{smcl} {* *! version 1 agu2013}{...} {cmd:help dfsummary} {hline} {title:Title} {p2colset 8 21 21 2}{...} {p2col:{hi:dfsummary} {hline 2}}Performs the (Augmented) Dickey-Fuller unit-root test and reports a summary table for different lags.{p_end} {p2colreset}{...} {title:Syntax} {p 8 18 2} {cmd:dfsummary} {varname} {ifin} [{cmd:,} {it:options}] {synoptset 14 tabbed}{...} {synopthdr} {synoptline} {syntab:Main} {synopt:{opt nocon:stant}}suppress constant term in regression{p_end} {synopt:{opt t:rend}}include trend term in regression{p_end} {synopt:{opt s:easonal}}include seasonal dummies in regression{p_end} {synopt:{opt reg:ress}}display regression table{p_end} {synopt:{opt l:ag(#)}}include {it:#} lagged differences{p_end} {synoptline} {p2colreset}{...} {p 4 6 2} You must {opt tsset} your data before using {opt dfsummary}; see {manhelp tsset TS}. {p_end} {p 4 6 2} {it:varname} may contain time-series operators; see {help tsvarlist}. {p_end} {title:Description} {pstd} {cmd: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. {cmd:dfsummary} reports a summary table for different lags of the difference of the variable in the regression {bf:(see Definitions)}. {title: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 {bf: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%. {title:Options} {dlgtab:Main} {phang} {opt noconstant} suppresses the constant term (intercept) in the model and indicates that the process under the null hypothesis is a random walk without drift. {opt noconstant} cannot be used with the {opt trend} or {opt seasonal} option. {phang} {opt 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 {opt noconstant} option. {phang} {opt seasonal} include seasonal dummies in regression {bf:(weekly, monthly, quarterly, halfyearly, frequency data only)}. This option may not be used with the {opt noconstant} option. {phang} {opt regress} specifies that the associated regression table appear in the output. By default, the regression table is not produced. {phang} {opt lags(#)} specifies the number of lagged difference terms to include in the covariate list {bf:(must be an integer >= 1)}. {title:Examples} {phang}{cmd:. dfsummary varname}{p_end} {phang}{cmd:. dfsummary varname, lag(5) s t reg}{p_end} {title:Saved results} {synoptset 15 tabbed}{...} {p2col 5 15 19 2: Matrix}{p_end} {synopt:{cmd:r(results)}}summary table{p_end} {p2colreset}{...} {title:Author} Maximo Sangiacomo {hi:Email: {browse "mailto:msangia@hotmail.com":msangia@hotmail.com}}