GAUSS Time-Series Panel Data (TSPDLIB)¶

Description¶

Unit root, cointegration, and causality testing tools for time series and panel data. Includes extensive coverage of testing in the presence of structural breaks.

Installation¶

The GAUSS Time Series and Panel data tests library can be installed and updated directly in GAUSS using the GAUSS package manager. It requires a working copy of GAUSS 21+.

Commands¶

Time Series Stationarity Tests¶

`adf()`	Augmented Dickey-Fuller unit root test.
`adf_1br()`	Augmented Dickey-Fuller unit root test with one structural break.
`adf_2br()`	Augmented Dickey-Fuller unit root test with two structural breaks.
`fourier_adf()`	Augmented Dickey-Fuller unit root test with flexible Fourier form structural breaks.
`fourier_gls()`	Local generalized least squares unit root test with flexible Fourier form structural breaks.
`fourier_kpss()`	KPSS stationarity test with flexible Fourier form structural breaks.
`fourier_lm()`	LM unit root test with flexible Fourier form structural breaks.
`kpss_1break()`	KPSS stationary test with one structural break.
`kpss_2break()`	KPSS stationary test with two structural breaks.
`lm_1break()`	LM unit root test with one structural break.
`lm_2break()`	LM unit root test with two structural breaks.
`lmkpss()`	Performs the Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) stationarity test.
`pp()`	Phillips and Perron unit root test (Perron, P., & Ng, S. (1996)).
`quantile_adf()`	Quantile augmented Dickey-Fuller unit root test.
`rals_adf()`	Augmented Dickey-Fuller unit root test with the RALS technique for non-normal errors.
`ralslm()`	LM unit root test with the RALS technique for non-normal errors.
`ralslm_breaks()`	Augmented Dickey-Fuller unit root test with 1 or 2 breaks and the RALS technique for non-normal errors.
`mgls()`	MGLS unit root test.
`erspt()`	ERS point optimal unit root test.

Panel Data Unit Root Tests¶

`cips()`	A simple unit root test in the presence of cross-section dependence.
`bng_panic()`	Panel analysis of idiosyncratic and common components (PANIC) test of nonstationarity. Computes the Pe test on ADF p-values found in Bai & Ng (2004).
`bng_panicnew()`	Panel analysis of idiosyncratic and common components (PANIC) test of nonstationarity. Pooled Pa, Pb, and PMSB tests in Bai & Ng (2010).
`jwl_panicadj()`	Panel analysis of idiosyncratic and common components (PANIC) test of nonstationarity. Computes the Ze and Ze+ tests in Westerlund & Larsson (2009).
`jwr_panicca()`	Panel analysis of idiosyncratic and common components (PANIC) test of nonstationarity. Computes the Pooled Pa, Pb, and PMSB tests in Westerlund & Reese (2016).
`pdfzk()`	Panel stationary test with gradual shifts.

Cointegration Tests¶

`coint_egranger()`	Engle-Granger residual-based test of the null hypothesis of no cointegration.
`coint_ghansen()`	Test of the null hypothesis of no cointegration against the alternative of cointegration with a structural break in the mean.
`coint_cissano()`	Lagrange Multiplier‐type statistic to test the null hypothesis of cointegration allowing for the possibility of a structural break.
`coint_hatemij()`	Test of the null hypothesis of no cointegration against the alternative of cointegration with two structural breaks.
`coint_pouliaris()`	Asymptotic critical values for residual based tests for cointegration.
`coint_shin()`	A residual-based test for the null of cointegration using a structural single equation model.
`coint_tsongetal()`	Test of the null hypothesis of cointegration allowing for structural breaks of unknown form in deterministic trend by using the Fourier form.
`coint_maki()`	Test of the null hypothesis of no cointegration against the alternative of cointegration with an unknown number of breaks.

Causality Tests¶

`granger()`	Tests for Granger causality of specified variables.
`panel_fisher()`	Tests for Granger causality in heterogeneous mixed panels with bootstrap critical values.
`panel_surwald()`	Tests for Granger causality in heterogeneous mixed panels with bootstrap critical values.
`panel_zhnc()`	Tests for Granger causality in heterogeneous mixed panels with bootstrap critical values.

Reference¶

The tspdlib library is written for GAUSS by Saban Nazlioglu, Department of International Trade & Finance, Pamukkale University-Türkiye.

If using this code please include the following citation: Nazlioglu, S (2021) TSPDLIB: GAUSS Time Series and Panel Data Methods (Version 2.0). Source Code. https://github.com/aptech/tspdlib

Applications

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