Econometrics is the area of statistics concerned in analyzing economic data, for both economic and business applications. This document, introduces the intermediate concepts of this area, for students already familiarized with basic econometric theory. In particular, topics concerning...

The paper introduces for the most frequently used three-dimensional panel data sets several random effects model specifications. It derives appropriate estimation methods for the balanced and unbalanced cases. An application is also presented where the bilateral trade of 20 EU countries is...

This study augments a standard growth model with institutional controls, and models the spatial dependence using geographical and institutional weight matrices. Spatial Durbin model is shown to be the most appropriate to describe the data and political institutions weight matrix best explains...

In this paper I provide new evidence on the implications of treatment effect heterogeneity for least squares estimation when the effects are inappropriately assumed to be homogenous. I prove that under a set of benchmark assumptions linear regression provides a consistent estimator of the...

Propensity Score Matching (PSM) has become a popular approach to estimation of causal effects. It relies on the assumption that selection into a treatment can be explained purely in terms of observable characteristics (the “unconfoundedness assumption”) and on the property that balancing on...

In this paper, we first re-visit the inference problem for interval identified parameters originally studied in Imbens and Manski (2004) and later extended in Stoye (2008). We take the general criterion function approach and establish a new confidence interval that is asymptotically valid under...

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption respectively....

The purpose of this paper is to show the capacity of a new non-parametric test based on symbolic entropy and symbolic dynamics to deal with the detection of linear and non-linear spatial causality. The good performance of the new test in detecting spatial causality and causal weighting matrix is...

This study is conducted to determine the factors affecting magnitude of poor families in the Philippines and measure the effect of the variables presented. The model was estimated using the Ordinary Least Square (OLS) procedure and cross sectional data set consisting of the 16 regions in the...

This paper derives some exact power properties of tests for spatial autocorrelation in the context of a linear regression model. In particular, we characterize the circumstances in which the power vanishes as the autocorrelation increases, thus extending the work of Krämer (2005, Journal of...