Estimating the FOMC’s interest rate rule with variable selection and partial regime switching

Abstract

When studying the Federal Open Market Committee’s (FOMC’s) interest rate rule, some authors, such as Gonzalez-Astudillo [(2018) Journal of Monetary, Credit, and Banking 50(1), 115–154.], find evidence for changes in inflation and output gap responses. Others, such as Sims and Zha [(2006) American Economic Review 96(1), 54–81.], only find evidence for a change in the variance of the interest rate rule. In this paper, I develop a new two-regime Markov-switching model that probabilistically performs variable selection and identification of parameter change for each variable in the model. I find substantial evidence that there have been changes in the FOMC’s response to the unemployment gap and in the volatility of the rule. When the FOMC responds strongly to the unemployment gap, I find a bimodal density for the inflation response coefficient. Despite the bimodal density, there is a low probability that there have been changes in the FOMC’s response to inflation.

Timing and Signals of Monetary Regime Switching

“…This result leads us to conclude that the timing of switches will be dominated by the dynamics of variance in models estimated with a single regime variable governing both the coefficients and variance parameters. 18 Thus, in order to determine if there are changes in the coefficients (and therefore the response to inflation and output), a model must control for heteroskedasticity either through a separate regime-switching process or alternative time-varying volatility framework as in Check (2017). In terms of model fit, the MS-FD rule does a better job at matching the data than the simple rule and recession rule but performs worse than the baseline rule.…”