Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers -- proxy means-testing, nominations from community members, and an algorithmic approach...

We introduce a new rule-based linking method for historical Census records. We augment earlier algorithms based on name, age and place of birth (Abramitzky, Boustan, Eriksson, 2012, or "basic ABE"), with five matching characteristics - middle initial, county of residence, and spouse and parents'...

This paper develops a new algorithm for detecting US recessions in real time. The algorithm constructs millions of recession classifiers by combining unemployment and vacancy data to reduce detection noise. Classifiers are then selected to avoid both false negatives (missed recessions) and false...

Vote-trading is common practice in committees and group decision-making. Yet we know very little about its properties. Inspired by the similarity between the logic of sequential rounds of pairwise vote-trading and matching algorithms, we explore three central questions that have parallels in the...

We first summarize the dominant interpretations of the "frontier" in the United States and predecessor colonies over the past 400 years: agricultural (1610s-1880s), industrial (1890s-1930s), scientific (1940s- 1980s), and algorithmic (1990s-present). We describe the difference between the...

An accurate global algorithm is critical for quantifying the dynamics of the Diamond-Mortensen-Pissarides model. Loglinearization understates the mean and volatility of unemployment, overstates the unemployment-vacancy correlation, and ignores impulse responses that are an order of magnitude...

We introduce an algorithm for solving dynamic economic models that merges stochastic simulation and projection approaches: we use simulation to approximate the ergodic measure of the solution, we construct a fixed grid covering the support of the constructed ergodic measure, and we use...

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...

A new algorithm for the group minimization problem (GP) is proposed. The algorithm can be broadly described as follows. A suitable relaxation of(GP) is defined, in which any feasible point satisfies the group equation but may have negative components. The feasible points of the relaxation are...

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