Identification via heteroskedasticity exploits differences in variances across regimes to identify parameters in simultaneous equations. I study weak identification in such models, which arises when variances change very little or the variances of multiple shocks change close to proportionally....

We demonstrate the use of comparative studies to resolve endogeneity issues. We provide a simple way to statistically test the corresponding condition which can help to ascertain that endogeneity is not posing any issues in a regression analysis. We give a step by step guide that can be...

We formulate a price discovery model in which the price discovery measures vary either locally, say, for instance, at intervals of 30 minutes or at a daily frequency. Given the empirical and theoretical evidence that price discovery measures relate to highly persistent fundamentals, we adopt a...

We estimate the domestic and international transmission of 4 types of US monetary policy, namely the current fed funds rate, Odyssean and Delphic forward guidance, and large-scale asset purchases. A credit channel is involved in the international transmission, driving large deviations from the...

A common approach for estimating the macroeconomic effects of oil supply news employs SVAR-IV models identified using changes in oil futures prices around OPEC quota announcements as an instrument. However, we show that the reduced-form oil price innovations, structural shocks, and the...

As part of the Colombian government's strategy to support the armed-conflict victims, a Graduation Program called "Transformando Mi Futuro" (Transforming my future) was implemented. Unlike other graduation programs, this one targets the urban population and has no assets transfers. To evaluate...

The paper focuses on establishing causation in regression analysis in observational settings. Simple static regression analysis cannot establish causality in the absence of a priori theory on possible causal mechanisms or controlled and randomized experiments. However, two regression based...

This paper reviews recent developments in nonparametric identi.cation of mea- surement error models and their applications in applied microeconomics, in particular, in empirical industrial organization and labor economics. Measurement error models describe mappings from a latent distribution to...

The measurement error problem in linear time series regression, with focus on the impact of error memory, modeled as nite-order MA processes, is considered. Three prototype models, two bivariate and one univariate ARMA, and ways of handling the problem by using instrumental variables (IVs) are...