On the Forecast Combination Puzzle
It is often reported in the forecast combination literature that a simple average of candidate forecasts is more robust than sophisticated combining methods. This phenomenon is usually referred to as the “forecast combination puzzle”. Motivated by this puzzle, we explore its poss...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-09-01
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| Series: | Econometrics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2225-1146/7/3/39 |
| _version_ | 1811184040527527936 |
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| author | Wei Qian Craig A. Rolling Gang Cheng Yuhong Yang |
| author_facet | Wei Qian Craig A. Rolling Gang Cheng Yuhong Yang |
| author_sort | Wei Qian |
| collection | DOAJ |
| description | It is often reported in the forecast combination literature that a simple average of candidate forecasts is more robust than sophisticated combining methods. This phenomenon is usually referred to as the “forecast combination puzzle”. Motivated by this puzzle, we explore its possible explanations, including high variance in estimating the target optimal weights (estimation error), invalid weighting formulas, and model/candidate screening before combination. We show that the existing understanding of the puzzle should be complemented by the distinction of different forecast combination scenarios known as combining for adaptation and combining for improvement. Applying combining methods without considering the underlying scenario can itself cause the puzzle. Based on our new understandings, both simulations and real data evaluations are conducted to illustrate the causes of the puzzle. We further propose a multi-level AFTER strategy that can integrate the strengths of different combining methods and adapt intelligently to the underlying scenario. In particular, by treating the simple average as a candidate forecast, the proposed strategy is shown to reduce the heavy cost of estimation error and, to a large extent, mitigate the puzzle. |
| first_indexed | 2024-04-11T13:07:43Z |
| format | Article |
| id | doaj.art-f822f1fec1fd4fa8910b85bbb692dd52 |
| institution | Directory Open Access Journal |
| issn | 2225-1146 |
| language | English |
| last_indexed | 2024-04-11T13:07:43Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Econometrics |
| spelling | doaj.art-f822f1fec1fd4fa8910b85bbb692dd522022-12-22T04:22:42ZengMDPI AGEconometrics2225-11462019-09-01733910.3390/econometrics7030039econometrics7030039On the Forecast Combination PuzzleWei Qian0Craig A. Rolling1Gang Cheng2Yuhong Yang3Department of Applied Economics and Statistics, University of Delaware, Newark, DE 19716, USASchool of Statistics, University of Minnesota, Minneapolis, MN 55455, USASchool of Statistics, University of Minnesota, Minneapolis, MN 55455, USASchool of Statistics, University of Minnesota, Minneapolis, MN 55455, USAIt is often reported in the forecast combination literature that a simple average of candidate forecasts is more robust than sophisticated combining methods. This phenomenon is usually referred to as the “forecast combination puzzle”. Motivated by this puzzle, we explore its possible explanations, including high variance in estimating the target optimal weights (estimation error), invalid weighting formulas, and model/candidate screening before combination. We show that the existing understanding of the puzzle should be complemented by the distinction of different forecast combination scenarios known as combining for adaptation and combining for improvement. Applying combining methods without considering the underlying scenario can itself cause the puzzle. Based on our new understandings, both simulations and real data evaluations are conducted to illustrate the causes of the puzzle. We further propose a multi-level AFTER strategy that can integrate the strengths of different combining methods and adapt intelligently to the underlying scenario. In particular, by treating the simple average as a candidate forecast, the proposed strategy is shown to reduce the heavy cost of estimation error and, to a large extent, mitigate the puzzle.https://www.mdpi.com/2225-1146/7/3/39combining for adaptationcombining for improvementmulti-level AFTERmodel selectionstructural break |
| spellingShingle | Wei Qian Craig A. Rolling Gang Cheng Yuhong Yang On the Forecast Combination Puzzle Econometrics combining for adaptation combining for improvement multi-level AFTER model selection structural break |
| title | On the Forecast Combination Puzzle |
| title_full | On the Forecast Combination Puzzle |
| title_fullStr | On the Forecast Combination Puzzle |
| title_full_unstemmed | On the Forecast Combination Puzzle |
| title_short | On the Forecast Combination Puzzle |
| title_sort | on the forecast combination puzzle |
| topic | combining for adaptation combining for improvement multi-level AFTER model selection structural break |
| url | https://www.mdpi.com/2225-1146/7/3/39 |
| work_keys_str_mv | AT weiqian ontheforecastcombinationpuzzle AT craigarolling ontheforecastcombinationpuzzle AT gangcheng ontheforecastcombinationpuzzle AT yuhongyang ontheforecastcombinationpuzzle |