Order
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 ![](/features/i/pdfdoc-icon.png)
- ARMA
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- ARMAX
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- Standard and robust variance estimates
- Static and dynamic forecasts
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- Linear constraints
- Multiplicative seasonal ARIMA
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- Spectral densities
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- Impulse–response functions (IRFs)
- Parametric autocorrelation estimates and graphs
- Check stability conditions
- Model selection criteria
New
ARCH/GARCH ![](/features/i/pdfdoc-icon.png)
- GARCH
- APARCH
- EGARCH
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- 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
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- Linear constraints
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Multivariate GARCH
- Diagonal VECH models
- Conditional correlation models
- Constant conditional correlation
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- Dynamic conditional correlation
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- Varying conditional correlation
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- Multivariate normal or multivariate Student's t errors
- Standard and robust variance estimates
- Static and dynamic forecasts
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- 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
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- Linear constraints
- Spectral densities
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- Impulse–response functions (IRFs)
- Parametric autocorrelation estimates and graphs
- Model selection criteria
New
Regression with AR(1) disturbances
- Heteroskedasticity-and-autocorrelation-consistent covariance matrices
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- Cochrane–Orcutt/Prais–Winsten methods
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- ARMA/ARIMA estimators
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- ARCH estimators
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Unobserved components model (UCM)
- Trend-cycle decomposition
- Stochastic cycles
- Estimation by state-space methods
- Standard and robust variance estimates
- Static and dynamic forecasts
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- Linear constraints
- Spectral densities
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FRED data
- Over 566,000 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
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- Cross-correlations
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- Cumulative sample spectral density
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- Periodograms
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- Line plots
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- Range plot with lines
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- Patterns of missing data
Time-series functions
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- 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
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Time-series operators ![](/features/i/pdfdoc-icon.png)
- L, lag
- F, leads
- D, differences
- S#, seasonal lag
Time-series time and date formats ![](/features/i/pdfdoc-icon.png)
- Default formats for clock-time daily, weekly, monthly, quarterly, half-yearly, yearly
- High-frequency data with millisecond resolution
- User-specified formats
Time-series filters
- Baxter–King band-pass filter
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- Butterworth high-pass filter
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- Christiano–Fitzgerald band-pass filter
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- Hodrick–Prescott high-pass filter
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Time-series smoothers ![](/features/i/pdfdoc-icon.png)
- Moving average (MA)
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- Single exponential
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- Double exponential
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- Holt–Winters nonseasonal exponential
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- Holt–Winters seasonal exponential
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- Nonlinear
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- Forecasting and smoothing
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Support for Haver Analytics database ![](/features/i/pdfdoc-icon.png)
- Import haver command makes using Haver datasets even easier
- Quickly access worldwide economics and financial datasets
See tests, predictions, and effects.
See New in Stata 18 to learn about what was added in Stata 18.