*! version 1 agu2013
help dfsummary

Title

dfsummary -- Performs the (Augmented) Dickey-Fuller unit-root test and reports a summary table for different lags.

Syntax

dfsummary
varname
[if] [in]
[, options]

Main
noconstant suppress constant term in regression

trend include trend term in regression

seasonal include seasonal dummies in regression

regress display regression table

lag(#) include # lagged differences

You must tsset your data before using dfsummary; see
[TS] tsset.

varname may contain time-series operators; see tsvarlist.

Description

dfsummary performs the augmented Dickey-Fuller test that a variable follows a unit-root process.
The null hypothesis is that the variable contains a unit root, and the alternative is that the variable was generated by a stationary process.
You may optionally exclude the constant, include a trend term, include seasonal dummies and include lagged values of the difference
of the variable in the regression. dfsummary reports a summary table for different lags of the difference
of the variable in the regression (see Definitions).

Definitions

The default summary table report for the sequence of ADF(n)...ADF(0) tests, for j=n,...,0:

t-adf the t-value of the corresponding ADF test
beta Y_1	the coefficient on the lagged level
\sigma Root MSE of the regression
lag the number of lagged differences
t-DY_lag	t-value of the longest lag difference
t-prob significance of the longest lag difference (P > |t|)
F-prob significance level of the F-test on the lags dropped up to that point
AIC Akaike's Bayesian information criteria
BIC Schwarz's Bayesian information criteria

Note: Critical values are given (MacKinnon approximate p-value if there is a constant or trend in associated regression).
Significance of the ADF test is marked by asterisks: * indicates significance at 5%, ** at 1%.

Options

Main

noconstant suppresses the constant term (intercept) in the
model and indicates that the process under the null hypothesis is a random
walk without drift. noconstant cannot be used with the trend
or seasonal option.

trend specifies that a trend term be included in the associated
regression and that the process under the null hypothesis is a random walk,
perhaps with drift. This option may not be used with the noconstant option.

seasonal include seasonal dummies in regression (weekly, monthly, quarterly, halfyearly, frequency data only).
This option may not be used with the noconstant option.

regress specifies that the associated regression table appear in the
output. By default, the regression table is not produced.

lags(#) specifies the number of lagged difference terms to include in
the covariate list (must be an integer >= 1).

Examples

. dfsummary varname

. dfsummary varname, lag(5) s t reg

Saved results

Matrix

r(results) summary table

Author

Maximo Sangiacomo
Email: msangia@hotmail.com