Search Results for author:
Found 5 papers, 1 papers with code
The boosted HP filter is more general than you might think
The global financial crisis and Covid recession have renewed discussion concerning trend-cycle discovery in macroeconomic data, and boosting has recently upgraded the popular HP filter to a modern machine learning device suited to data-rich and rapid computational environments.
Fully Modified Least Squares Cointegrating Parameter Estimation in Multicointegrated Systems
This paper departs from the parametric model, using a semiparametric formulation that reveals the explicit role that singularity of the long run conditional covariance matrix plays in determining multicointegration.
Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations
We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs.
Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs
This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs).
Boosting: Why You Can Use the HP Filter
The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research.
