Inflation persistence or ‘inertia’ has important consequences for monetary policy. Inflation which does not get eliminated today tends to persist entailing higher costs in the future. In turn, a monetary regime can exert significant influence on inflation persistence by its credibility and...

Large-scale inference has become increasingly popular in financial economics. I explore an empirical Bayes approach to large-scale multiple testing. The proposed approach bases its inference on the posterior probability that the null is true given the observed data. It provides a convenient way...

This paper provides a new, unified, and flexible framework to measure and characterize a convergence process. Specifically, we formally define a general notion of asymptotic price convergence, which encompasses convergence as steady-state and catching-up, and propose a model to represent a...

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...

This chapter reviews the principal methods used by researchers when forecasting seasonal time series. In addition, the often overlooked implications of forecasting and feedback for seasonal adjustment are discussed. After an introduction in Section 1, Section 2 examines traditional univariate...

Seasonal patterns in economic time series are generally examined from a univariate point of view. Using extensions of the unit root literature, important classes of seasonal processes are deterministic, stationary stochastic or mean reverting, and unit root stochastic. Time series tests have...

We propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modeled series. We first show that the Bayesian vs frequentist small sample bias controversy is driven by different default initial conditions. These initial conditions are...

We develop a new simultaneous time series model for volatility and dependence with long memory (fractionally integrated) dynamics and heavy-tailed densities. Our new multivariate model accounts for typical empirical features in financial time series while being robust to outliers or jumps in the...

Hedge Fund returns are often highly serially correlated mainly due to illiquidity exposures given that investments in such securities tend to be inactively traded and associated market prices are not always readily available. Following that, observed returns of such alternative investments tend...