Consistency of Hedonic Price Indexes with Unobserved Characteristics

Hedonic regressions are prone to omitted variable bias. The estimation of price relatives for new and disappearing goods using hedonic imputation methods involves taking ratios of hedonic models. This may lead to a situation where the omitted variable bias in each of the hedonic regressions offset each other. This study finds that the single imputation hedonic method estimates inconsistent price relatives, while the double imputation method may produce consistent price relatives depending on the behavior of unobserved characteristics in the comparison periods. The study outlines a methodology to test whether double imputation price relatives are consistent. The results of this study have implications with regard to the construction of quality adjusted indexes.Hedonic imputation method; omitted variable bias; model selection; quality adjusted price indexes; new and disappearing goods

Omitted variable bias and cross section regression

"July 1983."Bibliography: p. 27.by Thomas M. Stoker

Measuring contagion with a Bayesian, time-varying coefficient model

JEL Classification: C11, C15, F41, F42, G15Contagion, Gibbs sampling, Heteroskedasticity, Omitted variable bias, Time-varying coefficient models

Exogenous Treatment and Endogenous Factors: Vanishing of Omitted Variable Bias on the Interaction Term

Whether interested in the differential impact of a particular factor in various institutional settings or in the heterogeneous effect of policy or random experiment, the empirical researcher confronts a problem if the factor of interest is correlated with an omitted variable. This paper presents the circumstances under which it is possible to arrive at a consistent estimate of the mentioned effect. We find that if the source of heterogeneity and omitted variable are jointly independent of policy or treatment, then the OLS estimate on the interaction term between the treatment and endogenous factor turns out to be consistent.treatment effect; heterogeneity; policy evaluation; random experiments; omitted variable bias

Wages and Working Conditions

This paper presents another extension of the approach initiated by Brown. As in Brown's work, the wage change specification is used to control for bias due to omitted ability data. Then, as in Duncan and Hoimlund's study, working conditions are measured using subjective self?reported data. However, in this paper, working conditions are measured by a single comprehensive variable. This approach eliminates omitted working conditions as a source of bias. The working conditions measure is then treated as an unobserved variable which limits measurement error to an unknown scale factor. The model is estimated using a technique derived by memiya (1978).

Formal Covariate Benchmarking to Bound Omitted Variable Bias

Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking after residualizing the unobserved confounder on the set of observed covariates. In this paper, I explain the rationale and details of this procedure. I clarify some important details of the process of formal covariate benchmarking and highlight some of the difficulties of interpretation that researchers face in reasoning about the residualized part of unobserved confounders. I explain all the points with several empirical examples

Omitted variable bias of Lasso-based inference methods: A finite sample analysis

We study the finite sample behavior of Lasso-based inference methods such as post double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso not selecting relevant controls. This phenomenon can occur even when the coefficients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern high-dimensional OLS-based methods and provide practical guidance

Proxying ability by family background in returns to schooling estimations is generally a bad idea

A regression model is considered where earnings are explained by schooling and ability. It is assumed that schooling is measured with error and that there are no data on ability. Regressing earnings on observed schooling then yields an estimate of the return to schooling that is subject to positive omitted variable bias (OVB) and negative measurement error bias (MEB). The effects on the OVB and the MEB from using family background variables as proxies for ability are investigated theoretically and empirically. The theoretical analysis demonstrates that the impact on the OVB is uncertain, while the MEB invariably increases in magnitude. The empirical analysis shows that the MEB generally dominates the OVB. As the measurement error increases and/or more family background variables are added, the total bias rapidly becomes negative, driving the estimated return further and further away from the true value.Missing data; proxy variables; measurement error; consistent estimates of omitted variable bias and measurement error bias

How likely is it that omitted variable bias will overturn your results?

Building on a recently developed methodology for sensitivity analysis that parametrizes omitted variable bias in terms of partial R-Squared measures, I propose a simple statistic to capture the severity of omitted variable bias in any observational study: the probability of omitted variable bias overturning the reported result. The central element of my proposal is formal covariate benchmarking, whereby researchers choose an observed regressor (or a group of observed regressors) to benchmark the relative strength of association of the omitted regressor with the outcome variable and with the treatment variable. These relative strengths of association function as the two sensitivity parameters of the analysis. By allowing these sensitivity parameters to take all permissible values, we get the most conservative estimate of the probability that omitted variable bias can overturn the reported results. By using absolute and relative limits on the maximum values of the sensitivity parameters based on institutional knowledge or other details of the particular study, a researcher can generate less conservative estimates of that probability. For empirical studies with relatively large number of regressors and sample sizes, I suggest bounds for the sensitivity parameters based on simulation studies. I illustrate the methodology using an empirical example that studies the effect of exposure to violence on attitudes towards peace