," Journal of Macroeconomics, Elsevier, vol. 12(3), pages 437-454.
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    4. P. Swamy & George Tavlas, 2007. "The New Keynesian Phillips Curve and Inflation Expectations: Re-Specification and Interpretation," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 31(2), pages 293-306, May.
    5. Swamy, P.A.V.B. & Mehta, Jatinder S. & Chang, I-Lok & Zimmerman, T.S., 2009. "An efficient method of estimating the true value of a population characteristic from its discrepant estimates," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2378-2389, April.
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    8. Stephen G.Hall & George Hondroyiannis & P.A.V.B. Swamy & George S. Tavlas, 2007. "A Portofolio Balance Approach to Euro-Area Money Demand in a Time-Varying Environment," Working Papers 61, Bank of Greece.
    9. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    10. Stephen G. Hall & George Hondroyiannis & P. A. V. B. Swamy & G. S. Tavlas, 2009. "The New Keynesian Phillips Curve and Lagged Inflation: A Case of Spurious Correlation?," Southern Economic Journal, John Wiley & Sons, vol. 76(2), pages 467-481, October.
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    12. Stephen G. Hall & George Hondroyiannis & P.A.V.B. Swamy & George S. Tavlas, 2009. "Where Has All the Money Gone? Wealth and the Demand for Money in South Africa †," Journal of African Economies, Centre for the Study of African Economies, vol. 18(1), pages 84-112, January.
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    14. I-Lok Chang & P.A.V.B. Swamy & Charles Hallahan & George S. Tavlas, 2000. "A Computational Approach to Finding Causal Economic Laws," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 105-136, October.
    15. Brunner, Karl & Meltzer, Allan H., 1976. "The Phillips curve," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 1-18, January.
    16. Hondroyiannis, George & Swamy, P.A.V.B. & Tavlas, George S., 2009. "The New Keynesian Phillips Curve In A Time-Varying Coefficient Environment: Some European Evidence," Macroeconomic Dynamics, Cambridge University Press, vol. 13(2), pages 149-166, April.
    17. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    18. Pratt, John W. & Schlaifer, Robert, 1988. "On the interpretation and observation of laws," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 23-52.
    19. Havenner, A. & Swamy, P. A. V. B., 1981. "A random coefficient approach to seasonal adjustment of economic time series," Journal of Econometrics, Elsevier, vol. 15(2), pages 177-209, February.
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    Cited by:

    1. Emilian DOBRESCU, 2017. "Modelling an Emergent Economy and Parameter Instability Problem," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-28, June.
    2. Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2017. "Self-fulfilling dynamics: The interactions of sovereign spreads, sovereign ratings and bank ratings during the euro financial crisis," Journal of International Money and Finance, Elsevier, vol. 73(PB), pages 371-385.
    3. Stephen G. Hall & Heather D. Gibson & G. S. Tavlas & Mike G. Tsionas, 2020. "A Monte Carlo Study of Time Varying Coefficient (TVC) Estimation," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 115-130, June.
    4. Ko, Byoung-Wook, 2018. "Dynamic patterns of dry bulk freight spot rates through the lens of a time-varying coefficient model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 319-330.
    5. Mamdouh Abdelmoula M. ABDELSALAM, 2017. "Improving Phillips Curve’s Inflation Forecasts under Misspecification," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 54-76, September.

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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