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Models with time-varying mean and variance : a robust analysis of US industrial production

Bos, Charles S.; Koopman, Siem Jan - 2010

Many seasonal macroeconomic time series are subject to changes in their means and variances over a long time horizon. In this paper we propose a general treatment for the modelling of time-varying features in economic time series. We show that time series models with mean and variance functions...

Persistent link: https://www.econbiz.de/10011379641

Outlier detection in structural time series models : the indicator saturation approach

Marczak, Martyna; Proietti, Tommaso - 2014

Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has at- tracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The...

Persistent link: https://www.econbiz.de/10010402941

Price Dividend Ratio and Long-Run Stock Returns : A Score Driven State Space Model

Delle Monache, Davide - 2020

In this paper we develop a general framework to analyze state space models with time-varying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

Persistent link: https://www.econbiz.de/10012842441

Price dividend ratio and long-run stock returns : a score driven state space model

Delle Monache, Davide; Petrella, Ivan; Venditti, Fabrizio - 2020

In this paper we develop a general framework to analyze state space models with timevarying system matrices where time variation is driven by the score of the conditional likelihood. We derive a new filter that allows for the simultaneous estimation of the state vector and of the time-varying...

Persistent link: https://www.econbiz.de/10012156426

Predictability, Real Time Estimation, and the Formulation of Unobserved Components Models

Proietti, Tommaso - 2019

The formulation of unobserved components models raises some relevant interpretative issues, owing to the existence of alternative observationally equivalent specifi cations, differing for the timing of the disturbances and their covariance matrix. We illustrate them with reference to unobserved...

Persistent link: https://www.econbiz.de/10014107235

Observation-driven filters for timeseries with stochastic trends and mixed causal non-causal dynamics

Blasques, Francisco; Koopman, Siem Jan; Mingoli, Gabriele - 2023

the forecasting accuracy against competitive alternative methods and conclude that our model-based forecasts outperform …

Persistent link: https://www.econbiz.de/10014380706

Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production

Bos, Charles S.; Koopman, Siem Jan - Tinbergen Institute - 2010

Persistent link: https://www.econbiz.de/10008838615

Models with Time-varying Mean and Variance: A Robust Analysis of U.S. Industrial Production

Bos, Charles S.; Koopman, Siem Jan - Tinbergen Instituut - 2010

Persistent link: https://www.econbiz.de/10011256745

An Alternative Bayesian Approach to Structural Breaks in Time Series Models

van den Hauwe, Sjoerd; Paap, Richard; van Dijk, Dick J.C. - 2011

We propose a new approach to deal with structural breaks in time series models. The key contribution is an alternative dynamic stochastic specification for the model parameters which describes potential breaks. After a break new parameter values are generated from a so-called baseline prior...

Persistent link: https://www.econbiz.de/10010325904

On the predictive content of nonlinear transformations of lagged autoregression residuals and time series observations

Rossen, Anja - 2014

able to provide more accurate forecasting results than linear models. Therefore, simple autoregressive processes are … observations and autoregression residuals. The proposed forecasting models are applied to a large set of macroeconomic and … autoregression residuals, are somewhat able to provide better forecasting results than simple linear models. Thus, it may be …

Persistent link: https://www.econbiz.de/10010435409