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Risk aggregation with copula for banking industry
Yoshiba, Toshinao - 2015
Persistent link: https://www.econbiz.de/10011375924
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Macroeconomic uncertainty and forecasting macroeconomic aggregates
Reif, Magnus - 2018
Can information on macroeconomic uncertainty improve the forecast accuracy for key macroeconomic time series for the US? Since previous studies have demonstrated that the link between the real economy and uncertainty is subject to nonlinearities, I assess the predictive power of macroeconomic...
Persistent link: https://www.econbiz.de/10011918367
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L1-regularization of high-dimensional time-series models with flexible innovations
Medeiros, Marcelo C.; Mendes, Eduardo F. - 2015
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume that both the number of covariates in the model and the number of candidate variables can increase with the sample size (polynomially or geometrically). In other...
Persistent link: https://www.econbiz.de/10010505038
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Principles and algorithms for forecasting groups of time series : locality and globality
Montero-Manso, Pablo; Hyndman, Rob J.; Liu, Fei; Peng, Bin - 2020
Persistent link: https://www.econbiz.de/10012610903
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On using predictive-ability tests in the selection of time-series prediction models : a Monte Carlo evaluation
Costantini, Mauro; Kunst, Robert M. - 2018
selecting the best forecasting model class in finite samples of practical relevance. Flanking such a horse race by predictive …
Persistent link: https://www.econbiz.de/10011895825
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l 1 - penalized likelihood smoothing of volatility processes allowing for abrupt changes
Neto, David; Sardy, Sylvain; Tseng, Paul - 2009
Persistent link: https://www.econbiz.de/10003926975
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Large vector autoregressions with asymmetric priors
Carriero, Andrea; Clark, Todd E.; Marcellino, Massimiliano - 2015 - This draft: November 2015
We propose a new algorithm which allows easy estimation of Vector Autoregressions (VARs) featuring asymmetric priors and time varying volatilities, even when the cross sectional dimension of the system N is particularly large. The algorithm is based on a simple triangularisation which allows to...
Persistent link: https://www.econbiz.de/10011389735
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Exploiting the monthly data flow in structural forecasting
Giannone, Domenico; Monti, Francesca; Reichlin, Lucrezia - 2015
predictive accuracy in now-casting and forecasting. Our empirical results show that both the monthly version of the DSGE and the …
Persistent link: https://www.econbiz.de/10011399325
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Likelihood-based analysis for dynamic factor models
Jungbacker, Borus; Koopman, Siem Jan - 2008
We present new results for the likelihood-based analysis of the dynamic factor model that possibly includes intercepts and explanatory variables. The latent factors are modelled by stochastic processes. The idiosyncratic disturbances are specified as autoregressive processes with mutually...
Persistent link: https://www.econbiz.de/10011373811
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With or without you : do financial data help to forecast industrial production?
Kitlinski, Tobias - 2015
This paper analyzes the forecasting performance of financial market data in comparison to other indicator groups to … evaluate the forecasting performance using a significance test. In addition, we investigate the stability of forecasting models …-term forecasting, especially for the US and longer forecast horizons. Nevertheless, the results indicate that the Great Recession was …
Persistent link: https://www.econbiz.de/10010529472