time.hlp

是一个经济学管理应用软件 很难找的 但是经济学学生又必须用到

{smcl}
{* 06apr2005}{...}
{cmd:help time}
{hline}

{title:Title}

{pstd}
{hi:[TS] time series} {hline 2} Introduction
to time-series commands}


{title:Description}

{pstd}
Some Stata commands are written directly for performing time-series
analyses.  This entry provides an index to these commands.

{pstd}
Many other Stata commands allow time-series operators in expressions and
varlists (e.g., {helpb regress}, {helpb summarize}, {helpb graph},
{helpb list}, ...).  See {varlist} for a table of time-series operators.

{pstd}
Before using time-series analysis commands or time-series operators, you
must declare your data to be time series and indicate the time variable.  This
is done using the {cmd:tsset} command; see {helpb tsset}.

{pstd}
If your interest is in analyzing cross-sectional time-series (panel)
datasets, see {help xt}.


    {title:Data management tools and time-series operators}

{p2colset 9 25 27 2}{...}
{p2col :{helpb haver}}Load data from Haver Analytics database{p_end}
{p2col :{helpb tsset}}Declare a dataset to be time-series data{p_end}
{p2col :{helpb tsfill}}Fill in missing times with missing observations in time-series data{p_end}
{p2col :{helpb tsappend}}Add observations to a time-series dataset{p_end}
{p2col :{helpb tsreport}}Report time-series aspects of a dataset or estimation sample{p_end}
{p2col :{helpb tsrevar}}Time-series operator programming command{p_end}


    {title:Univariate time-series}

      {bf:Estimators}

{p2col :{helpb arima}}Autoregressive integrated moving-average models{p_end}
{p2col :{helpb arch}}Autoregressive conditional heteroskedasticity (ARCH) family of estimators{p_end}
{p2col :{helpb newey}}Regression with Newey-West standard errors{p_end}
{p2col :{helpb prais}}Prais-Winsten regression and Cochrane-Orcutt regression{p_end}


      {bf:Time-series smoothers and filters}

{p2colset 9 31 33 2}{...}
{p2col :{helpb tssmooth ma}}Moving-average filter{p_end}
{p2col :{helpb tssmooth dexponential}}Double-exponential smoothing{p_end}
{p2col :{helpb tssmooth exponential}}Single-exponential smoothing{p_end}
{p2col :{helpb tssmooth hwinters}}Holt-Winters nonseasonal smoothing{p_end}
{p2col :{helpb tssmooth shwinters}}Holt-Winters seasonal smoothing{p_end}
{p2col :{helpb tssmooth nl}}Nonlinear filter{p_end}


      {bf:Diagnostic tools}

{p2colset 9 25 27 2}{...}
{p2col :{helpb corrgram}}Tabulate and graph autocorrelations{p_end}
{p2col :{helpb xcorr}}Cross-correlogram for bivariate time series{p_end}
{p2col :{helpb cumsp}}Cumulative spectral distribution{p_end}
{p2col :{helpb pergram}}Periodogram{p_end}
{p2col :{helpb dfgls}}DF-GLS unit-root test{p_end}
{p2col :{helpb dfuller}}Augmented Dickey-Fuller unit-root test{p_end}
{p2col :{helpb pperron}}Phillips-Perron unit-roots test{p_end}
{p2col :{helpb dwstat}}Durbin-Watson d statistic{p_end}
{p2col :{helpb durbina}}Durbin's alternative test for serial correlation{p_end}
{p2col :{helpb bgodfrey}}Breusch-Godfrey test for higher-order serial correlation{p_end}
{p2col :{helpb archlm}}Engle's LM test for the presence of autoregressive conditional heteroskedasticity{p_end}
{p2col :{helpb wntestb}}Bartlett's periodogram-based test for white noise{p_end}
{p2col :{helpb wntestq}}Portmanteau (Q) test for white noise{p_end}


    {title:Multivariate time series}

      {bf:Estimators}

{p2col :{helpb var}}Vector autoregression models{p_end}
{p2col :{helpb svar}}Structural vector autoregression models{p_end}
{p2col :{helpb varbasic}}Fit a simple VAR and graph impulse-response functions{p_end}
{p2col :{helpb vec}}Vector error-correction models{p_end}

      {bf:Diagnostic tools}

{p2col :{helpb varlmar}}Obtain LM statistics for residual autocorrelation after {cmd:var} or {cmd:svar}{p_end}
{p2col :{helpb varnorm}}Test for normally distributed disturbances after {cmd:var} or {cmd:svar}{p_end}
{p2col :{helpb varsoc}}Obtain lag-order selection statistics for VARs and VECMs{p_end}
{p2col :{helpb varstable}}Check the stability condition of VAR or SVAR estimates{p_end}
{p2col :{helpb varwle}}Obtain Wald lag-exclusion statistics after {cmd:var} or {cmd:svar}{p_end}
{p2col :{helpb veclmar}}Obtain LM statistics for residual autocorrelation after {cmd:vec}{p_end}
{p2col :{helpb vecnorm}}Test for normally distributed disturbances after {cmd:vec}{p_end}
{p2col :{helpb vecrank}}Estimate the cointegrating rank using Johansen's framework{p_end}
{p2col :{helpb vecstable}}Check the stability condition of VECM estimates{p_end}


      {bf:Forecasting, inference, and interpretation}

{p2col :{helpb irf create}}Obtain impulse-response functions and FEVDs{p_end}
{p2col :{helpb fcast compute}}Compute dynamic forecasts of dependent variables after {cmd:var}, {cmd:svar}, or {cmd:vec}{p_end}
{p2col :{helpb vargranger}}Perform pairwise Granger causality tests after {cmd:var} or {cmd:svar}{p_end}


      {bf:Graphs and tables}

{p2col :{helpb irf graph}}Graph impulse-response functions and
FEVDs{p_end}
{p2col :{helpb irf cgraph}}Combine graphs of impulse-response functions and FEVDs{p_end}
{p2col :{helpb irf ograph}}Graph overlaid impulse-response functions and FEVDs{p_end}
{p2col :{helpb irf table}}Create tables of impulse-response functions and FEVDs{p_end}
{p2col :{helpb irf ctable}}Combine tables of impulse-response functions and FEVDs{p_end}
{p2col :{helpb fcast graph}}Graph forecasts of dependent variables computed by fcast compute{p_end}


      {bf:Results management tools}

{p2col :{helpb irf add}}Add IRF results from one IRF file to another{p_end}
{p2col :{helpb irf describe}}Describe an IRF file{p_end}
{p2col :{helpb irf drop}}Drop IRF results from the active IRF file{p_end}
{p2col :{helpb irf rename}}Rename an IRF result in an IRF file{p_end}
{p2col :{helpb irf set}}Set the active IRF file{p_end}


{title:Also see}

{psee}
Manual:  {bf:[U] 12.5.4 Time-series formats},{break}
{bf:[U] 24.3 Time-series dates},{break}
{bf:[U] 26.13 Models with time-series data},{break}
{bf:[TS] intro}, {bf:[TS] time series}

{psee}
Online:  {help tdates},
{help tfmt},
{helpb tsset},
{varlist},
{help xt};
and list above
{p_end}