ARIMA

• ARMA
• ARMAX
• standard and robust variance estimates
• Static and dynamic forecasts
  
• linear constraints
• multiplicative seasonal ARIMA
• Spectral densities
  

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  

• Long-memory processes
• Fractional integration
• Standard and robust variance estimates
• Static and dynamic forecasts
• Linear constraints
• Spectral densities

• Trend-cycle decomposition
• Stochastic cycles
• Estimation by state-space methods
• Standard and robust variance estimates
• Static and dynamic forecasts
• Linear constraints
• Spectral densities
  

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
• diagnostic and tests
  • cointegration tests
  • Granger causality tests
  • LM tests for residual autocorrelation
  • tests for normailty of residuals
  • lag order seleciton statistics
  • stability analysis using eigenvalues
  • Wald lag exclusion statistics
• geographical and tabular presentations and comparisons of IRFs and FEVDs
• IRF management tools

Time-series functions

• string conversion to date; daily, weekly, monthly, quarterly, half-yearly, yearly
• dates from numeric arguments
• date 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 date formats

• Default formats for clock-time daily, weekly, monthly, quarterly, half-yearly, yearly
• High-frequency data with millisecond resolution
  
• user-specified formats

• Define your own calendars
• Format variables using business calendar format
• Convert between business dates and regular dates
• Lags and leads calculated according to calendar
  

Rolling and recursive estimation

Regression diagnostics

• LM test for ARCH effects
• Breusch–Godfrey LM test for serial correlation
• Durbin alternative statistic test for serial correlation
• Durbin-Watson statistic

Regression with AR(1) disturbances

• White's method for heteroskedasticity robust variances
• two-step or iterated methods
• Cochrane–Orcutt, Prais–Winsten, and ARMA/ARIMA estimators

Time-series smoothers

• moving average (MA)
• single exponential
• double exponential
• Holt–Winters nonseasonal exponential
• Holt–Winters seasonal exponential
• nonlinear
• forecasting and smoothing

Graphs and tables

• autocorrelations and partial correlations
• cross-correlations
• cumulative sample spectral density
• periodograms
• line plots
• range plots with lines

Tests for white noise

• Portmanteau's test
• Bartlett's periodogram test

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 smoothers

• moving average (MA)
• single exponential
• double exponential
• Holt–Winters nonseasonal exponential
• Holt–Winters seasonal exponential
• nonlinear
• forecasting and smoothing

Tests for white noise

• Portmanteau’s test
• Bartlett’s periodogram test
  

Support for Haver Analytics database

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
  

• 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
  

• Baxter–King band-pass filter
• Butterworth high-pass filter
• Christiano–Fitzgerald band-pass filter
• Hodrick–Prescott high-pass filter

• Automatically create indicators based on categorical variables
  
• Form interactions among discrete and continuous variables
  
• Include polynomial terms
  
• Perform contrasts of categories/levels
  

• Estimated marginal means
  
• Marginal and partial effects
  
• Average marginal and partial effects
  
• Least-squares means
  
• Predictive margins
  
• Adjusted predictions, means, and effects
  
• Contrasts of margins*
• Pairwise comparisons of margins*
• Profile plots*
• Graphs of margins and marginal effects*

• Analysis of main effects, simple effects, interaction effects, partial interaction effects, and nested effects
  
• Comparisons against reference groups, of adjacent levels, or against the grand mean
  
• Orthogonal polynomials
  
• Helmert contrasts
  
• Custom contrasts
  
• ANOVA-style tests
  
• Contrasts of nonlinear responses
  
• Multiple-comparison adjustments
  
• Balanced and unbalanced data
  
• Contrasts in odds-ratio metric
  
• Contrasts of means, intercepts, and slopes
  
• Graphs of contrasts
  
• Interaction plots
  

• Compare estimated means, intercepts, and slopes
  
• Compare marginal means, intercepts, and slopes
  
• Balanced and unbalanced data
  
• Nonlinear responses
  
• Multiple-comparison adjustments: Bonferroni, Šidák, Scheffé, Tukey HSD, Duncan, and Student-Newman-Keuls adjustments
  
• Group comparisons that are significant
  
• Graphs of pairwise comparisons