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Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices
Casoli, Chiara; Lucchetti, Riccardo - 2021
In this article, we propose a cointegration-based Permanent-Transitory decomposition for nonstationary Dynamic Factor … Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a …-term stationary one. A Monte Carlo experiment shows that taking into account the cointegration structure in the DFM leads to a much …
Persistent link: https://www.econbiz.de/10012596987
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Permanent-Transitory decomposition of cointegrated time series via dynamic factor models, with an application to commodity prices
Casoli, Chiara; Lucchetti, Riccardo - In: The econometrics journal 25 (2022) 2, pp. 494-514
Persistent link: https://www.econbiz.de/10013253846
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Common factors, trends, and cycles in large datasets
Barigozzi, Matteo; Luciani, Matteo - 2017
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...
Persistent link: https://www.econbiz.de/10011803273
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Analyzing Commodity Futures Using Factor State-Space Models with Wishart Stochastic Volatility
Kleppe, Tore Selland - 2019
We propose a factor state-space approach with stochastic volatility to model and forecast the term structure of future contracts on commodities. Our approach builds upon the dynamic 3-factor Nelson-Siegel model and its 4-factor Svensson extension and assumes for the latent level, slope and...
Persistent link: https://www.econbiz.de/10012864217
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Capturing common components in high-frequency financial time series : a multivariate stochastic multiplicative error model
Hautsch, Nikolaus (contributor) - 2007
relations and cross-dependencies between the individual variables. This confirms economic theory and suggests more parsimonious …
Persistent link: https://www.econbiz.de/10003634717
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Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices
Casoli, Chiara; Lucchetti, Riccardo - 2021
In this article, we propose a cointegration-based Permanent-Transitory decomposition for non-stationary Dynamic Factor … Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a …-term stationary one. A Monte Carlo experiment shows that taking into account the cointegration structure in the DFM leads to a much …
Persistent link: https://www.econbiz.de/10012605977
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Robust implementation of a parsimonious dynamic factor model to nowcast GDP
Duarte, Pablo; Süssmuth, Bernd - 2014
Quarterly GDP figures usually are published with a delay of some weeks. A common way to generate GDP series of higher frequency, i.e. to nowcast GDP, is to use available indicators to calculate a single index by means of a common factor derived from a dynamic factor model (DFM). This paper deals...
Persistent link: https://www.econbiz.de/10010229863
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Analyzing business and financial cycles using multi-level factor models
Breitung, Jörg; Eickmeier, Sandra - 2014
This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero …
Persistent link: https://www.econbiz.de/10010373684
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Forecasting Stock Returns with Large Dimensional Factor Models
Giovannelli, Alessandro - 2020
We study equity premium out-of-sample predictability by extracting the information contained in a high number of macroeconomic predictors via large dimensional factor models. We compare the well known factor model with a static representation of the common components with a more general model...
Persistent link: https://www.econbiz.de/10012854353
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Dynamic semi-parametric factor model for functional expectiles
Burdejová, Petra; Härdle, Wolfgang - 2017
High-frequency data can provide us with a quantity of informa- tion for forecasting, help to calculate and prevent the future risk based on extremes. This tail behaviour is very often driven by ex- ogenous components and may be modelled conditional on other vari- ables. However, many of these...
Persistent link: https://www.econbiz.de/10011760356