Assessing potential output and the output gap is essential for policy-making and fiscal surveillance. The European Commission proposes a production function methodology that involves the estimation of two classes of Gaussian state space models. This paper presents the R package RGAP which...

The paper assesses the impact of adding information on financial cycles on the output gap estimates for eight advanced economies using two unobserved components models: a reduced form extended Hodrick-Prescott filter, and a standard semi-structural unobserved components model. To complement...

This paper builds an unobserved components model that combines a multivariate filter approach with a Cobb-Douglas production function. This combination allows potential output estimates to incorporate more economic structure than the traditional production function approach, while retaining the...

This paper analyses identification for multivariate unobserved components models in which the innovations to trend and cycle are correlated. We address order and rank criteria as well as potential non-uniqueness of the reduced-form VARMA model. Identification is shown for lag lengths larger than...

This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood estimator of the model based on the Kalman Smoother and the Expectation Maximisation algorithm. The asymptotic...

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...