The aim of this paper is to complement the MDE-SVAR approach when the weighting matrix is not optimal. In empirical studies, this choice is motivated by stochastic singularity or collinearity problems associated with the covariance matrix of Impulse Response Functions. Consequently, the...

This paper studies how the HP-Filter should be adjusted, when changing the frequency of observations. It complements the results of Baxter and King (1999) with an analytical analysis, demonstrating that the filter parameter should be adjusted by multiplying it with the fourth power of the...

The paper reconsiders the Hodrick-Prescott filter and the issue of a suitable choice of its smoothing parameter λ for quarterly data. To this end stochastic processes generate artificial data with a known growth trend and cyclical component, and a battery of Monte Carlo experiments tests what...

This chapter provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.

A recent article by J.D. Hamilton from 2018 attracted a great deal of attention, not only because of its telling title, "Why you should never use the Hodrick- Prescott filter", but also because it offered an alternative approach to detrending, the Hamilton regression filter (HRF). His...

We construct new estimates of potential output and the output gap using a multivariate approach that allows for an explicit role for measurement errors in the decomposition of real output. Because we include data on hours, output, employment, and the labor force, we are able to decompose our...

We propose the adaptive elastic net for estimating Vector Autoregressions. Unlike competing methods, this estimator preserves the standard structural-VAR toolkit but at the same time leads to accurate forecasts. We show validity of the bootstrap in constructing unconditional-on-model-selection...

We propose a blended approach which combines identification via heteroskedasticity with the widely used methods of sign restrictions, narrative restrictions, and external instruments.Since heteroskedasticity in the reduced form can be exploited to point identify a set of orthogonal shocks, its...

This paper examines the implications of using VARs in levels under the Max Share identification approach when variables exhibit unit or near-unit roots. We derive the asymptotic distributions of the Max Share estimator, demonstrating that it converges to a random matrix, resulting in...

This paper develops a novel method to correct small-sample bias in autoregressive roots of AR(p) models. We evaluate median-bias properties and variability of the bias-adjusted parameters by examining the accuracy of bias-adjusted impulse responses. Our simulation results show that bias...