我有EViesw 6.0 正式版光盘镜像,免费和大家共享。传起来真够慢的。希望高手编写一个注册机,造福大家。希望大家回复支持。
请lz标明是原创的 还是转帖。另外希望能提供更多的详细信息。
先行奖励一枚金币,以鼓其气。
版主jimmy_young2005
[此贴子已经被jimmy_young2005于2008-8-14 9:44:00编辑过]
你太伟大了!期盼注册机,或破解!!!
EViews 6版本功能更新
EViews 6 features a wide range of exciting changes and improvements. The
following is a brief summary of some of the most important new features in
Version 6.
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 3G 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 (scree 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 suppported 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 integrated univariate GARCH models,
constraining the persistent parameters of GARCH model to sum up 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.