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~isPartOf:"CESifo working papers : the international platform of Ludwig-Maximilians University's Center for Economic Studies and the Ifo Institute"
~isPartOf:"Cambridge working papers in economics"
~isPartOf:"Cambridge-INET working papers"
~isPartOf:"Cowles Foundation discussion paper"
~isPartOf:"International economic review"
~isPartOf:"Journal of business & economic statistics : JBES ; a publication of the American Statistical Association"
~isPartOf:"The review of economic studies"
~isPartOf:"Working paper / Department of Econometrics and Business Statistics, Monash University"
~isPartOf:"Working paper"
~person:"Martin, Gael M."
~subject:"Autokorrelation"
~subject:"Börsenkurs"
~subject:"Heteroskedastizität"
~subject:"Prognoseverfahren"
~subject:"Schätztheorie"
~subject:"Time series analysis"
~subject:"USA"
~subject:"United States"
~type_genre:"Graue Literatur"
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Autokorrelation
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Martin, Gael M.
Phillips, Peter C. B.
67
Hyndman, Rob J.
56
Gao, Jiti
55
Snyder, Ralph D.
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Athanasopoulos, George
21
McAleer, Michael
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Peng, Bin
17
Poskitt, Donald Stephen
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Kapetanios, George
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Pesaran, M. Hashem
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Dong, Chaohua
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Harvey, Andrew C.
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Linton, Oliver
14
Koehler, Anne B.
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Yu, Jun
11
Ord, John Keith
10
Chen, Xiaohong
9
Forbes, Catherine Scipione
9
Giraitis, Liudas
9
Li, Degui
9
Blazsek, Szabolcs
8
Escribano, Álvaro
8
Vahid, Farshid
8
McCabe, Brendan Peter Martin
7
Pan, Guangming
7
Panagiotelis, Anastasios
7
Bailey, Natalia
6
Caporin, Massimiliano
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Lieberman, Offer
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Piger, Jeremy Max
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Timmermann, Allan
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Yang, Yanrong
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Ben Taieb, Souhaib
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Chang, Chia-Lin
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Engsted, Tom
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Frazier, David T.
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Grose, Simone D.
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CESifo working papers : the international platform of Ludwig-Maximilians University's Center for Economic Studies and the Ifo Institute
Cambridge working papers in economics
Cambridge-INET working papers
Cowles Foundation discussion paper
International economic review
Journal of business & economic statistics : JBES ; a publication of the American Statistical Association
The review of economic studies
Working paper / Department of Econometrics and Business Statistics, Monash University
Working paper
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Variational Bayes in state space models : inferential and predictive accuracy
Frazier, David T.
;
Loiza-Maya, Ruben
;
Martin, Gael M.
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2022
Persistent link: https://www.econbiz.de/10013193949
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2
Focused Bayesian prediction
Loiza-Maya, Ruben
;
Martin, Gael M.
;
Frazier, David T.
-
2020
Persistent link: https://www.econbiz.de/10012606751
Saved in:
3
High-frequency jump tests : which test should we use?
Maneesoonthorn, Worapree
;
Martin, Gael M.
;
Forbes, …
-
2020
-
(Revised working paper 17/18)
Persistent link: https://www.econbiz.de/10012606872
Saved in:
4
Forecasting observables with particle filters : any filter will do!
Leung, Patrick
;
Forbes, Catherine Scipione
;
Martin, Gael M.
-
2019
Persistent link: https://www.econbiz.de/10012606152
Saved in:
5
Optimal bias correction of the logperiodogram estimator of the fractional paramete : a Jackknife Approach
Nadarajah, K.
;
Martin, Gael M.
;
Poskitt, Donald Stephen
-
2019
Persistent link: https://www.econbiz.de/10012592265
Saved in:
6
Approximate Bayesian forecasting
Frazier, David T.
;
Maneesoonthorn, Worapree
;
Martin, Gael M.
-
2018
Persistent link: https://www.econbiz.de/10012583287
Saved in:
7
Issues in the estimation of mis-specified models of fractionally integrated processes
Martin, Gael M.
;
Nadarajah, K.
;
Poskitt, Donald Stephen
-
2018
Persistent link: https://www.econbiz.de/10012583573
Saved in:
8
Construction and visualization of optimal confidence sets for frequentist distributional forecasts
Harris, David
;
Martin, Gael M.
;
Perera, Indeewara
; …
-
2017
Persistent link: https://www.econbiz.de/10011782085
Saved in:
9
Auxiliary likelihood-based approximate Bayesian computation in state space models
Martin, Gael M.
;
McCabe, Brendon P. M.
;
Frazier, David T.
; …
-
2016
Persistent link: https://www.econbiz.de/10011781699
Saved in:
10
Data-driven particle filters for Particle Markov Chain Monte Carlo
Leung, Patrick
;
Forbes, Catherine Scipione
;
Martin, Gael M.
-
2016
Persistent link: https://www.econbiz.de/10011781784
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