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K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation?

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  • Kaufmann, Sylvia

Abstract

Two Bayesian sampling schemes are outlined to estimate a time-varying Markov switching transition distribution. Using data augmentation transforms the non-linear, non-normal logit transition model into a linear-normal one. A partial representation of the difference in random utility model in combination with random permutation sampling provides highest sampling efficiency. The level of the covariate in the transition distribution which balances the persistence across states is defined to be the threshold level. For illustration, we estimate a two-pillar Phillips curve for the euro area, in which loan growth affects the transition distribution.

Suggested Citation

  • Kaufmann, Sylvia, 2015. "K-state switching models with time-varying transition distributions—Does loan growth signal stronger effects of variables on inflation?," Journal of Econometrics, Elsevier, vol. 187(1), pages 82-94.
    • Handle: RePEc:eee:econom:v:187:y:2015:i:1:p:82-94
    DOI: 10.1016/j.jeconom.2015.02.001
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    More about this item

    Keywords

    Bayesian analysis; Time-varying Markov transition; Permutation sampling; Phillips curve; Threshold level;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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