This article introduces the R package ExtremeBounds to perform extreme bounds analysis (EBA), a sensitivity test that examines how robustly the dependent variable of a regression model is related to a variety of possible determinants. ExtremeBounds supports Leamer's EBA that focuses on the upper...

The objective of this paper is to examine the impact of demutualization upon the performance of stock exchanges' in terms of their liquidity as per the share turnover velocity and the returns that are generated from the broad indexes. The impact of demutualization has been examined upon a global...

Estimation of empirical relationships is prone to bias. Economists have carefully identified and addressed sources of bias in structural and quasi-experimental approaches, but the randomized control trial (RCT) has only recently begun to receive such scrutiny. In this paper, we argue that...

This paper describes a modelling methodology for multivariate stochastic processes. The concept of multiple causality is discussed and a procedure to detect multiple causality is suggested. The data of a major Canadian supermarket is analyzed and a multivariate autoregressive model for this...

This paper examines point and density forecasts of real GDP growth, inflation and unemployment from the European Central Bank’s Survey of Professional Forecasters. We present individual uncertainty measures and introduce individual point- and density-based measures of disagreement. The...

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 proposes new estimation techniques for gravity models with zero trade values and heteroscedasticity. We propose various Heckman estimators with different distributions of the residuals, nonlinear forms of both selection and measure equations, and various process of the variance. We...

We introduce a data driven and model free approach for computing conditional expectations. The new method is based on classical techniques combined with machine learning methods. In particular, we consider kernel density estimation based on simulated risk factors combined with a control variate....

In this paper, we study the statistical properties of heterogeneous agent models with incomplete markets. Using a Bewley-Hugget-Aiyagari model we compute the equilibrium density function of wealth and show how it can be used for likelihood inference. We investigate the identifiability of the...

In this paper, we study the statistical properties of heterogeneous agent models with incomplete markets. Using a Bewley-Hugget-Aiyagari model we compute the equilibrium density function of wealth and show how it can be used for likelihood inference. We investigate the identifiability of the...