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~isPartOf:"Applied economics"
~isPartOf:"Computational economics"
~isPartOf:"Discussion paper / Tinbergen Institute"
~isPartOf:"Discussion paper"
~isPartOf:"International journal of forecasting"
~isPartOf:"Technological forecasting & social change : an international journal"
~person:"Carriero, Andrea"
~person:"Galvão, Ana Beatriz C."
~person:"Koopman, Siem Jan"
~subject:"Estimation theory"
~subject:"Nationaleinkommen"
~subject:"Prognoseverfahren"
~subject:"VAR-Modell"
~subject:"Welt"
~subject:"World"
~subject:"Zeitreihenanalyse"
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Indian Economic Outlook 2008-0...
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Estimation theory
Nationaleinkommen
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Forecasting model
18
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12
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12
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forecasting
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2
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Carriero, Andrea
Galvão, Ana Beatriz C.
Koopman, Siem Jan
Gupta, Rangan
11
McAleer, Michael
10
Moosa, Imad A.
9
Marcellino, Massimiliano
7
Blasques, Francisco
5
Balcilar, Mehmet
4
Buch, Claudia M.
4
Burns, Kelly
4
Dijk, Dick van
4
Ravazzolo, Francesco
4
Asai, Manabu
3
Clark, Todd E.
3
Eraslan, Sercan
3
Franses, Philip Hans
3
Gausden, Robert
3
Gkonkas, Periklēs
3
Götz, Thomas B.
3
Hartwig, Benny
3
Hasan, Mohammad S.
3
Hindrayanto, Irma
3
Lucas, André
3
Nikolopoulos, Konstantinos
3
Papadimitriou, Theophilos
3
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3
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3
Rubaszek, Michał
3
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3
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2
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2
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2
Bas, Eren
2
Bayer, Fábio M.
2
Bańbura, Marta
2
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2
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Applied economics
Computational economics
Discussion paper / Tinbergen Institute
Discussion paper
International journal of forecasting
Technological forecasting & social change : an international journal
FRB of Cleveland Working Paper
4
Tinbergen Institute Discussion Paper
4
Working Paper
4
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3
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3
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3
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2
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2
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1
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1
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1
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1
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1
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1
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ECONIS (ZBW)
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1
Low frequency and weighted likelihood solutions for mixed frequency dynamic factor models
Blasques, Francisco
;
Koopman, Siem Jan
;
Mallee, Max I. P.
-
2014
The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...
Persistent link: https://www.econbiz.de/10010391543
Saved in:
2
Nowcasting and
forecasting
economic growth in the Euro area using principal components
Hindrayanto, Irma
;
Koopman, Siem Jan
;
Winter, Jasper de
-
2014
short-term
forecasting
models. These empirical findings have been established for different macroeconomic data sets and … specification is most effective in its
forecasting
performance. Furthermore, the forecast performances of the different … extended empirical out-of-sample
forecasting
competition for quarterly growth of gross domestic product in the euro area and …
Persistent link: https://www.econbiz.de/10010395082
Saved in:
3
Spillover dynamics for systemic risk measurement using spatial financial time series models
Blasques, Francisco
;
Koopman, Siem Jan
;
Lucas, André
; …
-
2014
We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...
Persistent link: https://www.econbiz.de/10010391531
Saved in:
4
Addressing COVID-19 outliers in BVARs with stochastic volatility
Carriero, Andrea
;
Clark, Todd E.
;
Marcellino, Massimiliano
-
2022
the pandemic period, as well as for earlier subsamples of relatively high volatility. In historical
forecasting
, outlier …
Persistent link: https://www.econbiz.de/10013184356
Saved in:
5
Shadow-rate VARs
Carriero, Andrea
;
Clark, Todd E.
;
Marcellino, Massimiliano
-
2023
VARs are a popular tool for
forecasting
and structural analysis, but ill-suited to handle occasionally binding …
Persistent link: https://www.econbiz.de/10014320745
Saved in:
6
The information in systemic risk rankings
Nucera, Federico
;
Schwaab, Bernd
;
Koopman, Siem Jan
; …
-
2015
We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...
Persistent link: https://www.econbiz.de/10010532581
Saved in:
7
Measuring financial cycles in a model-based analysis : empirical evidence for the United States and the euro area
Galati, Gabriele
;
Hindrayanto, Irma
;
Koopman, Siem Jan
; …
-
2016
We adopt an unobserved components time series model to extract financial cycles for the United States and the five largest euro area countries over the period 1970 to 2014. We find that credit, the credit-to-GDP ratio and house prices have medium-term cycles which share a few common statistical...
Persistent link: https://www.econbiz.de/10011456728
Saved in:
8
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
Saved in:
9
The stochastic volatility in mean model
Koopman, Siem Jan
;
Hol Uspensky, Eugenie
-
2000
In this paper we present an exact maximum likelihood treatment forthe estimation of a Stochastic Volatility in Mean(SVM) model based on Monte Carlo simulation methods. The SVM modelincorporates the unobserved volatility as anexplanatory variable in the mean equation. The same extension...
Persistent link: https://www.econbiz.de/10011303314
Saved in:
10
Forecasting
the variability of stock index returns with stochastic volatility models and implied volatility
Hol Uspensky, Eugenie
;
Koopman, Siem Jan
-
2000
returns and intradaily squared returns for
forecasting
horizons rangingfrom 1 to 10 days. For the daily squared returns we …
Persistent link: https://www.econbiz.de/10011304384
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