Using Least Squares to Approximate Unknown Regression Functions

By Halbert White. 1980

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Citations in the corpus

Fairness in Criminal Justice Risk Assessments: The State of the Art (2017, methodology): “This is a common approach in computer science, especially for machine learning (Bishop 2006: section 1.5; M. J. Kearns 1994:section 1.2), and also can be found in econometrics (White 1980) and statistics (Buja et al. 2018a; Freedman 1981).”
LM Tests for Functional Form and Spatial Error Correlation (2001, unknown): “©Sage Publications, Inc. around the variables (see White 1980; Byron and Bera 1983).”
Quantile Regression Under Misspecification, with an Application to the U.S. Wage Structure (2006, unknown): “) describes a line between the approximation point and the true one.”
Effects of globalization on peace and stability: Implications for governance and the knowledge economy of African countries (2017, methodology): “While existing research such as White (1980a, 1980b), for example, has laid the theoretical ground upon which the intuitive underpinning of our estimation technique, compared to previous studies we use the fixed effect strategy to reduce the influence of omitted variable due to geographical,…”
Assumption Lean Regression (2018, methodology): “We draw heavily on two papers by Buja et al. (2016a;b), a portion of which draws on early insights of Halbert White (1980a). ar X iv :1 80 6.”
Decomposing Portfolio Value-at-Risk: A General Analysis (2003, background): “is non-linear and the underlying distributions are no longer White [1980, p.155] proves that linear least squares estimates provide consistent (i.e.”
Models as Approximations I: Consequences Illustrated with Linear Regression (2014, background): “gonal matrix with squared residuals r2 i = (Y i X~ i^)2 in the diagonal. With this notation the simplest and original form of the sandwich estimator of asymptotic variance can be written as follows (White 1980a): AV^ sand := E^[X~X~ T ] 1 E^[(Y X~ T^)2X~X~ T ] E^[X~X~ T ] 1 (25) The sandwich standar”
Semiparametric Counterfactual Density Estimation (2021, methodology): “This is akin to the well-known concept of a best linear predictor in standard linear regression [White, 1980].”
Cluster–Robust Variance Estimation for Dyadic Data (2013, unknown): “es of a plug-in variance estimator that is analogous to the sandwich estimators deﬁned for heteroskedastic or clustered data (Arellano, 1987; Cameron, Gelbach and Miller, 2011; Liang and Zeger, 1986; White, 1980a). We establish sufﬁ- cient conditions for consistency of the plug-in variance estimator”
Feasible invertibility conditions and maximum likelihood estimation for observation-driven models (2018, background): “See also [45] on estimation of pseudotrue parameters for misspecified models and their usefulness for forecasting, [14] on estimation of pseudo-true parameters for misspecified time series models, [32] on how to learn from models with wide forms of misspecification, and [37] for asymptotic theory of”
Finite Population Causal Standard Errors (2014, background): “¶University Distinguished Professor, Department of Economics, Michigan State University, wooldri1@msu.edu [1]”
Models as Approximations: How Random Predictors and Model Violations Invalidate Classical Inference in Regression (2014, unknown): “(25) The representation of βj as a simple regression coefficient is as follows: βj = E[Y Xj•] E[Xj• (2)] = E[μ( ~ X)Xj•] E[Xj• (2)] .”
Accounting for the Black-White Wealth Gap: A Nonparametric Approach (2001, background): “The magnitude is an empirical matter, but is likely to be larger the further the projection of the conditional expected wealth function is from the denser portions of the white earnings distribution (White 1980).”
Empirical Asset Pricing via Machine Learning (2018, unknown): “Vast predictor sets are viable for linear prediction when either penalization or dimension reduction 2See, for example, Hastie et al. (2009). is used.”