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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
• Model selection criteria New

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
• Model selection criteria New

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

• 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
• 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

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

Support for Haver Analytics database

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