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Focused Weighted-Average Least Squares Estimator

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  • Shou-Yung Yin

Abstract

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.

Suggested Citation

  • Shou-Yung Yin, 2026. "Focused Weighted-Average Least Squares Estimator," Papers 2603.03008, arXiv.org.
    • Handle: RePEc:arx:papers:2603.03008
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    References listed on IDEAS

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