Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/189731 
Erscheinungsjahr: 
2018
Schriftenreihe/Nr.: 
cemmap working paper No. CWP25/18
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
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.
Schlagwörter: 
Conditional mean and variance functions
linear regression
simultaneous approximation
heteroskedasticity
robust inference
misspecification
influence function
convexity
ordinary least-squares
dual regression
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