We develop and estimate a model of student study time on a social network. The model is designed to exploit unique data collected in the Berea Panel Study. Study time data allow us to quantify an intuitive mechanism for academic social interactions: own study time may depend on friend study time...

This paper presents results from a Monte Carlo study concerning inference with spatially dependent data. It investigates the impact of location/distance measurement errors upon the accuracy of parametric and nonparametric estimators of asymptotic variances.

In this paper, we introduce a method of generating bootstrap samples with unknown patterns of cross-sectional/spatial dependence, which we call the spatial dependent wild bootstrap. This method is a spatial counterpart to the wild dependent bootstrap of Shao (2010) and generates data by...

This paper investigates the role of social learning in the diffusion of a new agricultural technology in Ghana. We use unique data on farmers' communication patterns to define each individual's information neighborhood, the set of others from whom he might learn. Our empirical strategy is to...

In this chapter we examine social networks among farmers in a developing country. We use detailed data on economic activities and social interactions between people living in four study villages in Ghana. It is clear that economic development in this region is being shaped by the networks of...

We develop and estimate a model of study time choices of students on a social network. The model is designed to exploit unique data collected in the Berea Panel Study. Study time data allow us to quantify an intuitive mechanism for academic social interactions: own study time may depend on...

Many studies of historical persistence find that modern outcomes strongly reflect characteristics of the same places in the distant past. However they rely on data that often exhibit extreme spatial trends and autocorrelation, suggesting that their unusually large t-statistics may be due to...

Linear panel models, and the "event-study plots" that often accompany them, are popular tools for learning about policy effects. We discuss the construction of event-study plots and suggest ways to make them more informative. We examine the economic content of different possible identifying...

Linear panel models and the "event-study plots" that often accompany them are popular tools for learning about policy effects. In this paper, we introduce the "xtevent" package for Stata, which enables the construction of event-study plots following the suggestions in Freyaldenhoven et al....

This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a number of unknown parameters that is not vanishingly small relative to the sample size. We first present results in...