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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.

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
• 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
• 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
• Trend-cycle decomposition
• Stochastic cycles
• Estimation by state-space methods
• Standard and robust variance estimates
• Static and dynamic forecasts
• Linear constraints
• Spectral densities
• Over 566,000 U.S. and international economic and financial time series
• Search or browse by subject, title, or source
• 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

• 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
• 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
• 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

Support for Haver Analytics database

• New import haver command makes using Haver datasets even easier
• Quickly access worldwide economics and financial datasets

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
• Bayesian VAR New
Read about the DSGE features, including the new Bayesian estimation for linear and nonlinear DSGEs.Updated
• 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
• Bayesian dynamic forecast after VAR New
• 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
• One threshold or multiple thresholds
• Specify number of thresholds
• Automatically choose the number of thresholds, using
• BIC
• AIC
• Hannan-Quinn information criterion
• Thresholds may be:
• Points in time
• Values of covariates in the regression model
• Values of variables not in the regression model
• View and run all postestimation features for your command
• Automatically updated as estimation commands are run

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

Rolling and recursive estimation

See New in Stata 17 to learn about what was added in Stata 17.