We consider time series forecasting in the presence of ongoing structural change where both the time series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially...

This paper revisits a number of data-rich prediction methods that are widely used in macroeconomic forecasting, such as factor models and Bayesian shrinkage regression, and compares these methods with a lesser known alternative: partial least squares regression. In this method, linear,...

This paper considers the stationarity properties of a variety of financial variables using statistical tests for strict stationarity. We find that there has been a gradual shift in unconditional variances for the variables examined during the 90's and 2000's and that this is the main cause of...

This paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the US data set constructed by Smets and Wouters. We use an indirect inference method to map from this TV VAR to time variation in implied dynamic stochastic general equilibrium...