In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical...

We propose a random effects panel data model with both spatially correlated error components and spatially lagged dependent variables. We focus on diagnostic testing procedures and derive Lagrange multiplier (LM) test statistics for a variety of hypotheses within this model. We first construct...

We provide cross-country evidence that rejects the traditional interpretation of the natural resource curse. First, growth depends negatively on volatility of unanticipated output growth independent of initial income, investment, human capital, trade openness, natural resource dependence, and...

Cluster-robust inference is widely used in modern empirical work in economics and many other disciplines. The key unit of observation is the cluster. We propose measures of "high-leverage" clusters and "influential" clusters for linear regression models. The measures of leverage and partial...

This paper examines alternative accrual accounting rules from an incentive and control perspective. For a range of common production, financing and investment decisions we consider alternative asset valuation rules. The criterion for distinguishing among these rules is that the corresponding...

The paper reports the result of empirical tests on the validity of Fleuriet's model, also known in Brazil as Advanced or Dynamic Working Capital Analysis. According to some authors, the model, which was introduced in Brazil in the 80's, brought important innovations to working capital analysis...

This paper introduces a method which permits valid inference given a finite number of heterogeneous, correlated clusters. Many inference methods assume clusters are asymptotically independent or model dependence across clusters as a function of a distance metric. With panel data, these...

Efficient computational algorithms for bootstrapping linear regression models with clustered data are discussed. For OLS regression, a new algorithm is provided for the pairs cluster bootstrap, and two algorithms for the wild cluster bootstrap are compared. One of these is a new way to express...

We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regres- sion models estimated by least squares. These estimators have previously been com- putationally infeasible except for small...

The volatility of unanticipated output growth in income per capita is detrimental to long-run development, controlling for initial income per capita, population growth, human capital, investment, openness and natural resource dependence. This effect is significant and robust over a wide range of...