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Open AccessJournal ArticleDOI

Forecasting Some Low-Predictability Time Series Using Diffusion Indices

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TLDR
In this paper, the authors consider the application of diffusion index forecasting models to the problem of forecasting the growth rates of real output and real investment, and find gains in forecast accuracy at short horizons from the diffusion index models.
Abstract
The growth rates of real output and real investment are two macroeconomic time series which are particularly difficult to forecast. This paper considers the application of diffusion index forecasting models to this problem. We begin by characterizing the performance of standard forecasts, via recently-introduced measures of predictability and the forecast content, noting the maximum horizon at which the forecasts have value. We then compare diffusion index forecasts with a variety of alternatives, including the forecasts made by the OECD. We find gains in forecast accuracy at short horizons from the diffusion index models, but do not find evidence that the maximum horizon for forecasts can be extended in this way. Copyright © 2003 John Wiley & Sons, Ltd.

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Forecasting with Many Predictors

TL;DR: In this article, a survey of time series forecasting methods that exploit many predictors is presented, including forecast combination, forecast pooling, and Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model.
Book ChapterDOI

Chapter 10 Forecasting with Many Predictors

TL;DR: In this paper, a survey of time series forecasting methods that exploit many predictors is presented, including forecast combination, forecast pooling, and Bayesian model averaging, in which the forecasts from very many models, which differ in their constituent variables, are averaged based on the posterior probability assigned to each model.
Posted Content

Forecasting German GDP Using Alternative Factor Models Based on Large Datasets

TL;DR: Neither of the dynamic factor models can provide better forecasts than the static model over all forecast horizons and different specifications of the simulation design, so the application of theynamic factor models seems to provide only small forecasting improvements over the static factor model for forecasting German GDP.
Journal ArticleDOI

How Successful are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach

TL;DR: In this article, a meta-analysis of factor forecast applications for output and inflation is presented, which assesses the determinants of the forecast performance of large factor models relative to other models.
Journal ArticleDOI

Forecasting German GDP using alternative factor models based on large datasets

TL;DR: In this article, the forecasting performance of alternative factor models based on a large panel of quarterly time series for the German economy is discussed, and it is shown that the dynamic principal component model and subspace factor model outperform the static factor model in most cases in terms of mean-squared forecast error.
References
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Comparing Predictive Accuracy

TL;DR: The authors describes the advantages of these studies and suggests how they can be improved and also provides aids in judging the validity of inferences they draw, such as multiple treatment and comparison groups and multiple pre- or post-intervention observations.
ReportDOI

Comparing Predictive Accuracy

TL;DR: In this article, explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts are proposed and evaluated, and asymptotic and exact finite-sample tests are proposed, evaluated and illustrated.
Journal ArticleDOI

Macroeconomic Forecasting Using Diffusion Indexes

TL;DR: This paper used principal component analysis (PCA) to predict macroeconomic time series variable using a large number of predictors, and the predictors were summarized using a small number of indexes constructed by principal component analyzer.
Journal ArticleDOI

Testing the equality of prediction mean squared errors

TL;DR: In this article, the authors analyse the behaviour of two possible tests, and of modifications of these tests designed to circumvent shortcomings in the original formulations, and make a recommendation for one particular testing approach for practical applications.
Journal ArticleDOI

Asymptotic Inference about Predictive Ability

Kenneth D. West
- 01 Sep 1996 - 
TL;DR: The authors developed procedures for inference about the moments of smooth functions of out-of-sample predictions and prediction errors, when there is a long time series of predictions and realizations, and provided tools for analysis of predictive accuracy and efficiency, and more generally, of predictive ability.
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