Forecast Uncertainties in Macroeconomic Modeling

Garratt, Anthony; Lee, Kevin; Pesaran, M. Hashem; Shin, Yongcheol

doi:10.1198/016214503000000765

Cited by 155 publications

(90 citation statements)

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“…We use the Probability Integral Transform (PIT) technique originally introduced by Rosenblatt (1952) and more recently proposed by Diebold, Gunther and Tay (1998) to evaluate the correct speci…cation of predictive densities. Corradi and Swanson (2006b) provide a comprehensive recent overview of tests for predictive density evaluation; Granger and Pesaran (2000) and Garratt, Lee, Pesaran and Shin (2003) further complement the discussion. The di¤erence between this paper and those in the literature is that we operate in a data-rich environment using the extensive data set of Stock and Watson (2003), as well as the wide range of evaluation techniques we use.…”

Section: Introductionmentioning

confidence: 99%

Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set

Rossi

Sekhposyan

2014

International Journal of Forecasting

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We evaluate conditional predictive densities for U.S. output growth and in ‡ation using a number of commonly used forecasting models that rely on a large number of macroeconomic predictors. More speci…cally, we evaluate how well conditional predictive densities based on the commonly used normality assumption …t actual realizations out-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can improve or deteriorate point forecasts, they might have the opposite e¤ect on higher moments. We …nd that normality is rejected for most models in some dimension according to at least one of the tests we use. Interestingly, however, combinations of predictive densities appear to be correctly approximated by a normal density: the simple, equal average when predicting output growth and Bayesian model average when predicting in ‡ation.

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Section: Introductionmentioning

confidence: 99%

Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set

Rossi

Sekhposyan

2014

International Journal of Forecasting

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“…The latter was already mentioned in Leamer (1978) and recently applied to economic problems in e.g. Fernández et al (2001) (growth regressions) and in Garratt et al (2003) and Jacobson and Karlsson (2004) for macroeconomic forecasting.…”

Section: Bayesian Methodsmentioning

confidence: 88%

Bayesian time series analysis

Steel¹

The New Palgrave Dictionary of Economics

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This article describes the use of Bayesian methods in the statistical analysis of time series. The use of Markov chain Monte Carlo methods has made even the more complex time series models amenable to Bayesian analysis. Models discussed in some detail are ARIMA models and their fractionally integrated counterparts, state-space models, Markov switching and mixture models, and models allowing for timevarying volatility. A final section reviews some recent approaches to nonparametric Bayesian modelling of time series. Bayesian methodsThe importance of Bayesian methods in econometrics has increased rapidly over the last decade. This is, no doubt, fuelled by an increasing appreciation of the advantages that Bayesian inference entails. In particular, it provides us with a formal way to incorporate the prior information we often possess before seeing the data, it fits perfectly with sequential learning and decision making and it directly leads to exact small sample results. In addition, the Bayesian paradigm is particularly natural for prediction, taking into account all parameter or even model uncertainty. The predictive distribution is the sampling distribution where the parameters are integrated out with the posterior distribution and is exactly what we need for forecasting, often a key goal of time-series analysis.Usually, the choice of a particular econometric model is not prespecified by theory and many competing models can be entertained. Comparing models can be done formally in a Bayesian framework through so-called posterior odds, which is the product of the prior odds and the Bayes factor. The Bayes factor between any two models is the ratio of the likelihoods integrated out with the corresponding prior and summarizes how the data favour one model over another. Given a set of possible models, this immediately leads to posterior model probabilities. Rather than choosing a single model, a natural way to deal with model uncertainty is to use the posterior model probabilities to average out the inference

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“…In measuring uncertainty parameter estimation errors and uncertainty about the form of the model (for example, see Garratt, Lee, Pesaran, and Shin (2003), Wallis (2008)) are not taken into account in this study. However, the basic model used here allows to capture structural shifts in the economy (cf.…”

Section: Inflation Forecasting and Inflation Uncertaintymentioning

confidence: 99%

The Links between Inﬂation and Inﬂation Uncertainty at the Longer Horizon

Tsyplakov

2010

SSRN Journal

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In this paper I examine the Okun-Friedman hypothesis of the link between inflation and inflation uncertainty using historical international data on the monthly CPI. An indicator of inflation uncertainty at the two-years-ahead horizon is derived from a time-series model of inflation with time-varying parameters by means of Monte Carlo simulations. This indicator is compared to other uncertainty measures, with the short forecast horizon and based on simpler GARCH-type models. The analysis convincingly demonstrates that both the longer horizon and changing parameters are important for the regularity. The evidence obtained strongly supports the Okun-Friedman hypothesis both in the time dimension for most countries and across countries.

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Forecast Uncertainties in Macroeconomic Modeling

Cited by 155 publications

References 1 publication

Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set

Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set

Bayesian time series analysis

The Links between Inﬂation and Inﬂation Uncertainty at the Longer Horizon

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