Eviews 6
January 27, 2010
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Eviews 6
A combination of power and ease-of-use make EViews 6 the ideal package for anyone working with time series, cross-section, or longitudinal data. With EViews, you can quickly and efficiently manage your data, perform econometric and statistical analysis, generate forecasts or model simulations, and produce high quality graphs and tables for publication or inclusion in other applications.
Featuring an innovative graphical object-oriented user-interface and a sophisticated analysis engine, EViews blends the best of modern software technology with the features youve always wanted. The result is a state-of-the art program that offers unprecedented power within a flexible, easy-to-use interface.
Find out for yourself why EViews is the worldwide leader in Windows-based econometric software and the choice of those who demand the very best.
What Is New?
Improved Performance & Capacity
Nonlinear estimation, model solution, and other operations involving evaluation of series expressions are significantly faster since EViews now compiles expressions to native machine code.
Using Windows XP with the /3GB switch, Vista, or 64-bit XP or Vista, data capacity can be up to two and one-half times as large as under EViews 5.1.
Statistics and Econometrics Features
Statistics
EViews 6 features a new factor analysis object that allows you to: (1) compute covariances, correlations, or other measure of association (if necessary), (2) specify the number of factors, (3) obtain initial uniqueness estimates, (4) extract (estimate) factor loadings and uniquenesses, (5) examine diagnostics, (5) perform factor rotation, (6) estimate factor scores.
You may select from a menu of automatic methods for choosing the number of factors to be retained, or you may specify an arbitrary number of factors. You may estimate your model using principal factors, iterated principal factors, maximum likelihood, unweighted least squares, generalized least squares, and noniterative partitioned covariance estimation (PACE). Once you obtain initial estimates, rotations may be performed using any of more than 30 orthogonal and oblique methods, and factor scores may be estimated in more than a dozen ways.
Principal components analysis in EViews 6 has been greatly enhanced. You may now display line graphs of the ordered eigenvalues (screen plots), and examine scatterplots of the loadings and component scores (biplots). Loadings and component scores may now be computed with various weightings so that you may, for example, construct orthonormal or eigenvalue matching scores.
In addition to the previously supported ordinary (Pearson) correlations and covariances, you may now compute alternative measures of association: Spearman rank-order, Kendall’s tau-a and tau-b, as well as partial correlations and covariances. EViews 6 now performs pairwise tests of zero correlation, with or without multiple comparison adjustments.
Mean equality tests (ANOVA) now perform tests both under the standard maintained assumption of equal variances across subgroups, and now, under the assumption that the variances are heteroskedastic (Welch 1951, Satterthwaite 1946).
Econometrics
General
Linear quantile regression and least absolute deviations (LAD) specifications (Koenker, 2005) may now be estimated. Asymptotic covariance matrices for the quantile regression estimates may be calculated assuming i.i.d. errors, Huber’s Sandwich, or bootstrap methods. Specialized tools permit you to test for slope equality across quantile estimates (Koenker and Bassett, 1982), or to test for symmetry across quantile estimates (Newey and Powell, 1987).
EViews 6 provides stepwise regression tools for variable selection in ordinary least squares models. Among the methods and criteria that EViews supports are: undirectional-forwards, uni-directional-backwards, stepwise-forwards, stepwise backwards, swapwise-max R-squared increment, and combinatorial.
EViews 6 offers expanded heteroskedasticity testing (including Breusch-Pagan (1979), Godfrey (1978), Harvey (1978), Glejser (1969)), as well as the ability to specify custom tests in which you can test against departures from the homoskedastic null in a number of directions (say, by combining a White and Harvey test).
EViews 6 now offers the Quandt-Andrews Breakpoint Test (Andrews, 1993 and Andrews and Ploberger, 1994) which tests for one or more unknown structural breakpoints in an equation’s sample.
The Binary, Count, Censored, and Ordered equation estimation methods now permit you to specify your equation by expression (instead of restricting you to providing a list). This flexibility allows you to construct non-linear index specifications, or models with coefficient restrictions.
Time-series
You may now perform cointegration tests with panel and pooled time series cross-section data using the panel cointegration statistics of Pedroni (2004), Pedroni (1999), and Kao (1999), or the Fisher-type test suggested by Maddala and Wu (1999).
EViews now estimates multivariate GARCH models, providing support for the most popular multivariate specifications: Conditional Constant Correlation, the Diagonal VECH and (indirectly) the Diagonal BEKK. You may estimate the model assuming multivariate normal or multivariate t-distribution errors. Once estimated, you may examine the fitted conditional covariances, variances, and correlations and save results to your workfile. In addition, you may perform residuals tests on the raw or standardized residuals, where the latter may be computed using various standardization methods.
EViews 6 allows you to estimate univariate integrated GARCH models that constrain the persistent parameters of univariate GARCH model to sum to unity. The constant term in a GARCH model can be restricted, or the variance targeted, so that the long run variance of the model equals to the sample variance of the data. Users may now choose the weight when backcasting is used to calculate the pre-sample variance.
Models
EViews 6 model solution may be up to 30 times faster than under EViews 5.1. Among the improvements:
A new solution algorithm has been added to models. Broyden’s method is a quasi-Newton method that uses a secant approximation to the Jacobian instead of the true Jacobian when solving for the Newton step. The method has many of the desirable properties of Newton’s method without requiring the Jacobian to be evaluated and factored at each step.
Broyden’s algorithm is available for model solve.
The model solver can now reorder equations within simultaneous blocks so that a set of variables in the block can be solved for recursively, conditional on the values of the remaining variables in the block. This structure is used by the Newton and Broyden solution algorithms to substantially reduce the time required to solve models consisting of large sparse systems of equations.
Stochastic simulations can now be based on bootstrapped residuals as an alternative to normally distributed random numbers. Bootstrapped residuals may be drawn independently for each equation, or may be drawn from the same period across all equations.
The complete set of results from each repetition of a stochastic simulation can now be saved as a new page in the workfile.
Equations for endogenous variables can now be excluded from the model (treated as exogenous variables) automatically based on whether actual values are available for the variable in each period. This makes it easy to perform forecasts using all available data when new data arrive at different dates
http://rapidshare.com/files/143234771/b4all.rar
Pass: b4all
Download the instructions file above, with links to the set up programme. All instructions are in the rar file, unrar and follow the instructions
Hope this will help
I am not the original uploader, so all credit to him
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