A framework for decomposing shocks and measuring volatilities derived from multi-dimensional panel data of survey forecasts

Davies, Antony N.

doi:10.1016/j.ijforecast.2005.09.007

Cited by 16 publications

(11 citation statements)

References 13 publications

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“…The first and third components of the forecast error are specific to individual forecasters, separating a possible systematic effect or individual bias i φ from an idiosyncratic non-autocorrelated error, ith ε , which might reflect individual sentiment or "animal spirits", and mishandling of or inadequacies in the initial information set. The extension by Davies (2006) relaxes the assumption that the individual forecaster's bias i φ is independent of the forecast horizon h, so the decomposition becomes t ith ih th ith A F…”

Section: The Econometric Frameworkmentioning

confidence: 99%

Evaluating a three-dimensional panel of point forecasts: The Bank of England Survey of External Forecasters

Boero

Smith

Wallis

2008

International Journal of Forecasting

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This article provides a first analysis of the forecasts of inflation and GDP growth obtained from the Bank of England's Survey of External Forecasters, considering both the survey average forecasts published in the quarterly Inflation Report, and the individual survey responses, recently made available by the Bank. These comprise a conventional incomplete panel dataset, with an additional dimension arising from the collection of forecasts at several horizons; both point forecasts and density forecasts are collected. The inflation forecasts show good performance in tests of unbiasedness and efficiency, albeit over a relatively calm period for the UK economy, and there is considerable individual heterogeneity. For GDP growth, inaccurate real-time data and their subsequent revision are seen to cause serious difficulties for forecast construction and evaluation, although the forecasts are again unbiased. There is evidence that some forecasters have asymmetric loss functions.

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Section: The Econometric Frameworkmentioning

confidence: 99%

Evaluating a three-dimensional panel of point forecasts: The Bank of England Survey of External Forecasters

Boero

Smith

Wallis

2008

International Journal of Forecasting

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“…We also assume a publication lag of one month, which means that the December outcome becomes available in January. 7 A somewhat similar decomposition for analyzing forecast errors in a panel data setting has been used by Davies and Lahiri (1995), Davies (2006) and Boero et al (2008) with the difference that they do not adjust for timing.…”

Section: The Econometric Modelmentioning

confidence: 99%

Adjusting for information content when comparing forecast performance

Andersson¹,

Aranki²,

Reslow

2017

Journal of Forecasting

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Working Paper series presents reports on matters in the sphere of activities of the Riksbank that are considered to be of interest to a wider public. Terms of use: Documents inThe papers are to be regarded as reports on ongoing studies and the authors will be pleased to receive comments.The opinions expressed in this article are the sole responsibility of the author(s) and should not be interpreted as reflecting the views of Sveriges Riksbank. Adjusting for Information Content when Comparing ForecastSveriges Riksbank Working Paper Series No. 328Abstract Cross institutional forecast evaluations may be severely distorted by the fact that forecasts are made at different points in time, and thus with different amount of information. This paper proposes a method to account for these differences. The method computes the timing effect and the forecaster's ability simultaneously. Monte Carlo simulation demonstrate that evaluations that do not adjust for the differences in information content may be misleading. In addition, the method is applied on a real-world data set of 10 Swedish forecasters for the period 1999-2015. The results show that the ranking of the forecasters is affected by the proposed adjustment.

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“…This is also important in determining the correct expression for aggregate forecast uncertainty based on such panel of forecasts. Davies (2006) describes three types of shocks: cumulative shocks, cross-sectional shocks, and discrete shocks.…”

Section: Measuring Shocks Volatilities and Anticipated Changesmentioning

confidence: 99%

“…In this chapter we have summarized such a framework developed in Davies andLahiri (1995, 1999), and illustrated some of the uses of these multi-dimensional panel data. In particular, we have characterized the adaptive expectations mechanism in the context of broader rational and implicit expectations hypotheses, and suggested ways of testing one hypothesis over the others.…”

Section: Rationality Testsmentioning

confidence: 99%