We develop a specification test of predictive densities based on that the generalized residuals of correctly specified predictive density models are i.i.d. uniform. The proposed sequential test examines the hypotheses of serial independence and uniformity in two stages, wherein the first stage test of serial independence is robust to violation of uniformity. The approach of data driven smooth t...

The paper considers the conjecture that forecasts from preferred economic models or theories d-separate forecasts from less preferred models or theories from the actual realization of the variable for which a scientific explanation is sought. Dseparation provides a succinct notion to represent forecast dominance of one set of forecasts over another; it provides, as well, a criterion for model p...

This paper deals with the problem of estimation and prediction in a compound Poisson ECOGARCH(1, 1) model. For this we construct a quasi maximum likelihood estimator under the assumption that all jumps of the log-price process are observable. Since these jumps occur at unequally spaced time points, it is clear that the estimator has to be computed for irregularly spaced data. Assuming normally ...

Recent time series methods are applied to the problem of forecasting New Zealand’s real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-deter...

This paper analyzes the ability of both economic variables and moving-average rules to forecast the monthly U.S. equity premium using out-of-sample tests for 1960–2008. Both approaches provide statistically and economically significant out-of-sample forecasting gains, which are concentrated in U.S. business-cycle recessions. Nevertheless, economic variables and moving-average rules capture diff...

This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VAR...

We propose a simple but effective estimation procedure to extract the level and the volatility dynamics of a latent macroeconomic factor from a panel of observable indicators. Our approach is based on a multivariate conditionally heteroskedastic exact factor model that can take into account the heteroskedasticity feature shown by most macroeconomic variables and relies on an iterated Kalman fil...

This article offers a comparison of short-term forecasting ability of five demand systems with an application to U+S+ meat consumption+ Four static demand systems ~AIDS, Rotterdam, AIM, and DGM! and a dynamic Vector Error Correction Model ~VECM! are considered+ We tested the equality of mean square forecast errors+ We also investigated the possibility of forecast encompassing among competing mo...

This paper addresses the following issue: given a set of daily observations on an asset (historical opening, closing, high and low prices), how should one go about estimating the asset’s volatility? We use high-frequency data on very liquid assets to construct daily realized volatility series, which enables us to treat volatility as observed rather than latent. We then compare the empirical per...

Many authors have documented that it is challenging to explain exchange rate fluctuations with macroeconomic fundamentals: a random walk forecasts future exchange rates better than existing macroeconomic models. This paper applies newly developed tests for nested model that are robust to the presence of parameter instability. The empirical evidence shows that for some countries we can reject th...