We provide evidence on the least biased ways to identify causal effects in situations where there are multiple outcomes that all depend on the same endogenous regressor and a reasonable but potentially contaminated instrumental variable that is available. Simulations provide suggestive evidence...

Causal inference methods are widely used in empirical research; however, there is a paucity of evidence on the properties of shared latent factor estimators in the presence of contaminated instrumental variable (IV) when a strong IV may not be available. We present a theoretical formulation to...

This paper presents a method of calculating sharp bounds on the average treatment effect using linear programming under identifying assumptions commonly used in the literature. This new method provides a sensitivity analysis of the identifying assumptions and missing data in an application...

The rapidly growing literature on the relationship between energy consumption and economic growth has not univocally identified the real causal relationship yet. We argue that bivariate models, which analyze the causality at the level of the total economy, are not appropriate - especially in...

More than 6.4 million U.S. properties entered foreclosure between 2007 and 2009. Among the dire consequences of foreclosure is a possible increase in crime. Foreclosures and the subsequent property vacancies and residential turnover may affect community crime rates by increasing opportunities...

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of...

This paper examines the nexus between financial development and economic growth in Ghana between 1970 and 2020. The study adopted the Bayesian Model Averaging (BMA) techniques to address issues of model uncertainty due to many potential explanatory variables that could influence growth. The...

The rapidly growing literature on the relationship between energy consumption and economic growth has not univocally identified the "real" causal relationship yet. We argue that bivariate models, which analyze the causality at the level of the total economy, are not appropriate - especially in...

In this paper we propose a test for a set of linear restrictions in a Vector Autoregressive Moving Average (VARMA) model. This test is based on the autoregressive metric, a notion of distance between two univariate ARMA models, M0 and M1, introduced by Piccolo in 1990. In particular, we show...

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of...