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Modelling seasonalities in nonlinear inflation rates using SEASETARs
Gooijer, Jan G. de; Vidiella-i-Anguera, Antoni - 2000
In this paper, we present a new time series model, whichdescribes self-exciting threshold autoregressive (SETAR) nonlinearityand seasonality simultaneously. The model is termed multiplicativeseasonal SETAR (SEASETAR). It can be viewed as a special case of ageneral non-multiplicativeSETAR model...
Persistent link: https://www.econbiz.de/10011304390
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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 … efficient estimates of the parameters using a monthly dataset of core inflation for which we consider different subsamples of …
Persistent link: https://www.econbiz.de/10011373822
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Time series models with a common stochastic variance for analysing economic time series
Koopman, Siem Jan; Bos, Charles S. - 2002
concerned with themodelling and forecasting of two U.S. macroeconomic time series:inflation and industrial production. …
Persistent link: https://www.econbiz.de/10011327834
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Behavioral heterogeneity in US inflation dynamics
Cornea, Adriana; Hommes, Cars H.; Massaro, Domenico - 2013
Persistent link: https://www.econbiz.de/10010191432
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Why frequency matters for unit root testing
Boswijk, Herman Peter; Klaassen, Franc - 2004 - Rev.
It is generally believed that for the power of unit root tests, only the time span and not the observation frequency matters. In this paper we show that the observation frequency does matter when the high-frequency data display fat tails and volatility clustering, as is typically the case for...
Persistent link: https://www.econbiz.de/10011342578
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How large is average economic growth? : evidence from a robust method
Boswijk, Herman Peter; Franses, Philip Hans - 2001
This paper puts forward a method to estimate average economic growth, andits associated confidence bounds, which does not require a formal decision onpotential unit root properties. The method is based on the analysis of eitherdifference-stationary or trend-stationary time series models,...
Persistent link: https://www.econbiz.de/10011325976
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Reconstruction of the Spanish money supply, 1492-1810
Chen, Yao; Palma, Nuno; Ward, Felix - 2021 - Revision: May 2021
How did the Spanish money supply evolve in the aftermath of the discovery of large amounts of precious metals in Spanish America? We synthesize the available data on the mining of precious metals and their international flow to estimate the money supply for Spain from 1492 to 1810. Our estimate...
Persistent link: https://www.econbiz.de/10012510792
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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
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A note on "continuous invertibility and stable QML estimation of the EGARCH(1,1) model"
Blasques, Francisco; Gorgi, Paolo; Koopman, Siem Jan; … - 2015
We revisit Wintenberger (2013) on the continuous invertibility of the EGARCH(1,1) model. We note that the definition of continuous invertibility adopted in Wintenberger (2013) may not always be sufficient to deliver strong consistency of the QMLE. We also take the opportunity to provide other...
Persistent link: https://www.econbiz.de/10011401308
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Generalized autoregressive method of moments
Creal, Drew; Koopman, Siem Jan; Lucas, André; … - 2015
We introduce a new estimation framework which extends the Generalized Method of Moments (GMM) to settings where a subset of the parameters vary over time with unknown dynamics. To filter out the dynamic path of the time-varying parameter, we approximate the dynamics by an autoregressive process...
Persistent link: https://www.econbiz.de/10011431471