Order Stata
Time series
Handle all the statistical challenges inherent to time-series
data—autocorrelations, common factors, autoregressive
conditional heteroskedasticity, unit roots, cointegration, and
much more. From graphing and filtering to fitting complex
multivariate models, let Stata reveal the structure in your
time-series data.
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

FRED data
New
- Over 470,000 U.S. and international
economic and financial time series
- Search or browse by subject, title,
or source
- Download directly into Stata
- Put series on a common periodicity
- Easily update datasets containing dozens,
or even hundreds, of series
- Easy-to-use interface for searching and
browsing
- Commands for updating datasets and replicability
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
Read about the new DSGE features.
See tests, predictions, and effects.
See
New in Stata 15
for more about what was added in Stata 15.