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Diagnostic Tools for Nonlinearity in Spatial Models

de Graaff, Thomas; Florax, Raymond J.G.M.; Nijkamp, Peter; … - 1998

econometrics to nonlinearity tests for empirical data series, in particular the so-called BDS (Brock, Dechert, Scheinkman) test. A …

Persistent link: https://www.econbiz.de/10010324411

Testing for Nonlinear Structure and Chaos in Economic Time. A Comment.

Hommes, Cars; Manzan, Sebastiano - 2006

This short paper is a comment on ``Testing for Nonlinear Structure and Chaos in Economic Time Series'' by Catherine Kyrtsou and Apostolos Serletis. We summarize their main results and discuss some of their conclusions concerning the role of outliers and noisy chaos. In particular, we include...

Persistent link: https://www.econbiz.de/10010325181

Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach

Koop, Gary; van Dijk, Herman K.; Hoek, Henk - 1997

In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are analogous to...

Persistent link: https://www.econbiz.de/10010324436

Testing for Integration using Evolving Trend and Seasonals Models: A Bayesian Approach

Koop, Gary; van Dijk, Herman K. - 1999

In this paper, we make use of state space models toinvestigate the presence of stochastic trends in economic time series. Amodel is specified where such a trend can enter either in the autoregressiverepresentation or in a separate state equation. Tests based on the formerare analogous to...

Persistent link: https://www.econbiz.de/10010324712

Forecasting Daily Time Series using Periodic Unobserved Components Time Series Models

Koopman, Siem Jan; Ooms, Marius - 2004

month. We focus on forecasting performance and the underlying periodic forecast function, defined by the in … forecasting based on state space models with regressor variables. Our methods are illustrated by an application to time series of … periodic flexibility helps help in simulated out-of-sample forecasting for two extra years of data. …

Persistent link: https://www.econbiz.de/10010325388

Measuring Pattern, Amplitude and Timing Differences between Monetary and Non-Monetary Seasonal Factors of Tourism - the Case of Aruba

Ridderstaat, Jorge; Nijkamp, Peter - 2013

Seasonality is a frequent and important occurrence in the tourism industry, with simultaneous effects on both the …

Persistent link: https://www.econbiz.de/10010326369

Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory

Asai, Manabu; McAleer, Michael; Peiris, Shelton - 2017

-SLM model to three stock market indices. The estimation and forecasting results indicate the adequacy of considering general …

Persistent link: https://www.econbiz.de/10011819498

Modeling Trigonometric Seasonal Components for Monthly Economic Time Series

Hindrayanto, Irma; Aston, John A.D; Koopman, Siem Jan; … - 2010

The basic structural time series model has been designed for the modelling and forecasting of seasonal economic time …

Persistent link: https://www.econbiz.de/10014198312

Forecasting the Variability of Stock Index Returns with Stochastic Volatility Models and Implied Volatility

Hol, 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/10010324427

The Stochastic Volatility in Mean Model

Koopman, Siem Jan; Uspensky, Eugenie Hol - 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/10010324578