TSP Features

A complete list of TSP commands is also available, as well as a list of features added to TSP 5.0.

• Convenient loading of raw data from free format, fixed format, binary, Lotus or Excel files
• Flexible transformations of series using algebraic expressions, with a full set of operators and functions (transcendental, probability, rounding, etc.)
• Display and save the original and transformed time series; print a table; graphics on DOS, Windows and MacOS; write to external files in many formats
• Single equation estimation using a variety of techniques:
  • OLSQ Ordinary least squares (with extensive diagnostics)
  • 2SLS Two stage least squares (instrumental variables)
  • LIML Limited Information Maximum Likelihood
  • AR1 Regression with first-order serial correlation
  • PDL (polynomial distributed lag) variables can be included in all these methods, and Shiller distributed lags can be included in OLSQ
  • LAD Least Absolute Deviations regression - minimizes sum of absolute values of residuals, instead of squares of residuals. A robust estimator. Handles quantile and censored quantile estimation.
  • LMS Least Median of Squares regression - minimizes the median squared (or absolute value) residual. Another robust estimator, useful for outlier detection.
• Retrieval of coefficient, residuals, predicted dependent variables, and other test statistics from these estimators for later use
• Estimation of equation systems by simultaneous methods:
  • LSQ least squares, minimum distance, nonlinear least squares, multivariate regression and three-stage least squares, both linear and nonlinear
  • GMM Generalized method of moments estimation (multiple equation, nonlinear, panel data)
  • FIML Estimation of a complete nonlinear simultaneous model by the full information maximum likelihood method
• Extensive hypothesis testing facilities available following any estimation command.
• Simulation, either dynamic or static, of an estimated nonlinear or linear simultaneous model
• Time Series methods:
  • BJ Box Jenkins identification, estimation and forecasting
  • KALMAN Kalman Filter (State Space) estimation
  • ARCH ARCH, GARCH, GARCH-M models of autoregressive conditional heteroskedasticity
  • VAR Vector autoregressive models
  • COINT for unit roots, Engle-Granger and Johansen trace tests for cointegration
• Maximum likelihood and qualitative dependent variable estimation:
  • PROBIT Binary probit model
  • TOBIT Tobit (0/+) model
  • SAMPSEL Sample selection model (generalized Tobit)
  • LOGIT Conditional, multinomial, and mixed logit for two or more choices
  • ORDPROB Ordered probit model
  • INTERVAL Interval regression (ordered probit with category boundary values known)
  • POISSON Poisson model for count data
  • NEGBIN Negative Binomial models for count data
  • ML User-programmed likelihood functions with automatically generated analytic derivatives, examples for Box-Cox, frontier production, nested logit, switching regression, bivariate probit
• Panel Data estimation, fixed and random effects for balanced or unbalanced data
• In addition, TSP's special features give user flexibility in extending the program:
  • Ease in handling of multi-sectoral data and equations
  • Full matrix algebra
  • Control of program flow by logical expressions and tests, GOTO statements, and loops
  • User-written procedures, callable with arguments
  • Random number generation for many distributions: normal, multivariate normal, Cauchy, Student's t, Laplace, exponential, Poisson, negative binomial, gamma, uniform, empirical (bootstrap)
• Extensive library of TSP procedures, developed to solve special econometric problems.

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