Holston, Laubach and Williams’ (2017) estimates of the natural rate of interest are driven by the downward trending behaviour of ‘other factor’ z(t). I show that their implementation of Stock and Watson’s (1998) Median Unbiased Estimation (MUE) to determine the size of parameter λ(z)...

This article presents a computationally efficient approach to sample from Gaussian state space models. The method is an instance of precision-based sampling methods that operate on the inverse variance-covariance matrix of the states (also known as precision). The novelty is to handle cases...

We present a model for hourly electricity load forecasting based on stochastically time-varying processes that are designed to account for changes in customer behaviour and in utility production efficiencies. The model is periodic: it consists of different equations and different parameters for...

This paper studies the evolution of long-run output and labour productivity growth rates in the G-7 countries during the post-war period. We estimate the growth rates consistent with a constant unemployment rate using time-varying parameter models that incorporate both stochastic volatility and...

Recent releases of X-13ARIMA-SEATS and JDemetra+ enable their users to choose between the non-parametric X-11 and the parametric ARIMA model-based approach to seasonal adjustment for any given time series without the necessity of switching between different software packages. To ease the...

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...

In this discussion paper we introduce time-varying parameters in the dynamic Nelson–Siegel yield curve model for the simultaneous analysis and forecasting of interest rates of different maturities. The Nelson–Siegel model has been recently reformulated as a dynamic factor model with vector...

Stock betas is estimated allowing for variability over time. A discussion and introduction to the Kalman flter state space approach is given, and the mathematical tools derived. A well established method using the state space approach to estimate stock betas is presented. The less known moving...

In a linear state space model Y(t)=BT(t) e(t), we investigate if the unobserved trend, T(t), cointegrates with the predicted trend, E(t), and with the estimated predicted trend, in the sense that the spreads are stationary. We find that this result holds for the spread B(T(t)-E(t)) and the...

A particle filter approach for general mixed-frequency state-space models is considered. It employs a backward smoother to filter high-frequency state variables from low-frequency observations. Moreover, it preserves the sequential nature of particle filters, allows for non-Gaussian shocks and...