bng_panic#

Purpose#

Computes the Pe test on ADF p-values found in Bai & Ng (2004) using panel analysis of idiosyncratic and common components (PANIC) test of nonstationarity.

Format#

{ ADFe, pval, lags, Pe, n_factors } = bng_panic(y, model[, pmax, ic_lags, kmax, ic_factors])#

Parameters:

y (TxN matrix) – Panel data to be tested.
model (Scalar) –
Model to be implemented.

1

Constant.

2

Constant and trend.
pmax (Scalar) – Optional, the maximum number of lags for \(\Delta y\). Default = 8.
ic_lags (Scalar) –
Optional, the information criterion used for choosing lags. Default = 3.

1

Akaike.

2

Schwarz.

3

t-stat significance.
kmax (Scalar) – Maximum number of factors. Default = 5.
ic_factors (Scalar) –
Information Criterion for optimal number of factors. Default = 1.

1

PCp criterion.

2

ICp criterion.

Returns:

ADFe (Scalar) – ADF statistic for idiosyncratic components for each cross-section.
pval (Scalar) – p-value of ADFe.
lags (Scalar) – Number of lags selected by chosen information criterion.
Pe (Scalar) – Pe statistic based on principal components with N(0,1).
n_factors (Scalar) – Number of factors by chosen information criterion

Examples#

library tspdlib;

// Load date file
y = loadd(__FILE_DIR $+ "PDe.dat");

/*
** Using the defaults
** for maximum number of lags,
** information criterions,
** and maximum number of factors.
*/

/*
** Model with constant
*/
model = 1;
{ ADFe, pval, lags, Pe, nf } = bng_panic(y, model);

Source#

pd_panic.src

1	Constant.
2	Constant and trend.

1	Akaike.
2	Schwarz.
3	t-stat significance.

1	PCp criterion.
2	ICp criterion.