Every year has large demand and supply shifts associated with the seasons, regardless of the phase of the business cycle. Based on measures dating back to the 1940s, the seasonal shifts reject the hypotheses that demand shifts affect employment outcomes significantly more in recession years than...

Many recent papers have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these papers generally use ex post...

We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the benchmark to all the alternative models simultaneously rather than sequentially, and do...

Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is summarized by a small number of data series. However,...

This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to...

Regime-switching models are well suited to capture the non-linearities in interest rates. This paper examines the econometric performance of regime-switching models for interest rate data from the US, Germany and the UK. There is strong evidence supporting the presence of regime switches but...

In this paper, we quantify the changes in the relationship between international forces and many key US macroeconomic variables over the 1984-2005 period, and analyze changes in the monetary policy transmission mechanism. We do so by estimating a Factor-Augmented VAR on a large set of US and...

This paper develops a vector autoregression (VAR) for time series which are observed at mixed frequencies - quarterly and monthly. The model is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. We show how to evaluate the marginal data density to...

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables....

This paper revisits the important ideas proposed by Atkinson and Stiglitz's seminal 1969 paper on technological change. After linking these ideas to the induced innovation literature of the 1960s and the more recent directed technological change literature, it explains how these three...