In nonlinear panel models with fixed effects and fixed-T, the incidental parameter problem poses identification difficulties for structural parameters and partial effects. Existing solutions are model-specific, likelihood-based, impose time homogeneity, or restrict the distribution of unobserved...

We provide a new full-commitment intertemporal collective household model to estimate resource shares, defined as the fraction of household expenditure enjoyed by household members. Our model implies nonlinear time-varying household quantity demand functions that depend on fixed effects. We...

An expected utility based cost-benefit analysis is in general fragile to its distributional assumptions. We derive necessary and sufficient conditions on the utility function of the expected utility model to avoid this. The conditions ensure that expected (marginal) utility remains finite also...

In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice that the researchers make when estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over the candidate specifications...

This paper studies a dynamic ordered logit model for panel data with fixed effects. The main contribution of the paper is to construct a set of valid moment conditions that are free of the fixed effects. The moment functions can be computed using four or more periods of data, and the paper...

Many regression models have two dimensions, say time (<italic>t</italic> = 1,…,<italic>T</italic>) and households (<italic>i</italic> = 1,…,<italic>N</italic>), as in panel data, error components, or spatial econometrics. In estimating such models we need to specify the structure of the error variance matrix <italic>Ω</italic>, which is of dimension <italic>T N</italic> × <italic>T N</italic>. If <italic>T N</italic> is...

We introduce an accurate, easily implementable, and fast algorithm to compute optimal decisions in discrete-time long-horizon welfaremaximizing problems. The algorithm is useful when interest is only in the decisions up to period T, where T is small. It relies on a flexible parametrization of...