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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.



  • 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 New

  • Dynamic regression
  • Autoregression
  • Tables of transition probabilities
  • Tables of expected durations
  • Standard and robust variance estimates


  • 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

Watch Using freduse to download time-series data from the Federal Reserve.

Forecast models

  • 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


  • 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

State-space models

  • 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

Dynamic-factor models

  • 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 New

  • View and run all postestimation features for your command
  • Automatically updated as estimation commands are run

Tests for structural breaks New

  • 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

Time-series filters

  • 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

    See tests, predictions, and effects.

    See New in Stata 14 for more about what was added in Stata 14.





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