Click on the links below to access the software.
Data analysis:
Tabulating data.
Calculating summary statistics of the data: sample mean and standard error, minimum, maximum, effective sample size, and the quantiles in steps of 10%. Plotting time series.
Drawing scatter diagrams.
Kernel estimation of the marginal density of a variable (two versions: standard kernel density estimation, and Bierens' SMINK estimation).
Auto- and cross-correlation functions for time series. In the autocorrelation case also the Box-Pierce Q statistics, the Ljung-Box Q statistics, and the partial autocorrelations are computed.
Periodogram of a time series
Correlation matrix and its eigenvalues.
Unit root tests: Augmented Dickey-Fuller tests, Phillips-Perron tests, Bierens' unit root tests on the basis of higher-order sample autocorrelations, Bierens' unit root tests against nonlinear trend stationarity, and the Bierens-Guo and KPSS tests of the (linear trend) stationarity hypothesis against the unit root (with drift) hypothesis.
Bierens' test for complex-conjugate unit roots (this approach is still experimental).
Single equation models:
Linear regression analysis, which in the time series case can be extended to models with ARMA errors and/or GARCH errors.
Discrete dependent variables modeling, i.e., logit/probit analysis, Poison regression, Binomial logit/probit regression, and multinomial logit analysis.
Tobit analysis.
Nonlinear regression analysis, which in the time series case can be extended to nonlinear models with ARMA errors and/or GARCH errors.
A guided tour explaining how to estimate a CES production function by nonlinear least squares.
Quantile regression.
Two-stage least squares/instrumental variables estimation. Kernel estimation of the error density of linear and nonlinear regression models, quantile regression models, and two-stage least squares models.
Nonparametric kernel regression with one or two explanatory variables.
Tests for ARCH of (nonlinear) regression models and models with ARMA errors.
Out-of-sample forecasting with linear and nonlinear regression models.
ARIMA modeling, estimation, and forecasting. Wald and F-tests of linear parameter restrictions. Bierens' Integrated Conditional Moment (ICM) test of model correctness.
Multiple equations models:
General method of moments (GMM) estimation of a system of regression equations with possibly common coefficients, including seemly unrelated regression (SUR) estimation, and estimation of fixed effect or pooled panel data models.
VAR innovation response analysis: Sims' nonstructural VAR, and Bernanke's structural VAR analysis, including asymptotic innovation response confidence bands.
Johansen's and Bierens' cointegration tests. Bierens' nonparametric cotrending test