Inferences about structural instability in macroeconomics

I investigate the problem of making inferences about nonlinearity and volatility change in macroeconomic time series from both the theoretical and econometric perspectives.;In the first essay, I estimate DSGE models with recurring regime changes in monetary policy (inflation target and reaction coefficients), technology (growth rate and volatility), and/or nominal price rigidities. In the models, agents are assumed to know deep parameter values but make probabilistic inference about prevailing and future regimes based on Bayes' rule. I develop an estimation method that takes these probabilistic inferences into account when relating state variables to observed data. In an application to postwar U.S. data, I find stronger support for regime switching in monetary policy than in technology or nominal rigidities. In addition, a model with regime switching policy that conforms to the long-run Taylor principle given in Davig and Leeper (2007) is preferred to a determinacy- indeterminacy model motivated by Lubik and Schorfheide (2004). These empirical results indicate that, even though a passive policy regime produced more volatility in the economy from the early 1970s to the mid-1980s, the economy can be explained by determinacy over the entire postwar period, implying no role for sunspot shocks in explaining the changes in volatility.;The second essay proposes a new approach to constructing confidence sets for the timing of structural breaks. This approach involves using Markov-chain Monte Carlo methods to simulate the marginal "fiducial" distributions of break dates from the likelihood function. We compare our proposed approach to asymptotic and bootstrap confidence sets and find that it has the best overall finite-sample performance in terms of producing short confidence sets with accurate coverage rates.;In the third essay, I study how to make inference about complicated patterns of structural breaks in time series. For example, multiple groups of parameters (e.g., intercept, persistence, and conditional variance) have structural breaks independently at different dates. In this essay, I extend Chib's (1998) algorithm in which a Markov-chain transition matrix governs the change-point structure to allow for breaks at different dates in an efficient and tractable way. In particular, I do this by adding as many transition matrices as necessary for the parameter groups. I apply this approach to postwar U.S. inflation and find support for an autoregressive model with multiple structural breaks in intercept (higher around 1965), persistence (lower around the mid-1980s), and conditional variance (higher around 1968 and lower again around the mid-1980s).