Much of empirical economics involves regression analysis. However, does the presentation of results affect economists ability to make inferences for decision making purposes? In a survey, 257 academic economists were asked to make probabilistic inferences on the basis of the outputs of a...

We investigate the relationship between monetary policy and inflation dynamics in the US using a medium scale structural model. The specification is estimated with Bayesian techniques and fits the data reasonably well. Policy shocks account for a part of the decline in inflation volatility;...

We propose new methods for evaluating predictive densities. The methods include Kolmogorov-Smirnov and Cramer-von Mises-type tests for the correct specification of predictive densities robust to dynamic mis-specification. The novelty is that the tests can detect mis-specification in the...

We evaluate conditional predictive densities for U.S. output growth and inflation using a number of commonly used forecasting models that rely on a large number of macroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used...

While forecasting is a common practice in academia, government and business alike, practitioners are often left wondering how to choose the sample for estimating forecasting models. When we forecast inflation in 2014, for example, should we use the last 30 years of data or the last 10 years of...

The goal of this paper is to develop formal tests to evaluate the relative in-sample performance of two competing, misspecified, non-nested models in the presence of possible data instability. Compared to previous approaches to model selection, which are based on measures of global performance,...

In this paper, we ask whether it is possible to forecast gross value-added (GVA) and its sectoral subcomponents at the regional level. With an autoregressive distributed lagmodel we forecast total and sectoral GVA for one German state (Saxony) with more than 300 indicators from different...

Forecast models with large cross-sections are often subject to overparameterization leading to unstable parameter estimates and hence inaccurate forecasts. Recent articlessuggest that a large Bayesian vector autoregression (BVAR) with sufficient prior information dominates competing approaches....

This review provides an overview of forecasting methods that can help researchers forecast in the presence of non-stationarities caused by instabilities. The emphasis of the review is both theoretical and applied, and provides several examples of interest to economists. We show that modeling...

The aim of the present work is to test the predictive power of the term spread in forecasting real economic growth rates and recession probabilities in Italy. According to the most recent literature, the relationship between the term spread and economic growth rates is modelled as a nonlinear...