Cross-sectionally Augmented IPS Test for Unit Roots in Panel Models

Source: R/test_cips.R

Cross-sectionally augmented Im, Pesaran and Shin (IPS) test for unit roots in panel models.

cipstest(
  x,
  lags = 2L,
  type = c("trend", "drift", "none"),
  model = c("cmg", "mg", "dmg"),
  truncated = FALSE,
  ...
)

Arguments

x

an object of class "pseries",

lags

integer, lag order for Dickey-Fuller augmentation,

type

one of "trend" (default), "drift", "none",

model

one of "cmg" (default), "mg", "dmg",

truncated

logical, specifying whether to calculate the truncated version of the test (default: FALSE),

...

further arguments passed to critvals.cips (non-exported function).

Value

An object of class "htest".

Details

Pesaran's (Pesaran 2007) cross-sectionally augmented version of the IPS unit root test (Im et al. 2003) (H0: pseries has a unit root) is a so-called second-generation panel unit root test: it is in fact robust against cross-sectional dependence, provided that the default model="cmg" is calculated. Else one can obtain the standard (model="mg") or cross-sectionally demeaned (model="dmg") versions of the IPS test.

Argument type controls how the test is executed:

  • "none": no intercept, no trend (Case I in (Pesaran 2007) ),

  • "drift": with intercept, no trend (Case II),

  • "trend" (default): with intercept, with trend (Case III).

References

Im KS, Pesaran MH, Shin Y (2003). “Testing for unit roots in heterogenous panels.” Journal of Econometrics, 115(1), 53-74.

Pesaran MH (2007). “A simple panel unit root test in the presence of cross-section dependence.” Journal of Applied Econometrics, 22(2), 265–312.

Author

Giovanni Millo

Examples


data("Produc", package = "plm")
Produc <- pdata.frame(Produc, index=c("state", "year"))
## check whether the gross state product (gsp) is trend-stationary
cipstest(Produc$gsp, type = "trend")
#> Warning: p-value greater than printed p-value
#> 
#> 	Pesaran's CIPS test for unit roots
#> 
#> data:  Produc$gsp
#> CIPS test = -0.58228, lag order = 2, p-value = 0.1
#> alternative hypothesis: Stationarity
#>