This study develops a framework for testing hypotheses on structural parameters in in-complete models. Such models make set-valued predictions and hence do not generally yield a unique likelihood function. The model structure, however, allows us to construct tests based on the least favorable...

We explore the international spillovers from fiscal policy shocks via trade in Europe. A fiscal expansion stimulates domestic activity, which leads to more foreign exports and, hence, higher foreign output. To quantify this, we combine a panel VAR model in government spending, net taxes and GDP...

A growing empirical literature has sought to determine the effects of monetary policy shocks on exchange rates and other important macroeconomic variables. This paper seeks to add to this literature in the area of emerging markets by using the Vector Auto-Regression (VAR) methodology in an...

A growing empirical literature has sought to determine the effects of monetary policy shocks on exchange rates and other important macroeconomic variables. This paper seeks to add to this literature in the area of emerging markets by using the Vector Auto-Regression (VAR) methodology in an...

This paper provides a discussion of the `housing market' channels of the monetarytransmission mechanism (MTM) and offers some evidence on institutional differences in the European housing and mortgage markets. Using a number of VAR models, estimated individually for nine European countries over...

This paper considers inference in logistic regression models with high dimensional data. We propose new methods for estimating and constructing confidence regions for a regression parameter of primary interest α0, a parameter in front of the regressor of interest, such as the treatment variable...

We develop uniformly valid confidence regions for regression coefficients in a high-dimensional sparse least absolute deviation/median regression model. The setting is one where the number of regressors p could be large in comparison to the sample size n, but only s << n of them are needed to accurately describe the regression function. Our new methods are based on the instrumental median regression estimator that assembles the optimal estimating equation from the output of the post l1-penalized median regression and post l1-penalized least squares in an auxiliary equation. The estimating equation is immunized against non-regular estimation of nuisance part of the median regression function, in the sense of Neyman. We establish that in a homoscedastic regression model, the instrumental median regression estimator of a single regression coefficient is asymptotically root-n normal uniformly with respect to the underlying sparse model. The resulting confidence regions are valid uniformly with respect to the underlying model. We illustrate the value of uniformity with Monte-Carlo experiments which demonstrate that standard/naive post-selection inference breaks down over large parts of the parameter space, and the proposed method does not. We then generalize our method to the case where p1 > n regression coefficients...</<>