Jiancheng Jiang and Sha Yu Jiancheng Jiang and Sha Yu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.70825

## Abstract

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Scientific World Journal. 2013:1-8

3471-3480

2011;2(1):50-59

92 Time Series Analysis and Applications

981-990

In this chapter, we review nonlinear models for vector time series data and develop new nonparametric estimation and inference for them. Vector time series data exist widely in practice. In financial markets, multiple time series are usually correlated. When analyzing several interdependent time series, in general one should consider them as a single vector time series fitted by multivariate models, which provides a useful tool for modeling interdependencies among multiple time series and for simultaneously analyzing feedback and Granger causality effects. Since nonlinear features are widely observed in time series, we consider nonlinear methodology for modeling nonlinear vector time series data, which allows flexibility in the model structure and avoids the curse of dimensionality.

Keywords: cointegration, VAR, multivariate threshold autoregressive model, nonparametric smoothing, generalized likelihood ratio
