scholarly journals Bayesian forecast combination for inflation using rolling windows : an emerging country case

2012 ◽  
Author(s):  
Luis Fernando Melo-Velandia ◽  
Rubén Albeiro Loaiza-Maya
Keyword(s):  
Forecast Combination ◽  
Bayesian Forecast ◽  
Emerging Country ◽  
Rolling Windows

scholarly journals SIMPLE BAYESIAN FORECAST COMBINATION

2020 ◽  
Vol 15 (04) ◽  
pp. 2050016
Author(s):  
PHILIP HANS FRANSES
Keyword(s):  
The Netherlands ◽  
Diffusion Models ◽  
Ease Of Use ◽  
Forecast Combination ◽  
Bayesian Forecast ◽  
Daily Data ◽  
Combined Forecasts

In this paper, it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. The algorithm is applied to forecasts from three non-nested diffusion models for S shaped processes like virus diffusion. An illustration to daily data on first-wave cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the newly combined forecasts.

scholarly journals Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 884-919
Author(s):  
Ulrich Gunter
Keyword(s):  
Moving Average ◽  
Forecast Accuracy ◽  
Forecast Combination ◽  
Autoregressive Integrated Moving Average ◽  
Hotel Room ◽  
Daily Data ◽  
Forecast Models ◽  
Rolling Windows ◽  
Combined Forecasts ◽  
Box Cox Transformation

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques.

scholarly journals Forecasting virus diffusion with simple Bayesian forecast combination

2020 ◽  
Author(s):  
Philip Hans Franses
Keyword(s):  
The Netherlands ◽  
Diffusion Models ◽  
Ease Of Use ◽  
Forecast Combination ◽  
Bayesian Forecast ◽  
Daily Data ◽  
Combined Forecasts

AbstractThere are various diffusion models for S shaped processes like virus diffusion and these models are typically not nested. In this note it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. An illustration to daily data on cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the thus combined forecasts.

Bayesian forecast combination in VAR-DSGE models

2019 ◽  
Vol 59 ◽  
pp. 278-298 ◽  
Author(s):  
Kuo-Hsuan Chin ◽  
Xue Li
Keyword(s):  
Dsge Models ◽  
Forecast Combination ◽  
Bayesian Forecast

First Insights on Peruvian Luxury Consumers

2018 ◽  
Vol 9 (8) ◽  
pp. 699-712
Author(s):  
Anne-Flore Maman Larraufie ◽  
Keyword(s):  
Emerging Markets ◽  
Cultural Dimensions ◽  
Historical Background ◽  
Two Stage ◽  
Secondary Sources ◽  
Strong Potential ◽  
Analytic Process ◽  
Emerging Country ◽  
Research Questions ◽  
Practical Recommendations

Peru is an emerging country showing strong potential for future luxury developments. It already holds luxury regular consumers, mainly in the Lima capital. However, it is currently approached in a standardized process by luxury firms, following what is done in other emerging markets for luxury. To be efficient, it is necessary to get more knowledge about Peruvian consumers. This is what this article aims at. After reviewing the historical background of the country along with its cultural dimensions, we present results from a two-stage analytic process based on data collected from secondary sources and interviews with consumers. We derive from that practical recommendations for luxury managers and propose some research questions and hypotheses to be further explored and tested.

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