ARIMA
ARMA
ARMAX
Standard and robust variance estimates
Static and dynamic forecasts
Linear constraints
Multiplicative seasonal ARIMA
Spectral densities
Impulse-response functions (IRFs)
Parametric autocorrelation estimates and graphs
Check stability conditions
ARCH/GARCH
GARCH
APARCH
EGARCH
NARCH
AARCH
GJR and more
ARCH in mean
Standard and robust variance estimates
Normal, Student’s t, or generalized error distribution
Multiplicative deterministic heteroskedasticity
Static and dynamic forecasts
Linear constraints
Multivariate GARCH
Diagonal VECH models
Conditional correlation models
Constant conditional correlation
Dynamic conditional correlation
Varying conditional correlation
Multivariate normal or multivariate Student’s t errors
Standard and robust variance estimates
Static and dynamic forecasts
Linear constraints
Markov-switching models
Dynamic regression
Autoregression
Tables of transition probabilities
Tables of expected durations
Standard and robust variance estimates
ARFIMA
Long-memory processes
Fractional integration
Standard and robust variance estimates
Static and dynamic forecasts
Linear constraints
Spectral densities
Impulse-response functions (IRFs)
Parametric autocorrelation estimates and graphs
Regression with AR(1) disturbances
Heteroskedasticity-and-autocorrelation-consistent covariance matrices
Cochrane–Orcutt/Prais–Winsten methods
ARMA/ARIMA estimators
ARCH estimators
Unobserved components model (UCM)*
Trend-cycle decomposition
Stochastic cycles
Estimation by state-space methods
Standard and robust variance estimates
Static and dynamic forecasts
Linear constraints
Spectral densities
Business calendars
Define your own calendars
Create calendar from dataset
Format variables using business calendar format
Convert between business dates and regular dates
Lags and leads calculated according to calendar
Graphs and tables
Autocorrelations and partial correlations
Cross-correlations
Cumulative sample spectral density
Periodograms
Line plots
Range plot with lines
Patterns of missing data
Time-series functions
String conversion to date: daily, weekly, monthly, quarterly, half-yearly, yearly
Dates and times from numeric arguments
Date and time literal support
Periodicity conversion, e.g., daily date to quarterly
Date and time ranges
Time-series operators
L, lag
F, leads
D, differences
S#, seasonal lag
Time-series time and date formats
Default formats for clock-time daily, weekly, monthly, quarterly, half-yearly, yearly
High-frequency data with millisecond resolution
User-specified formats
Support for Haver Analytics database
New import haver command makes using Haver datasets even easier
Quickly access worldwide economics and financial datasets
Combine results from multiple estimation commands
Specify identities and declare exogenous variables
Obtain dynamic and static forecasts
Use simulation methods to obtain prediction intervals
Specify alternative scenarios and perform “what-if” analyses
VAR/SVAR/VECM
Vector autoregression (VAR)
Structural vector autoregression (SVAR)
Vector error-correction models (VECM)
Impulse–response functions (IRFs)
Simple IRFs
Orthogonalized IRFs
Structural IRFs
Cumulative IRFs
Dynamic multipliers
Forecast-error variance decompositions (FEVD)
Static and dynamic forecasts
Diagnostics and tests
Cointegration tests
Granger causality tests
LM tests for residual autocorrelation
Tests for normality of residuals
Lag-order selection statistics
Stability analysis using eigenvalues
Wald lag-exclusion statistics
Graphical and tabular presentations and comparisons of IRFs and FEVDs
VARMA models
Structural time-series models
Stochastic general-equilibrium models
Stationary and nonstationary models
Standard and robust variance estimates
Static and dynamic forecasts
Linear constraints
Unobserved factors with vector autoregressive structure
Exogenous covariates
Autocorrelated disturbances in dependent variables’ equations
Standard and robust variance estimates
Static and dynamic forecasts
Linear constraints
Postestimation Selector
View and run all postestimation features for your command
Automatically updated as estimation commands are run
Tests for structural breaks
Unknown break point
Known break points
Tests for white noise
Portmanteau’s test
Bartlett’s periodogram test
Regression diagnostics
LM test for ARCH effects
Breusch–Godfrey LM test for serial correlation
Durbin alternative test for serial correlation
Durbin–Watson statistic
Tests for unit roots
Dickey–Fuller
Modified Dickey–Fuller t test proposed by Elliott, Rothenberg, and Stock
Augmented Dickey–Fuller test
Phillips–Perron
Baxter–King band-pass filter
Butterworth high-pass filter
Christiano–Fitzgerald band-pass filter
Hodrick–Prescott high-pass filter
Time-series smoothers
Moving average (MA)
Single exponential
Double exponential
Holt–Winters nonseasonal exponential
Holt–Winters seasonal exponential
Nonlinear
Forecasting and smoothing
Rolling and recursive estimation
Additional resources
In the spotlight: State-space models: Easier than they look
Introduction to Time Series Using Stata
NetCourse® 461: Introduction to Univariate Time Series with Stata
