Browsing by Subject "Instrumental variable"

This paper investigates Bayesian instrumental variable models with many instruments. The number of instrumental variables grows with the sample size and is allowed to be much larger than the sample size. With some sparsity condition on the coefficients on the instruments, we characterize a general prior specification where the posterior consistency of the parameters is established and calculate the corresponding convergence rate.

In particular, we show the posterior consistency for a class of spike and slab priors on the many potentially weak instruments. The spike and slab prior shrinks the number of instrumental variables, which avoids overfitting and provides uncertainty quantifications on the first stage. A simulation study is conducted to illustrate the convergence notion and estimation/selection performance under dependent instruments. Computational issues related to the Gibbs sampler are also discussed.

Instrumental variable (IV) models are widely used in the social and health sciences in situations where a researcher would like to measure a causal eect but cannot perform an experiment. Formally checking the assumptions of an IV model with a given dataset is impossible, leading many researchers to take as given a linear functional form and two-stage least squares tting procedure. In this paper, we propose a method for evaluating the validity of IV models using observed data and show that, in some cases, a more flexible nonlinear model can address violations of the IV conditions. We also develop a test that detects violations in the instrument that are present in the observed data. We introduce a new version of the validity check that is suitable for machine learning and provides optimization-based techniques to answer these questions. We demonstrate the method using both the simulated data and a real-world dataset.

This paper investigates whether government spending multipliers are time-varying.

The multipliers are measured using time-varying parameter (TVP) local projections.

This paper uses a simple modication to local projections that corrects for the inherent

autocorrelated errors in local projections. The results indicate that there is

evidence of time variation in government spending multipliers and that the results of

previous studies should be seriously questioned. The results also indicate that there

is significant time variation in the strength of Blanchard-Perotti and defense news

identified shocks.