arXiv 3 Mar 2026 · Econometrics
arXiv:2603.03008 · PDF · DOI · OpenAlex · Extracted main text
We propose a focused weighted-average least squares (FWALS) estimator that addresses the computational burden of focused model averaging. By semi-orthogonalizing auxiliary regressors, the weighting problem is reduced from $2^{k_2}$ sub-models to at most $k_2$ regressor-wise weights, yielding a tractable sub-optimal procedure. Under local-to-zero conditions, we derive the limiting distribution of FWALS for smooth focused functions and provide a plug-in AMSE criterion for data-driven weight selection. Simulations show that FWALS closely matches the focused information criterion (FIC) benchmark and delivers stable performance when focused function is designed for impulse response function. Prior-based WALS can be competitive in some settings, but its performance depends on the signal regime and the design of focused parameter. Overall, FWALS offers a practical and robust alternative with substantial computational savings.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Magnus, Jan R. and Powell, Owen and Prüfer, Patricia (2010) A Comparison of two Model Averaging Techniques With an Application to Growth Empirics | 1.000 | 11 | 5 | 100% |
| 2 | Liu, Chu-An (2015) Distribution Theory of the Least Squares Averaging Estimator | 1.000 | 11 | 4 | 100% |
| 3 | Luca, Giuseppe De and Magnus, Jan R. and Peracchi, Franco (2025) Bayesian Estimation of the Normal Location Model: A Non‐Standard Approach | 0.874 | 5 | 2 | 100% |
| 4 | De Luca, Giuseppe and Magnus, Jan R and Peracchi, Franco (2018) Weighted-average least squares estimation of generalized linear models | 0.843 | 3 | 3 | 100% |
| 5 | Charkhi, Ali and Claeskens, Gerda and Hansen, Bruce E (2016) Minimum Mean Squared Error Model Averaging in Likelihood Models | 0.811 | 4 | 2 | 100% |
| 6 | Luca, Giuseppe De and Magnus, Jan R. and Peracchi, Franco (2022) Sampling properties of the Bayesian posterior mean with an application to WALS estimation | 0.737 | 3 | 2 | 100% |
| 7 | Zhu, Rong and Wang, Haiying and Zhang, Xinyu and Liang, Hua (2023) A Scalable Frequentist Model Averaging Method | 0.737 | 3 | 2 | 100% |
| 8 | Lohmeyer, Jan and Palm, Franz and Reuvers, Hanno and Urbain, Jean-Pi… (2019) Focused information criterion for locally misspecified vector autoregressive models | 0.644 | 2 | 2 | 100% |
| 9 | Lu, Xun (2015) A Covariate Selection Criterion for Estimation of Treatment Effects | 0.644 | 2 | 2 | 100% |
| 10 | Hansen, Bruce E (2007) Least Squares Model Averaging | 0.644 | 2 | 2 | 100% |
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