4,688 research outputs found
Tests of equal predictive ability with real-time data
This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multi-step predictions from both non-nested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstances. The paper concludes with an examination of the real-time predictive content of various measures of economic activity for inflation.Economic forecasting ; Real-time data
Asymptotically minimax Bayes predictive densities
Given a random sample from a distribution with density function that depends
on an unknown parameter , we are interested in accurately estimating
the true parametric density function at a future observation from the same
distribution. The asymptotic risk of Bayes predictive density estimates with
Kullback--Leibler loss function is used to examine various ways of choosing prior
distributions; the principal type of choice studied is minimax. We seek
asymptotically least favorable predictive densities for which the corresponding
asymptotic risk is minimax. A result resembling Stein's paradox for estimating
normal means by the maximum likelihood holds for the uniform prior in the
multivariate location family case: when the dimensionality of the model is at
least three, the Jeffreys prior is minimax, though inadmissible. The Jeffreys
prior is both admissible and minimax for one- and two-dimensional location
problems.Comment: Published at http://dx.doi.org/10.1214/009053606000000885 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Advances in forecast evaluation
This paper surveys recent developments in the evaluation of point forecasts. Taking West’s (2006) survey as a starting point, we briefly cover the state of the literature as of the time of West’s writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coefficients), the evaluation of forecasts in the finite sample (based on estimated model coefficients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power in determining the split of a sample into in-sample and out-of-sample portions; whether the accuracy of inference in evaluation of multistep forecasts can be improved with the judicious choice of HAC estimator (it can); and the extension of West’s (1996) theory results for population-level, unconditional forecast evaluation to the case of conditional forecast evaluation.Forecasting ; Time-series analysis
Advances in forecast evaluation
This paper surveys recent developments in the evaluation of point forecasts. Taking West's (2006) survey as a starting point, we briefly cover the state of the literature as of the time of West's writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coefficients), the evaluation of forecasts in the finite sample (based on estimated model coefficients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power in determining the split of a sample into in-sample and out-of-sample portions; whether the accuracy of inference in evaluation of multi-step forecasts can be improved with judicious choice of HAC estimator (it can); and the extension of West's (1996) theory results for population-level, unconditional forecast evaluation to the case of conditional forecast evaluation.Forecasting
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