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Parameter driven multi-state duration models : simulated vs. approximate maximum likelihood estimation
Monteiro, André Antonio - 2008
Persistent link: https://www.econbiz.de/10003690030
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Long memory modelling of inflation with stochastic variance and structural breaks
Bos, Charles S.; Koopman, Siem Jan; Ooms, Marius - 2007
We investigate changes in the time series characteristics of postwar U.S. inflation. In a model-based analysis the conditional mean of inflation is specified by a long memory autoregressive fractionally integrated moving average process and the conditional variance is modelled by a stochastic...
Persistent link: https://www.econbiz.de/10011373822
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Parameter driven multi-state duration models : simulated vs. approximate maximum likelihood estimation
Monteiro, André Antonio - 2008 - This version: February 15, 2008
evaluating the likelihood, two of the methods rely on Monte Carlo integration with importance sampling techniques. The third …
Persistent link: https://www.econbiz.de/10011374420
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The dynamic Skellam model with applications
Koopman, Siem Jan; Lit, Rutger; Lucas, André - 2014
extension of numerically accelerated importance sampling techniques. We illustrate the new model by two empirical studies and …
Persistent link: https://www.econbiz.de/10010253460
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On importance sampling for state space models
Jungbacker, Borus; Koopman, Siem Jan - 2005
Persistent link: https://www.econbiz.de/10003248123
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Finite sample optimality of score-driven volatility models
Blasques, Francisco; Lucas, André; Vlodrop, Andries van - 2017
We study optimality properties in finite samples for time-varying volatility models driven by the score of the predictive likelihood function. Available optimality results for this class of models suffer from two drawbacks. First, they are only asymptotically valid when evaluated at the...
Persistent link: https://www.econbiz.de/10011772958
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What do professional forecasters actually predict?
Nibbering, Didier; Paap, Richard; Wel, Michel van der - 2015
In this paper we study what professional forecasters actually explain. We use spectral analysis and state space modeling to decompose economic time series into a trend, a business-cycle, and an irregular component. To examine which components are captured by professional forecasters we regress...
Persistent link: https://www.econbiz.de/10011305773
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Quantifying time-varying forecast uncertainty and risk for the real price of oil
Aastveit, Knut Are; Cross, Jamie; Dijk, Herman K. van - 2021
We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil …, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach …
Persistent link: https://www.econbiz.de/10012545165
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Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil
Aastveit, Knut Are; Cross, Jamie; Dijk, Herman K. van - 2021
We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil …, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter …-varying forecast uncertainty and risk for the real price of oil over the period 1974-2018. We show that the combination approach …
Persistent link: https://www.econbiz.de/10012795319
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Attrition in panel data and the estimation of dynamic labor markets models
Berg, Gerard J. van den; Lindeboom, Maarten - 1994
Persistent link: https://www.econbiz.de/10000151678