Forecast Combinations in a DSGE‐VAR Lab

Costantini, Mauro; Gunter, Ulrich; Kunst, Robert M.

doi:10.1002/for.2427

Cited by 26 publications

(11 citation statements)

References 83 publications

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“…A few years later, Song et al [31] similarly stated that according to their results, forecast combination only improved forecasting performance in the tourism context in just over 50% of all cases compared with the most accurate single prediction. In a similar vein, Gunter and Önder [12] found that combined forecasts based on Bates-Granger weights, on multiple forecast encompassing tests, as well as on a combination of the two approaches [32]. The authors applied the aforementioned forecast combination techniques to Google Analytics indicators used as leading indicators for forecasting tourist arrivals to Vienna.…”

Section: Related Literaturementioning

confidence: 95%

Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

2020

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This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

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Section: Related Literaturementioning

confidence: 95%

Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

2020

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“…Gunter and O ¨nder (2016) and Shen et al (2011) used a multiple forecast that encompassed testing to determine the constituent models that remained to be combined. Uniform weights can be applied to these surviving models (Costantini et al, 2017). By contrast, the sum of weights is not always equal to one for nonlinear combination methods, which is necessary to identify the interactions among the relevant criteria.…”

Section: Forecast Combinationsmentioning

confidence: 99%

Combination forecasting using multiple attribute decision making in tourism demand

2022

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Purpose This study aims to address three important issues of combination forecasting in the tourism context: reducing the restrictions arising from requirements related to the statistical properties of the available data, assessing the weights of single models and considering nonlinear relationships among combinations of single-model forecasts. Design Methodology Approach A three-stage multiple-attribute decision-making (MADM)-based methodological framework was proposed. Single-model forecasts were generated by grey prediction models for the first stage. Vlsekriterijumska Optimizacija I Kompromisno Resenje was adopted to develop a weighting scheme in the second stage, and the Choquet integral was used to combine forecasts nonlinearly in the third stage. Findings The empirical results for inbound tourism in Taiwan showed that the proposed method can significantly improve accuracy to a greater extent than other combination methods. Along with scenario forecasting, a good forecasting practice can be further provided by estimating ex-ante forecasts post-COVID-19. Practical Implications The private and public sectors in economies with high tourism dependency can benefit from the proposed method by using the forecasts to help them formulate tourism strategies. Originality Value This study contributed to presenting a MADM-based framework that advances the development of a more accurate combination method for tourism forecasting.

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“…t is insignificant at a specific level of significance. As a result, encompassing tests eliminate uninformative rival models (Costantini et al, 2017).…”

Section: Forecast Encompassing Testmentioning

confidence: 99%

The impact of Google Trends index and encompassing tests on forecast combinations in tourism

2022

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Purpose Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set. Design/methodology/approach Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set. Findings The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM. Practical implications With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting. Originality/value To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

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Forecast Combinations in a DSGE‐VAR Lab

Cited by 26 publications

References 83 publications

Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

Combination forecasting using multiple attribute decision making in tourism demand

The impact of Google Trends index and encompassing tests on forecast combinations in tourism

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