1.Transformations of variables: linear and multiplicative transformations, log, exp, differences, percentage changes, lags, moving averages, time since  last turning point, dummy variables for subsets of observations, seasonal dummy variables, indicator functions, trends, constants, and customized transformations.
2.Renaming variables on the input file..
3.Deleting variables on the input file
4.Preparing time series data for out-of-sample forecasting.
5.Reordering cross-section data.

1.Viewing and/or printing the output text file via various wordprocessors.
2.Viewing, editing, and printing graphical output files, via Windows Paint(Brush).
3.Storing in, and retrieving data from, the EasyReg database.

1.Tabulating data.
2.Calculating summary statistics of the data: sample mean and standard error, minimum, maximum, effective sample size, and the quantiles in steps of 10%.
3.Plotting time series.
4.Drawing scatter diagrams.
5.Kernel estimation of the marginal density of a variable (two versions: standard kernel density estimation, and Bierens' SMINK estimation).
6.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.
7.Periodogram of a time series
8.Correlation matrix and its eigenvalues.
9.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.
10.Bierens' test for complex-conjugate unit roots (this approach is still experimental).
11.Single equation models:
12.Linear regression analysis, which in the time series case can be extended to models with ARMA errors and/or GARCH errors.
13.Discrete dependent variables modeling, i.e., logit/probit analysis, Poison regression, Binomial logit/probit regression, and multinomial logit analysis.
14.Tobit analysis.
15.Nonlinear regression analysis, which in the time series case can be extended to nonlinear models with ARMA errors and/or GARCH errors.
16.A guided tour explaining how to estimate a CES production function by nonlinear least squares.
17.Quantile regression.
18.Two-stage least squares/instrumental variables estimation.
19.Kernel estimation of the error density of linear and nonlinear regression models, quantile regression models, and two-stage least squares models.
20.Nonparametric kernel regression with one or two explanatory variables.
21.Tests for ARCH of (nonlinear) regression models and models with ARMA errors.
22.Out-of-sample forecasting with linear and nonlinear regression models.
23.ARIMA modelling, estimation, and forecasting.
24.Wald and F-tests of linear parameter restrictions.
25.Bierens' Integrated Conditional Moment (ICM) test of model correctness.
26.Multiple equations models:
27.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.
28.VAR innovation response analysis: Sims' nonstructural VAR, and Bernanke's structural VAR analysis, including asymptotic innovation response confidence bands.
29.Johansen's and Bierens' cointegration tests.
30.Bierens' nonparametric cotrending test

Viewing the EasyReg data formats.

1.Distribution tools: Computation of either the cumulative distribution function F(x) and corresponding p-value in a given x, or the critical value at a given significance level, for the following standard distributions:
2.Standard normal
3.standard Cauchy
4.Student t
5.Chi-square
6.F
and the following non-standard distributions:
7.Dickey-Fuller "rho" 1, 2, and 3
8.Dickey-Fuller "tau" 1, 2, and 3
The Dickey-Fuller 'rho' distributions 1,2 and 3 are the asymptotic distributions of n (=sample size) times the OLS estimate of the coefficient 'a', and the 'tau' distributions 1, 2 and 3 are the asymptotic distributions of the t-statistics of 'a', in one of the following three AR models, respectively:
y(t) - y(t-1) = a.y(t-1) + u(t),
y(t) - y(t-1) = a.y(t-1) + b + u(t),
y(t) - y(t-1) = a.y(t-1) + b + c.t + u(t),
under the hypothesis that y(t) is a Gaussian random walk: y(t) = y(t-1) + u(t), where u(t) is i.i.d. standard normal. These distributions play a key-role in the Augmented Dickey-Fuller and Phillips-Perron unit root tests. All test statistics which have one of these (asymptotic) null distributions are automatically endowed with (asymptotic) p-values.
9.Teaching tools: Demonstration of the difference between the concepts of convergence in probability and convergence in distribution,
the size and power of the t-test, various Wiener processes, and spurious time series regression results.
10.Matrix tools: For a given matrix A, his module computes A', AA', A'A, A^2, trace(A), A^-1, det(A) (the latter four if A is square), the eigenvalues and eigenvectors of A if A is symmetric, diag(A) if A is a row or column vector, and for conformable matrices A and B: A+B, A-B, and AB.

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