Nonlinear Time Series Analysis
ARCH Test for time series
Cook and Vougas(2009) nonlinear unit root test function
Cuestas and Garratt(2011) nonlinear unit root test function
Cuestas and Ordonez(2014) nonlinear unit root test function
Enders and Granger_1998 nonlinear unit root test function
Enders and Siklos(2001) Nonlinear Cointegration Test Function
STAR Vector Error Correction Model
Harvey and Mills(2002) nonlinear unit root test function
Hu and Chen(2016) nonlinear unit root test function
Kilic(2011) nonlinear unit root test function
Kruse(2011) nonlinear unit root test function
Kapetanios, Shin and Snell(2006) nonlinear cointegration test function
Kapetanios, Shin and Snell(2003) nonlinear unit root test function
Leybourne Newbold and Vougas (1998) nonlinear unit root test function
Mc.Leod.Li nonlinearity test
MTAR Vector Error Correction Model
Park and Shintani(2012) nonlinear unit root test function
Pascalau(2007) nonlinear unit root test function
SETAR model estimation
Sollis(2004) nonlinear unit root test function
Sollis(2009) nonlinear unit root test function
Terasvirta (1994) nonlinearity test
Vougas(2006) nonlinear unit root test function
Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.