Instrumental Variables Estimation of Heteroskedastic Linear Models Using All Lags of Instruments

We propose and evaluate a technique for instrumental variables estimation of linear models with conditional heteroskedasticity. The technique uses approximating parametric models for the projection of right hand side variables onto the instrument space, and for conditional heteroskedasticity and serial correlation of the disturbance. Use of parametric models allows one to exploit information in all lags of instruments, unconstrained by degrees of freedom limitations. Analytical calculations and simulations indicate that there sometimes are large asymptotic and finite sample efficiency gains relative to conventional estimators (Hansen (1982)), and modest gains or losses depending on data generating process and sample size relative to quasi-maximum likelihood. These results are robust to minor misspecification of the parametric models used by our estimator.

Instrumental Variables Estimation of Heteroskedastic Linear Models Using All Lags of Instruments

We propose and evaluate a technique for instrumental variables estimation of linear models with conditional heteroskedasticity. The technique uses approximating parametric models for the projection of right hand side variables onto the instrument space, and for conditional heteroskedasticity and serial correlation of the disturbance. Use of parametric models allows one to exploit information in all lags of instruments, unconstrained by degrees of freedom limitations. Analytical calculations and simulations indicate that there sometimes are large asymptotic and finite sample efficiency gains relative to conventional estimators (Hansen (1982)), and modest gains or losses depending on data generating process and sample size relative to quasi-maximum likelihood. These results are robust to minor misspecification of the parametric models used by our estimator.

Lessons Learned and Next Steps in Energy Efficiency Measurement and Attribution: Energy Savings, Net to Gross, Non-Energy Benefits, and Persistence of Energy Efficiency Behavior

This white paper examines four topics addressing evaluation, measurement, and attribution of direct and indirect effects to energy efficiency and behavioral programs: Estimates of program savings (gross); Net savings derivation through free ridership / net to gross analyses; Indirect non-energy benefits / impacts (e.g., comfort, convenience, emissions, jobs); and, Persistence of savings

Accounting for Nonresponse Heterogeneity in Panel Data

The paper proposes a technique for the estimation of possibly nonlinear panel data models in the presence of heterogeneous unit nonresponse. Attrition or unit nonresponse in panel data usually renders parameter estimators inconsistent unless the unavailable information is missing completely at random. For moment based estimators this problem can be expressed in terms of the impossibility to construct the sample equivalents of the population moments of interest. However, if the attrition process is conditionally mean independent of the variables of interest then the sample equivalents of the population moments can be recovered by weighting the moment functions with the conditional response probability (or propensity score). The latter is usually unknown and has to be estimated. In the presence of nonresponse heterogeneity the propensity score can be estimated by conventional parametric estimation methods like the multinomial logit or probit model. The technique proposed in this paper leads to a moment estimator which simultaneously exploits the weighted moment functions of interest and the score function of the multinomial choice model. The use of simulated moments is discussed for applications with many nonresponse reasons. An applications of the estimator to firm level data is presented where the variables of interest are R&D investments related to product and process innovations.

Instrumental variables estimation of heteroskedastic linear models using all lags of instruments

estimation;linear models

Bayesian multivariate exogeneity analysis: An application to a UK money demand equation

Money Demand

The Power of Single Equation Tests for Cointegration when the Cointegrating Vector is Prespecified.

In this paper I present an alternative derivation of the asymptotic distribution of Kremers, Ericsson and Dolado's (1992) conditional ECM- based t-test for no-cointegration with a single prespecified cointegrating vector. This alternative distribution, which is identical to the distribution of Hansen's (1995) covariate augmented t-test for a unit root, is valid for weakly exogenous regressors and depends on a consistently estimable nuisance parameter that takes on values in the unit interval. I show analytically, using asymptotic power functions based on near cointegrated alternatives, that the ECM t-test with a prespecified cointegrating vector can have much higher power than single equation tests for cointegration based on estimating the cointegrating vector. I also characterize situations in which the ECM t-test computed with a misspecified cointegrating vector will have high power.cointegration, common factor, error correction model, local power, misspecification, near-cointegration, strong exogeneity, weak exogeneity.