SHAZAM 計量經濟學軟體
SHAZAM是一套計量經濟學軟體,專門為計量經濟學家，統計學家，生物統計學家，計量社會學家，心理測量學家，政治統計學家，和其他需要使用統計技術的人士開發的功能全面的電腦應用程式。
SHAZAM is a comprehensive computer program for econometricians, statisticians, biometricians, sociometricians, psychometricians, politicometricians and others who use statistical techniques.
Currently, SHAZAM is used in 89 countries from the Northernmost (University of Tromso, Norway) to the Southernmost (University of Otago, New Zealand) Universities in the world and in Antarctica.
The primary strength of SHAZAM is for the estimation and testing of many types of regression models. The SHAZAM command language has great flexibility and provides capabilities for programming procedures.
SHAZAM has an interface to the GNUPLOT package for high quality graphics.
SHAZAM includes features for:
- data transformations, handling missing observations, matrix manipulation, evaluation of derivatives and integrals, data sorting, computation of cumulative distribution functions for a variety of probability distributions;
- descriptive statistics, calculation of price indexes, moving averages, exponential smoothing, seasonal adjustment, financial time series, ARIMA (Box-Jenkins) time series models, Dickey-Fuller and Phillips-Perron unit root tests, tests for cointegration, nonparametric density estimation;
- OLS estimation, restricted least squares, weighted least squares, ridge regression, distributed lag models, generalized least squares, estimation with autoregressive or moving average errors, estimation with heteroskedastic errors, ARCH and GARCH models, Box-Cox regressions, probit models, logit models, tobit models, estimation using regression quantiles (including MAD estimation), regression with non-normal errors (including exponential regression, beta regression and poisson regression), regression with time varying coefficients, nonparametric methods, generalized entropy methods, fuzzy set models;
- linear and nonlinear hypothesis testing, calculation of confidence intervals and ellipse plots, computation of the Newey-West autocorrelation consistent covariance matrix, regression diagnostic tests (including tests for heteroskedasticity, CUSUM tests, RESET specification error tests), computation of p-values for many test statistics (including the p-value for the Durbin-Watson test), forecasting;
- nonlinear least squares, estimation of systems of linear and nonlinear equations by SURE, 2SLS and 3SLS, generalized method of moments (GMM) estimation, pooled time-series cross-section methods;
- principal components and factor analysis, principal components regression, linear programming, minimizing and maximizing nonlinear functions, solving nonlinear simultaneous equations.