forecast horizons from one to twelve months-ahead, which are based on different average estimation windows, expanding windows …This paper applies combining forecasts of air travel demand generated from the same model but over different estimation … windows. The combination approach used resorts to Pesaran and Pick (2011), but the empirical application is extended in …

forecast horizons from one to twelve months-ahead, which are based on different average estimation windows, expanding windows …This paper applies combining forecasts of air travel demand generated from the same model but over different estimation … windows. The combination approach used resorts to Pesaran and Pick (2011), but the empirical application is extended in …

forecast horizons from one to twelve months-ahead, which are based on different average estimation windows, expanding windows …This paper applies combining forecasts of air travel demand generated from the same model but over different estimation … windows. The combination approach used resorts to Pesaran and Pick (2011), but the empirical application is extended in …

estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks …

estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks …

estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks …

estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks …