Estimation of Markov regime-switching regression models with endogenous switching

By Chang-Jin Kim, Jeremy Piger, R. Startz. 2003

261 citations recorded by the source corpus.

Following Hamilton [1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357-384], estimation of Markov regime-switching regressions typically relies on the assumption that the latent state variable controlling regime change is exogenous. We relax this assumption and develop a parsimonious model of endogenous Markov regime-switching. Inference via maximum likelihood estimation is possible with relatively minor modifications to existing recursive filters. The model nests the exogenous switching model, yielding straightforward tests for endogeneity. In Monte Carlo experiments, maximum likelihood estimates of the endogenous switching model parameters were quite accurate, even in the presence of certain model misspecifications. As an application, we extend the volatility feedback model of equity returns given in Turner et al. [1989. A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics 25, 3-22] to allow for endogenous switching.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/891e23cd34aee52e13659a2cceb28a9e33b7aaff

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Allowance Price Drivers in the First Phase of the EU ETS (2010, background): “Such tests in principle allow for detection of stochastically crashing bubbles that are difficult to detect otherwise [40, 41, 42, 43].”
A new approach to model regime switching (2017, methodology): “Kim et al. (2008) consider a regression model similar to our model in (4), yet they set the state dependent regression coefficients (βt) to be dependent only on the current state variable (st).”
Markov-Switching MIDAS Models (2013, methodology): “This assumption was originally relaxed by Filardo (1994), who used timevarying transition probabilities modeled as a logistic function, while Kim, Piger, and Startz (2008) modeled them as a probit function.”