TSP Features

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

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

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 including linear programming
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.

If you have any questions or comments about TSP please send an email to info@tspintl.com.

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