arXiv 5 Apr 2018 · Econometrics · 3 citations (OpenAlex)
arXiv:1804.01631 · PDF · DOI · OpenAlex · Extracted main text
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 the 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 and robustness properties. In particular, we show that the corresponding mean-variance regression location-scale model weakly dominates the ordinary least-squares location model under a Kullback-Leibler measure of divergence, with strict improvement in the presence of heteroskedasticity. The simultaneous mean-variance regression loss function is globally convex and the corresponding estimator is easy to implement. We establish its consistency and asymptotic normality under misspecification, provide robust inference methods, and present numerical simulations that show large improvements over ordinary and weighted least-squares in terms of estimation and inference in finite samples. We further illustrate our method with two empirical applications to the estimation of the relationship between economic prosperity in 1500 and today, and demand for gasoline in the United States.
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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 | Spady, R. H. and Stouli, S (2018) Dual Regression self | 0.928 | 5 | 3 | 80% |
| 2 | MacKinnon, J. G. and White, H (1985) Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties | 0.737 | 3 | 2 | 100% |
| 3 | MacKinnon, J. G (2013) Thirty Years of Heteroskedasticity-Robust Inference | 0.737 | 3 | 2 | 100% |
| 4 | Romano, J. P. and Wolf, M (2017) Resurrecting Weighted Least Squares | 0.737 | 3 | 2 | 100% |
| 5 | White, H (1982) Maximum Likelihood Estimation of Misspecified Models | 0.644 | 2 | 2 | 100% |
| 6 | Acemoglu, D., Johnson, S. and Robinson, J (2002) Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution | 0.585 | 3 | 1 | 100% |
| 7 | Newey, W. and Mc Fadden, D (1994) Large Sample Estimation and Hypothesis Testing | 0.550 | 6 | 3 | 17% |
| 8 | White, H (1980) A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity | 0.511 | 2 | 1 | 100% |
| 9 | Angrist, J. D. and Pischke, J. S (2008) Mostly Harmless Econometrics: An Empiricist's Companion | 0.405 | 1 | 1 | 100% |
| 10 | Akaike, H (1973) Information Theory and an Extension of the Likelihood Principle | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions | 0.644 | 2 | 2 |
| 2 | The Efficiency Gap | 0.405 | 1 | 1 |