Thick modeling

Granger, Clive W. J.; Jeon, Yongil

doi:10.1016/s0264-9993(03)00017-8

Cited by 199 publications

(138 citation statements)

References 18 publications

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“…For example, considering the MAPE indicator in Figure 4, the best performing combination for the least volatile load period 6 and for the peak load period 38 is obtained with the models TVR, MS and ARMAX. This agrees with previous research: it has been argued that, rather than combining the full set of forecasts, it is often advantageous to discard the models with the worst performance (see, for instance, Aiolfi and Favero, 2005;Granger and Jeon, 2004;Marcellino, 2004;Watson, 2001, 2004). However, in our study some exceptions emerge when the worst predictive model is the TVR.…”

Section: Ex Post Analysessupporting

confidence: 91%

Combining day-ahead forecasts for British electricity prices

et al. 2013

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This paper considers how well the approach of combining forecasts extends to the context of electricity prices. With the increasing popularity of regime switching and time-varying parameter models for predicting power prices, the multi model and evolutionary considerations that usually support the combining of simpler time series methods may be less applicable when the individual models incorporate these features. We address this question with a backtesting analysis on British day-ahead prices. Furthermore, given the volatility of power prices and concerns about accurate forecasting under extreme price excursions, we evaluate the results using various error metrics including expected shortfall. The comparisons are furthermore carefully simulated to consider model selection uncertainty in order to realistically test the value of combining as an ex ante policy. Overall, our results support combining for both accurate operational planning and risk management.

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Section: Ex Post Analysessupporting

confidence: 91%

Combining day-ahead forecasts for British electricity prices

et al. 2013

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“…Finally, although disaggregations are based on different information sets, the forecast errors for different periods do not differ in a relevant way between the two approaches; therefore, the combination of the forecasts derived from both approaches, based on the average of the forecasts following Granger and Jeon (2004), makes sense and yields the best results (see the last column in Table 3), suggesting that both disaggregations matter. Table 4 shows the forecast errors for the year-on-year rates of total HICP derived from the two approaches that take into account the disaggregation by the combined criterion of sectors and countries.…”

Section: Forecasting Inflation In the Euro Areamentioning

confidence: 99%

Econometric modelling for short‐term inflation forecasting in the euro area

Espasa

Albacete

2007

Journal of Forecasting

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This paper examines the problem of forecasting macro-variables which are observed monthly (or quarterly) and result from geographical and sectorial aggregation. The aim is to formulate a methodology whereby all relevant information gathered in this context could provide more accurate forecasts, be frequently updated, and include a disaggregated explanation as useful information for decision-making. The appropriate treatment of the resulting disaggregated data set requires vector modelling, which captures the long-run restrictions between the different time series and the shortterm correlations existing between their stationary transformations. Frequently, due to a lack of degrees of freedom, the vector model must be restricted to a block-diagonal vector model. This methodology is applied in this paper to inflation in the euro area, and shows that disaggregated models with cointegration restrictions improve accuracy in forecasting aggregate macro-variables.

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“…Alternatively, we use a "thick" modeling approach (Granger and Jeon, 2004;Aiolfi and Favero, 2005;Rapach, et al 2010) to combine the 2 n different forecasts to a single "optimal" forecast. Finally, the TAV model-averaging criterion uses only the forecasts in the interval plus/minus one standard deviation around the mean of the 2 n forecasts to form a forecast.…”

Section: The Real-time Forecasting Approachmentioning

confidence: 99%

Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy

2016

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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. Abstract Economic theory predicts that, in a small open economy, the dynamics of the real price of gold should be linked to real interest rates and the rate of change of the real exchange rate. Using data for Australia, we use a real-time forecasting approach to analyze whether real interest rates and the rate of change of the real exchange rate help to forecast out-ofsample the rate of change of the real price of gold. We study the economic value-added of out-of-sample forecasts using a behavioral-finance approach that takes into account that a forecaster may have an asymmetric loss function. Terms of use: Documents in EconStor mayJEL classification: C53; E44; G02

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Thick modeling

Cited by 199 publications

References 18 publications

Combining day-ahead forecasts for British electricity prices

Combining day-ahead forecasts for British electricity prices

Econometric modelling for short‐term inflation forecasting in the euro area

Fluctuations of the real exchange rate, real interest rates, and the dynamics of the price of gold in a small open economy

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