EconBase
← All papers

Mean-shift least squares model averaging

Kenichiro McAlinn, Kosaku Takanashi

arXiv 3 Dec 2019 · Econometrics

arXiv:1912.01194 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a new estimator for selecting weights to average over least squares estimates obtained from a set of models. Our proposed estimator builds on the Mallows model average (MMA) estimator of Hansen (2007), but, unlike MMA, simultaneously controls for location bias and regression error through a common constant. We show that our proposed estimator-- the mean-shift Mallows model average (MSA) estimator-- is asymptotically optimal to the original MMA estimator in terms of mean squared error. A simulation study is presented, where we show that our proposed estimator uniformly outperforms the MMA estimator.

Citation extraction

35
references
57
in-text mentions
35
distinct cited
3
self-citations
6,452
main-text words

appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Hansen, B. E (2007) Least squares model averaging1.000185100%
2Wan, A. T., Zhang, X., Zou, G (2010) Least squares model averaging by mallows criterion0.87452100%
3Bates, J. M., Granger, C. W. J (1969) The combination of forecasts0.51121100%
4Aastveit, K. A., Gerdrup, K. R., Jore, A. S., Thorsrud, L. A (2014) Nowcasting GDP in real time: A density combination approach0.40511100%
5Aastveit, K. A., Ravazzolo, F., Van Dijk, H. K (2018) b0.40511100%
6Amisano, G. G., Giacomini, R (2007) Comparing density forecasts via weighted likelihood ratio tests0.40511100%
7Billio, M., Casarin, R., Ravazzolo, F., van Dijk, H. K (2012) Combination schemes for turning point predictions0.40511100%
8Billio, M., Casarin, R., Ravazzolo, F., van Dijk, H. K (2013) Time-varying combinations of predictive densities using nonlinear filtering0.40511100%
9Kapetanios, G., Mitchell, J., Price, S., Fawcett, N (2015) Generalised density forecast combinations0.40511100%
10Geweke, J. F., Amisano, G. G (2011) Optimal prediction pools0.40511100%

Showing the top 10 of 35 scored citations.