Survey data of forecasts of the housing market may provide a particularly rich data nvironment for researchers and policymakers to study developments in housing markets. Based on the approach advanced by Elliott et al. (Rev. Ec. Studies. 72, 1197-1125, 2005), we studied the properties of a large...

We propose an integrated treatment of the problems of optimal monetary and fiscal policy, for an economy in which prices are sticky and the only available sources of government revenue are distorting taxes. Our linear-quadratic approach allows us to nest both conventional analyses of optimal...

This article studies the process from data acquisition to policy decision in relation to an optimum policy on global warming. Policymakers must be reasonably skeptical before proposing remedies to curb warming, but policymakers cannot await the final proof of any proposal's merit. Balancing...

We derive the Bayes estimator of vectors of structural VAR impulse responses under a range of alternative loss functions. We also derive joint credible regions for vectors of impulse responses as the lowest posterior risk region under the same loss functions. We show that conventional impulse...

It is known that the classical ruin function under exponential claim-size distribution depends on two parameters, which are referred to as the mean claim size and the relative security loading. These parameters are assumed to be unknown and random, thus, a loss function that measures the loss...

We measure the tone (sentiment) of the ECB's Governing Council regarding economic outlook at the time of each monetary policy meeting and use this information together with the Eurosystem/ECB staff macroeconomic projections to directly estimate the Governing Council's loss function. Our results...

Several recent studies have expressed concern that the Haar prior typically imposed in estimating sign-identified VAR models may be unintentionally informative about the implied prior for the structural impulse responses. This question is indeed important, but we show that the tools that have...

We develop metrics based on Shapley values for interpreting time-series forecasting models, including "black-box" models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...

Point forecasts can be interpreted as functionals (i.e., point summaries) of predictive distributions. We extend methodology for the identification of the functional based on time series of point forecasts and associated realizations. Focusing on state‐dependent quantiles and expectiles, we...