adfp

a binary selection of 0 or 1. 0 uses the MAIC, a penalty on
k that accounts for the bias in the sum of the autoregressive coefficient.
 1 uses the more general form MIC.

penalty

The maximum number of lags for the vector autoregressions. An
upper bound of (12*(T/100)^.25)^8 is suggested
in Schwert (1989)

kmax

The minimum number of lags for the vector autoregression. k = 0
is a reasonable point.

kmin

a binary selection of 0 or -1. a value of -1 will modify the series
with a generalized least squares regression.

This function performs a modified Dickey-Fuller t test for a
unit root in which the series has been modified by a generalized least
squares regression.

This package contains a methodology that makes use of the factor
    structure of large dimensional panels to understand the nature of
    nonstationarity inherent in data. This is referred to as PANIC - Panel
    Analysis of Nonstationarity in Idiosyncratic and Common Components. PANIC
    (2004) includes valid pooling methods that allow panel tests to be
    constructed. PANIC (2004) can detect whether the nonstationarity in a
    series is pervasive, or variable specific, or both. PANIC (2010) includes
    two new tests on the idiosyncratic component that estimates the pooled
    autoregressive coefficient and sample moment, respectively. The PANIC model
    approximates the number of factors based on Bai and Ng (2002)

adfp: Generalized Least Squares Modified Dickey-Fuller t test

Description

Usage

Arguments

Value