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May 18, 2013
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Stata Time-Series Reference ManualCopyright 2011ISBN-13: 978-1-59718-094-8 Pages: 717; paperback Price $60.00 |
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See a larger photo of the front cover Overview of the Stata 12 documentation Table of contents Introduction to time-series manual (pdf) Introduction to time-series commands (pdf) Sample entries (pdf):
arima—ARIMA, ARMAX, and other dynamic regression models
Glossary (pdf)corrgram—Tabulate and graph autocorrelations var intro—Introduction to vector autoregression models Subject and author index (pdf) Download the datasets for this manual | |
| intro (pdf) | Introduction to time-series manual |
| time series (pdf) | Introduction to time-series commands |
| arch | Autoregressive conditional heteroskedasticity (ARCH) family of estimators |
| arch postestimation | Postestimation tools for arch |
| arfima | Autoregressive fractionally integrated moving-average models |
| arfima postestimation | Postestimation tools for arfima |
| arima (pdf) | ARIMA, ARMAX, and other dynamic regression models |
| arima postestimation | Postestimation tools for arima |
| corrgram (pdf) | Tabulate and graph autocorrelations |
| cumsp | Cumulative spectral distribution |
| dfactor | Dynamic-factor models |
| dfactor postestimation | Postestimation tools for dfactor |
| dfgls | DF-GLS unit-root test |
| dfuller | Augmented Dickey–Fuller unit-root test |
| fcast compute | Compute dynamic forecasts of dependent variables after var, svar, or vec |
| fcast graph | Graph forecasts of variables computed by fcast compute |
| haver | Load data from Haver Analytics database |
| irf | Create and analyze IRFs, dynamic-multiplier functions, and FEVDs |
| irf add | Add results from an IRF file to the active IRF file |
| irf cgraph | Combine graphs of IRFs, dynamic-multiplier functions, and FEVDs |
| irf create | Obtain IRFs, dynamic-multiplier functions, and FEVDs |
| irf ctable | Combine tables of IRFs, dynamic-multiplier functions, and FEVDs |
| irf describe | Describe an IRF file |
| irf drop | Drop IRF results from the active IRF file |
| irf graph | Graph IRFs, dynamic-multiplier functions, and FEVDs |
| irf ograph | Graph overlaid IRFs, dynamic-multiplier functions, and FEVDs |
| irf rename | Rename an IRF result in an IRF file |
| irf set | Set the active IRF file |
| irf table | Create tables of IRFs, dynamic-multiplier functions, and FEVDs |
| mgarch | Multivariate GARCH models |
| mgarch ccc | Constant conditional correlation multivariate GARCH models |
| mgarch ccc postestimation | Postestimation tools for mgarch ccc |
| mgarch dcc | Dynamic conditional correlation multivariate GARCH models |
| mgarch dcc postestimation | Postestimation tools for mgarch dcc |
| mgarch dvech | Diagonal vech multivariate GARCH models |
| mgarch dvech postestimation | Postestimation tools for mgarch dvech |
| mgarch vcc | Varying conditional correlation multivariate GARCH models |
| mgarch vcc postestimation | Postestimation tools for mgarch vcc |
| newey | Regression with Newey–West standard errors |
| newey postestimation | Postestimation tools for newey |
| pergram | Periodogram |
| pperron | Phillips–Perron unit-root test |
| prais | Prais–Winsten and Cochrane–Orcutt regression |
| prais postestimation | Postestimation tools for prais |
| psdensity | Parametric spectral density estimation after arima, arfima, and ucm |
| rolling | Rolling-window and recursive estimation |
| sspace | State-space models |
| sspace postestimation | Postestimation tools for sspace |
| tsappend | Add observations to a time-series dataset |
| tsfill | Fill in gaps in time variable |
| tsfilter | Filter a time-series, keeping only selected periodicities |
| tsfilter bk | Baxter–King time-series filter |
| tsfilter bw | Butterworth time-series filter |
| tsfilter cf | Christiano–Fitzgerald time-series filter |
| tsfilter hp | Hodrick–Prescott time-series filter |
| tsline | Plot time-series data |
| tsreport | Report time-series aspects of a dataset or estimation sample |
| tsrevar | Time-series operator programming command |
| tsset | Declare data to be time-series data |
| tssmooth | Smooth and forecast univariate time-series data |
| tssmooth dexponential | Double-exponential smoothing |
| tssmooth exponential | Single-exponential smoothing |
| tssmooth hwinters | Holt–Winters nonseasonal smoothing |
| tssmooth ma | Moving-average filter |
| tssmooth nl | Nonlinear filter |
| tssmooth shwinters | Holt–Winters seasonal smoothing |
| ucm | Unobserved-components models |
| ucm postestimation | Postestimation tools for ucm |
| var intro (pdf) | Introduction to vector autoregression models |
| var | Vector autoregression models |
| var postestimation | Postestimation tools for var |
| var svar | Structural vector autoregression models |
| var svar postestimation | Postestimation tools for svar |
| varbasic | Fit a simple VAR and graph IRFs or FEVDs |
| varbasic postestimation | Postestimation tools for varbasic |
| vargranger | Perform pairwise Granger causality tests after var or svar |
| varlmar | Perform LM test for residual autocorrelation after var or svar |
| varnorm | Test for normally distributed disturbances after var or svar |
| varsoc | Obtain lag-order selection statistics for VARs and VECMs |
| varstable | Check the stability condition of VAR or SVAR estimates |
| varwle | Obtain Wald lag-exclusion statistics after var or svar |
| vec intro | Introduction to vector error-correction models |
| vec | Vector error-correction models |
| vec postestimation | Postestimation tools for vec |
| veclmar | Perform LM test for residual autocorrelation after vec |
| vecnorm | Test for normally distributed disturbances after vec |
| vecrank | Estimate the cointegrating rank of a VECM |
| vecstable | Check the stability condition of VECM estimates |
| wntestb | Barlett's periodogram-based test for white noise |
| wntestq | Portmanteau (Q) test for white noise |
| xcorr | Cross-correlogram for bivariate time series |
| Glossary (pdf) | |
| Subject and author index (pdf) | |
