Common approaches to test for the economic value of directional forecasts are based on the classical Chi-square test for independence, Fisher’s exact test or the Pesaran and Timmerman (1992) test for market timing. These tests are asymptotically valid for serially independent observations....

I construct risk-corrected approximations of the policy functions of DSGEmodels around the stochastic steady state and ergodic mean that are linear in the state variables. The resulting approximations are uniformly more accurate than standard linear approximations and capture the dynamics of...

We introduce a nonlinear infinite moving average as an alternative to the standard state-space policy function for solving nonlinear DSGE models. Perturbation of the nonlinear moving average policy function provides a direct mapping from a history of innovations to endogenous variables,...

We prove that standard regularity and saddle stability assumptions for linear approximations are sufficient to guarantee the existence of a unique solution for all undetermined coefficients of nonlinear perturbations of arbitrary order to discrete time DSGE models. We derive the perturbation...

It is commonly accepted that information is helpful if it can be exploited to improve a decision mak- ing process. In economics, decisions are often based on forecasts of up{ or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework...

I compare the performance of solution methods in solving a standard real business cycle model with labor market search frictions. Under the conventional calibration, the model is solved by the projection method using the Chebyshev polynomials as its basis, and the perturbation methods up to...

In this paper we adopt a principal components analysis (PCA) to reduce the dimensionality of the term structure and employ autoregressive models (AR) to forecast principal components which, in turn, are used to forecast swap rates. Arguing in favor of structural variation, we propose data...

This paper proposes a novel approach to the combination of conditional covariance matrix forecasts based on the use of the Generalized Method of Moments (GMM). It is shown how the procedure can be generalized to deal with large dimensional systems by means of a two-step strategy. The finite...

Population forecasts are crucial for many social, political and economic decisions. Official population projections rely in general on deterministic models which use different scenarios for future vital rates to indicate uncertainty. However, this technique shows substantial weak points such as...

Risk management technology applied to high dimensional portfolios needs simple and fast methods for calculation of Value-at-Risk (VaR). The multivariate normal framework provides a simple off-the-shelf methodology but lacks the heavy tailed distributional properties that are observed in data. A...