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Measuring pattern, amplitude and timing differences between monetary and non-monetary seasonal factors of tourism : the case of Aruba

Ridderstaat, Jorge; Nijkamp, Peter - 2013

Persistent link: https://www.econbiz.de/10010191316

Forecasting in a changing world : from the great recession to the COVID-19 pandemic

Artemova, Mariia; Blasques, Francisco; Koopman, Siem Jan; … - 2021

We develop a new targeted maximum likelihood estimation method that provides improved forecasting for misspecified linear autoregressive models. The method weighs data points in the observed sample and is useful in the presence of data generating processes featuring structural breaks, complex...

Persistent link: https://www.econbiz.de/10012416341

Combinations schemes for turning points predictions

Billio, Monica; Casarin, Roberto; Ravazzolo, Francesco; … - 2011

Persistent link: https://www.econbiz.de/10009720757

Forecasting regional labour market developments under spatial heterogeneity and spatial autocorrelation

Longhi, Simonetta; Nijkamp, Peter - 2005

error models – to correct for misspecification due to neglected spatial autocorrelation in the data set. Our empirical …

Persistent link: https://www.econbiz.de/10011343272

In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation driven models

Blasques, Francisco; Koopman, Siem Jan; Łasak, Katarzyna; … - 2015

We study the performance of alternative methods for calculating in-sample confidence and out of-sample forecast bands for time-varying parameters. The in-sample bands reflect parameter uncertainty only. The out-of-sample bands reflect both parameter uncertainty and innovation uncertainty. The...

Persistent link: https://www.econbiz.de/10011295703

Forecasting daily time series using periodic unobserved components time series models

Koopman, Siem Jan; Ooms, Marius - 2004

We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend and seasonal. Periodic time series models allow dynamic characteristics to depend on the period of the year, month, week or day. In the...

Persistent link: https://www.econbiz.de/10011342560

Periodic unobserved cycles in seasonal time series with an application to US unemployment

Koopman, Siem Jan; Ooms, Marius; Hindrayanto, Irma - 2006

This paper discusses identification, specification, estimation and forecasting for a general class of periodic unobserved components time series models with stochastic trend, seasonal and cycle components. Convenient state space formulations are introduced for exact maximum likelihood...

Persistent link: https://www.econbiz.de/10011350384

Matrix-valued spatial autoregressions with dynamic and robust heterogeneous spillovers

Lin, Yicong; Lucas, André; Ye, Shiqi - 2025

We introduce a new time-varying parameter spatial matrix autoregressive model that integrates matrix-valued time series, heterogeneous spillover effects, outlier robustness, and time-varying parameters in one unified framework. The model allows for separate dynamic spatial spillover effects...

Persistent link: https://www.econbiz.de/10015423404

Conditional score residuals and diagnostic analysis of serial dependence in time series models

Blasques, F.; Gorgi, P.; Koopman, Siem Jan - 2021

. These residuals offer reliable and powerful diagnostic tools for testing residual autocorrelation. Furthermore, they can be … employed in models of which it is not clear how to define residuals. The asymptotic properties of the empirical autocorrelation … testing residual autocorrelation, when compared to squared GARCH residuals. We finally show how a diagnostic analysis can be …

Persistent link: https://www.econbiz.de/10012666810

Misspecification in linear spatial regression models

Florax, Raymond J. G. M.; Nijkamp, Peter - 2003

, referring to both heterogeneity and interdependence of phenomena occurring in two-dimensional space. Spatial autocorrelation or …

Persistent link: https://www.econbiz.de/10011334352