The Inconsistency Puzzle Resolved: an Omitted Variable

The contemporary version of the dynamic Ramsey problem omits expectations of a household’s initial lump-sum wealth taxation due to policy revision; therefore, the attainable resource allocation set in this problem is ill-defined. This omission leads to misleading conclusions about the optimal policy in the short run and, in particular, that the Ramsey policy is dynamically inconsistent. The effect of introducing the expectations into the analysis of dynamic inconsistency is similar to that of introducing expected inflation into the Phillips curve: we show that only an unexpected policy surprise affects the attainable resource allocation set and the optimal policy. In contrast to Chamley (1986), we show that intensive capital income taxation at the beginning of an optimal policy does not imply a lump-sum taxation of household wealth and cannot reduce the excess tax burden. We also demonstrate that the Ramsey policy is dynamically consistent even without commitment. We resolve the Ramsey problem and compare our results to those of Chamley on optimal capital income taxation.Consistency, Equilibrium policy, Optimal taxation

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

Taylor rules, omitted variables, and interest rate smoothing in the US

We test for the presence of interest rate smoothing in forward looking Taylor rules in first differences. We also consider financial and asymmetric preferences indicators. We find that interest rate smoothing is not induced by an omitted variable bias.Taylor rules; Interest rate smoothing; Serial correlation; Observational equivalence; Omitted variables

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

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

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

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

We propose to use a time-varying coefficient model to measure contagion. The proposed measure works in the joint presence of heteroskedasticity and omitted variables. It requires knowledge of the source of the crisis but not its timing. The estimation procedure is Bayesian and is based on Markov Chain Monte Carlo methods. We asses the performance of the proposed measure both with simulated and actual data.Contagion, Gibbs sampling, heteroskedasticity, omitted variable

"The Degree of Precautionary Saving: A Reexamination"

Extending Dynan's methodology (1993), we show that a significant frac tion of the prudence parameter puzzle can be explained by a downward omitted variable bias. Further, the estimated prudence is substantially higher for liquidity-constrained households.