Methods for measuring expectations and uncertainty in Markov-switching models

Modeling the Evolution of Expectations and Uncertainty in General Equilibrium

“…Uncertainty at horizon h of one, four, and 20 quarters is measured using the standard deviation of the variable of interest at time conditional on agents' information set at time t , . It is worth emphasizing that this measure of uncertainty is computed taking into account the possibility of regime changes and the evolution of agents' beliefs, using the methods described in Bianchi (). First, it should be observed that when agents are mostly convinced to be in the short‐lasting high growth regime (i.e., in the narrow white areas or at the beginning of the broad white areas), uncertainty is generally higher and remarkably similar at all horizons.…”

“…This sluggish adjustment of public expectations is hard to reproduce through rational expectations models in which the functioning of the whole economy is common knowledge among agents. Furthermore, the methods introduced in this article can be combined with techniques developed by Bianchi () to obtain an analytical characterization of the evolution of uncertainty. Bianchi () shows how to compute measures of expectations and uncertainty in Markov‐switching models with perfect information.…”

“…Furthermore, the methods introduced in this article can be combined with techniques developed by Bianchi () to obtain an analytical characterization of the evolution of uncertainty. Bianchi () shows how to compute measures of expectations and uncertainty in Markov‐switching models with perfect information. The combination of these methods endows researchers with a convenient toolkit to take dynamic general equilibrium rational expectations models to the growing amount of data on macroeconomic uncertainty.…”

Monetary/Fiscal policy mix and agents' beliefs

“…In this respect, the paper is related to the growing literature that allows for parameter instability in DSGE models. Justiniano and Primiceri (2008) allow for heteroskedasticity, while Schorfheide (2005), Liu, Waggoner, and Zha (2011), Bianchi (2013b), Doh (2013), Fernandez-Villaverde, Guerron-Quintana, andRubio-Ramirez (2010), and Baele, Bekaert, Cho, Inghelbrecht, and Moreno (2011) also model changes in the parameters of the Taylor rule or the in ‡ation target. Coibion and Gorodichenko (2011) study the consequences of the high trend in ‡ation of the '70s for price determinacy.…”

Identification of DSGE models—The effect of higher-order approximation and pruning

“…A further extension would be to establish rank criteria for other DSGE model specifications, as long as we are able to calculate moments or the spectrum of the datagenerating process. For instance, Bianchi (2013) derives analytical moments for Markov switching models, which can be used in a similar fashion to check identification via rank criteria for Markov switching DSGE models. Identification results for M 2 (left) and G 2 (right) for 100 draws from the prior domain using analytical derivatives with robust tolerance level, T ¼ 30 and N ¼10,000.…”