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

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

ARFIMA

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

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

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

Business calendars

• 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 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

Tests for unit roots

• Dickey–Fuller
  • Modified Dickey–Fuller t test proposed by Elliott, Rothenberg, and Stock
  • Augmented Dickey–Fuller test
• Phillips–Perron

Graphs and tables

• Autocorrelations and partial correlations
• Cross-correlations
• Cumulative sample spectral density
• Periodograms
• Line plots
• Range plot with lines

Tests for white noise

• Portmanteau’s test
• Bartlett’s periodogram test

Support for Haver Analytics database

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
• IRF management tools

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

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

Factor variables

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

Marginal analysis

• 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

Contrasts

• 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

Pairwise comparisons

• 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