Simultaneous mean-variance regression

Richard Spady and Sami Stouli
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Richard Spady: Institute for Fiscal Studies and Johns Hopkins

No CWP25/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: We propose simultaneous mean-variance regression for the linear estimation and approximation of conditional mean functions. In the presence of heteroskedasticity of unknown form, our method accounts for varying dispersion in the regression outcome across the support of conditioning variables by using weights that are jointly determined with mean regression parameters. Simultaneity generates outcome predictions that are guaranteed to improve over ordinary least-squares prediction error, with corresponding parameter standard errors that are automatically valid. Under shape misspecification of the conditional mean and variance functions, we establish existence and uniqueness of the resulting approximations and characterize their formal interpretation. We illustrate our method with numerical simulations and two empirical applications to the estimation of the relationship between economic prosperity in 1500 and today, and demand for gasoline in the United States.

Keywords: Conditional mean and variance functions; linear regression; simultaneous approximation; heteroskedasticity; robust inference; misspecification; influence function; convexity; ordinary least-squares; dual regression (search for similar items in EconPapers)
Date: 2018-04-05
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Simultaneous Mean-Variance Regression (2019)
Working Paper: Simultaneous Mean-Variance Regression (2018)
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