Mode Identification of Volatility in Time-Varying Autoregression

Mode Identification of Volatility in Time-varying Autoregression

In many applications, time series exhibit non-stationary behavior that might reasonably be modeled as a time-varying autoregressive process. In the context of such a model, we discuss the problem of testing for modality of the variance function. We propose a test of modality which is local, and which when used iteratively, can be used to identify the total number of modes in a given series. Thi...

Monitoring Time-Varying Parameters in an Autoregression

Aggregation, Heterogeneous Autoregression and Volatility of

Bootstrapping autoregression under non-stationary volatility

This paper studies robust inference in autoregression around a polynomial trend with stable autoregressive roots under non-stationary volatility. The formulation of the volatility process is quite general including many existing deterministic and stochastic non-stationary volatility specifications. The aim of the paper is two-fold. First, it develops a limit theory for least squares estimators ...

Learning and Time-Varying Macroeconomic Volatility

This paper presents a DSGE model in which agents’ learning about the economy can endogenously generate time-varying macroeconomic volatility. Economic agents use simple models to form expectations and need to learn the relevant parameters. Their gain coefficient is endogenous and is adjusted according to past forecast errors. The model is estimated using likelihood-based Bayesian methods. The e...