Energy consumption, survey data and the prediction of industrial production in Italy: a comparison and combination of different models

Marchetti, Domenico J.; Parigi, Giuseppe

doi:10.1002/1099-131x(200009)19:5<419::aid-for749>3.3.co;2-a

Cited by 8 publications

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“…Salazar and Weale (1999). Marchetti and Parigi (2000), and Simpson et al (2001). There are two kinds of multi-step ahead forecasting, the static one and dynamic one.…”

Section: Notesmentioning

confidence: 98%

“…First, we observed that various time series models have been used to predict industrial productions (e.g., Hsu et al, 2003;Marchetti & Parigi, 2000;Simpson, Osborn, & Sensier, 2001;Tseng et al, 1999). Second, we looked for a Bayesian multivariate time series model that fits unsteady environments better than traditional frequency-based models, and found that the non-informative diffuse-prior Bayesian vector autoregression (NDBVAR) model has good features: its prior is flexible and its computation is efficient.…”

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confidence: 99%

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Production Forecasting of Taiwan's Technology Industrial Cluster: A Bayesian Autoregression Approach

et al. 2005

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This study proposes a forecasting method that combines the clustering effect and non‐informative diffuse‐prior Bayesian vector autoregression (NDBVAR) model to forecast the productions of technology industries. Two empirical cases are examined to verify the proposed method: the semiconductor industry and computer manufacturing industry in Taiwan. It is found that the NDBVAR model outperforms the other three conventional time series models including the autoregression (AR), vector autoregression (VAR), and Litterman Bayesian VAR (LBVAR) models. Moreover, the NDBVAR model also outperforms the forecast reports from leading market information providers over the past several years. The forecasting method proposed is therefore concluded to be a feasible approach for production prediction, especially for technology industries in volatile environments. Résumé La présente étude propose une méthode prévisionnelle qui combine les effets de regroupement et le non‐informative diffuse‐prior Bayesian vector autoregression model (NDBVAR) pour prévoir les productions des industries de technologie. Pour évaluer la méthode proposée, l'étude examine deux cas empiriques: les industries taiwanaises du semiconducteur et de fabrication d'ordinateur. Elle révèle que le modèle NDBVAR est plus performant que les trois modèles conventionnels en série chronologique notamment le modèle d'autoregression (AR), le modèle de vecteur d'autoregression (VAR), et le modèle Litterman Bayesian (LBVAR). L'étude montre aussi qu'au cours des dernières années, les modèles NDBVAR ont été plus performants que les rapports prévisionnels des prestataires d'informations qui dominent le marché. Elle débouche sur la constatation que la méthode prévisionnelle proposée est une approche réalisable pour la prévision de la production, en particulier pour les industries de la technologie dans un environnement volatile.

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“…Salazar and Weale (1999). Marchetti and Parigi (2000), and Simpson et al (2001). There are two kinds of multi-step ahead forecasting, the static one and dynamic one.…”

Section: Notesmentioning

confidence: 98%

mentioning

confidence: 99%

Production Forecasting of Taiwan's Technology Industrial Cluster: A Bayesian Autoregression Approach

et al. 2005

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“…As outlined above, we choose a linear forecasting model following Marchetti and Parigi (2000) and Zizza (2002). In particular, the forecasting model for country i is of the form…”

Section: Empirical Application: Forecasting Industrial Productionmentioning

confidence: 99%

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

Feldkircher

2011

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. Terms of use: Documents in EconStor may AbstractIn this study we evaluate the forecast performance of model averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold: First the predictive likelihood does always better than the traditionally employed 'marginal' likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (rmse) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995) with a hold-out sample size of 25% to minimize the rmse (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries beating for almost all countries the AR (1) benchmark model.

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“…Encompassing tests were originally proposed by Chong and Hendry (1986) who, however, reserved little space for forecast combination within the encompassing paradigm and remarked on the importance of searching for the best model specification (see also Granger and Jeon, 2004). Instead, Diebold (1989) emphasized the aim of reconciling forecast combination and encompassing, which is also underlined by Hallman and Kamstra (1989), Donaldson and Kamstra (1996), Harvey et al (1999), Marchetti and Parigi (2000), Fang (2003), Marcellino (2008), Clements and Hendry (2011) and McMillan and Speight (2012), among others. These studies consider encompassing testing procedures for two forecasts such as those by Fair and Shiller (1989), Harvey et al (1998), West (2001) and Clark and McCracken (2001).…”

Section: Introductionmentioning

confidence: 99%

Forecast Combinations in a DSGE‐VAR Lab

Costantini

Gunter

Kunst

2016

Journal of Forecasting

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We explore the benefits of forecast combinations based on forecastencompassing tests compared to simple averages and to Bates-Granger combinations. We also consider a new combination method that fuses test-based and Bates-Granger weighting. For a realistic simulation design, we generate multivariate time-series samples from a macroeconomic DSGE-VAR model. Results generally support Bates-Granger over uniform weighting, whereas benefits of test-based weights depend on the sample size and on the prediction horizon. In a corresponding application to real-world data, simple averaging performs best. Uniform averages may be the weighting scheme that is most robust to empirically observed irregularities.

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Energy consumption, survey data and the prediction of industrial production in Italy: a comparison and combination of different models

Cited by 8 publications

References 0 publications

Production Forecasting of Taiwan's Technology Industrial Cluster: A Bayesian Autoregression Approach

Production Forecasting of Taiwan's Technology Industrial Cluster: A Bayesian Autoregression Approach

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

Forecast Combinations in a DSGE‐VAR Lab

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