Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners,...

Nonlinearity is an important consideration in many problems of finance and economics, such as pricing securities, computing equilibrium, and conducting structural estimations. We extend the transform analysis in Duffie, Pan, and Singleton (2000) by providing analytical treatment of a general...

We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...

Under the assumption of no unmeasured confounders, a large literature exists on methods that can be used to estimating average treatment effects (ATE) from observational data and that spans regression models, propensity score adjustments using stratification, weighting or regression and even the...

Empirical exercises in economics frequently involve estimation of highly nonlinear models. The criterion function may not be globally concave or convex and exhibit many local extrema. Choosing among these local extrema is non-trivial for a variety of reasons. In this paper, we analyze the...

In applications, the linear multiple regression model is often modified to allow for nonlinearity in an independent variable. It is argued here that in practice it may often be desirable to specify a Bayesian prior that the unknown functional form is "simple" or "uncomplicated" rather than to...

NL2SOL is a modular program for solving the nonlinear least-squares problem that incorporates a number of novel features. It maintains a secant approximation S to the second-order part of the least-squares Hessian and adaptively decides when to use this approximation. We have found it very...

What are the statistical and computational problems associated with robust nonlinear regression? This paper presents a number of possible approaches to these problems and develops a particular algorithm based on the work of Powell and Dennis

The prediction accuracy of six estimators of econometric models are compared. Two of rthe estimators are ordinary least squares (OLS) and full-information maximum likelihood. (FML). The other four estimators are robust estimators in the sense that they give less weight to large residuals. One of...

One basic feature of aggregate data is the presence of time-varying variance in real and nominal variables. Periods of high volatility are followed by periods of low volatility. For instance, the turbulent 1970s were followed by the much more tranquil times of the great moderation from 1984 to...