EViews has always been known for its unmatched ease-of-use, but there's always room for improvement. We've raised the ante in EViews 10 with a number of interface improvements. Here are but a few of the highlights:
EViews 10 supports a new workfile backup feature, called “snapshots.” Workfiles can now
be easily backed up and managed using this new system. Snapshots can be manually taken
whenever you want to save the current state of the workfile. EViews also supports automatic
snapshots that are taken periodically to allow you to easily roll back a workfile to a previous
state and/or investigate changes made to your workfile between points.
Not only do snapshots provide a history of your data, but you can also store a record of every action you have performed between snapshots, giving a full record of your workflow.
The spreadsheet view of series and groups has been updated to include a selection of descriptive statistics along the status bar. The display can be customized to control which statistics to calculate, and the values are updated in real time as you select different ranges of cells within the spreadsheet.
EViews 10 now supports a maximum object name length of 300 characters. Before EViews 10, the maximum object name length was 24 characters.
Although this may seem like a minor change - users fetching data from third-party data vendors will no longer have to face the constraints of converting existing series names into a 24 character limit.
New to EViews 10 all program log windows appear as tabbed windows, and you may rearrange
their positions. Additionally, you have the ability to specify a name for the log window
and direct the messages to the log window with the specified name.
EViews' R and Matlab® integration allows you to run R or Matlab code from within EViews itself, granting access to the powerful programming languaes of these packages to create or run processes not currently implemented in EViews.
EViews 10 offers a number of improvements to the integration engine:
R Integration no longer requires third-party products to be installed alongside EViews and R.
New XON and XOFF commands allow faster issuing of commands to the external applications from within EViews programs.
Saving and opening R .RDATA files.
Improved connection log window, giving direct console access to R or Matlab.
EViews also supports saving workfiles in the ubiquitous JSON format, allowing you to access your data from many applications, including web-apps.
New Graph, Table and Spool Features
Each version of EViews has always introduced improvements to our powerful graphing and presentation quality output engine, and EViews 10 is no different. Here are some of the improvements we've made to graphs in this version.
EViews 10 introduces bubble plots as a new graph type. Bubble plots are extensions of scatter plots, where a third dimension may be used to specify the size of the data points. Unlike traditional
scatter plots, where bubble sizes are fixed, bubble plots allow for variable size bubbles
Observation labels can be added to annotate each bubble.
Many common mathematical expressions lose accuracy and/or precision when
calculated naively. EViews 10 provides functions that preserve the accuracy/precision of results
for specific expressions. While these functions are intended to be used near particular critical
values, they can be invoked safely anywhere in the expressions’ domains.
EViews 10 New Econometrics and Statistics: Estimation
EViews 9 introduced Threshold Regression (TR) and Threshold Autoregression (TAR) models, and EViews 10 expands up these model by adding Smooth Threshold Regression and Smooth Threshold Autoregression as options.
In STR models the regime switching that occurs when an observed variable crosses unknown thresholds happens smoothly. As a result, STR models are often considered to have more “realistic” dynamics that their discrete TR model counterparts.
EViews' implementation of STR includes features such as:
Estimation of parameters for both shape and location of the smooth threshold.
Model selection for the threshold variable.
Specification of both regieme varying and regieme non-varying regressors.
EViews 10 increases the options for heteroskedastic consistent covariance estimators beyond the familiar White estimator available in previous versions. The class of estimators supported belong to the HC family described by Long and Ervin, 2000, and Cribari-Neto and da Silva, 2011.
The estimators differ in their choice of observation-specific weights used to improve the finite sample properties of the residual error covariance.
In many settings, observations may be grouped into different groups or “clusters” where
errors are correlated for observations in the same cluster and uncorrelated for observations
in different clusters. EViews 10 offers support for consistent estimation of coefficient covariances
that are robust to either one and two-way clustering.
As with the HC estimators, EViews supports a class of cluster-robust covariance estimators, with each estimator differing on the weights it gives to observations in the cluster.
The basic $k$-variable VAR(p) specification has $k(pk+d)$ coefficients so that even moderate
sized VARs require estimation of a large number of parameters. When VARs are applied to
macroeconomic data with limited sample sizes, model over-parameterization is a frequent
problem as there are too few observations to estimate precisely the VAR parameters.
EViews now offers support for the linear restriction approach to handling this over-parameterization
One of the key elements behind Structural VAR estimation is the necessary imposition of restrictions on the residual structure matrices.
These restrictions generally take the form of restrictions on the factorization matrices, A and B, restrictions on the short-run impulse response matrix S, or restrictions on the long-run impulse response matrix F (or C), or a combination of the above.
Previous versions of EViews only allowed restrictions on A and B, or on F. EViews 10 broadens the restriction engine by allowing restrictions on any of the four matrices, adding linear restrictions, and adds a new interface allowing easier specification of the restrictions.
Autoregressive Distributed Lag (ARDL) estimation has been drastically improved for EViews 10. In particular, EViews now allows absolute control over lag specification.
Any of the variables (dependent or regressor) can be specified with a custom lag, and you can mix the specification allowing certain variable to have fixed custom lags and the remainder having their lags chosen via model selection methods.
Moreover, in the context of the ARDL approach to the Bounds Cointegration Test of Pesaran Shin and Smith (2001) (PSS), EViews now offers inference under all 5 deterministic cases considered in PSS. Also, alongside the asymptotic critical values provided in PSS, EViews now offers finite sample critical values from Narayan (2005)
Finally, in addition to the Bounds F-test, Eviews now also reports the appropriate Banerjee, Dolado, Mestre (1998) (BDM) t-bounds test.
EViews 10 New Econometrics and Statistics: Testing and Diagnostics
Structural residuals play an important role in VAR analysis, and their computation is
required for a wide range of VAR analysis, including impulse response, forecast variance
decomposition, and historical decomposition.
While EViews has long computed these transformed residuals for internal use, EViews 10
now makes structural residuals available to users.
Prior versions of EViews computed the multivariate LM test statistic for residual correlation
at a specified order using the LR form of the Breusch-Godfrey test with an Edgeworth expansion
correction (Johansen 1995, Edgerton and Shukur 1999).
EViews 10 offers two substantive improvements for testing VAR serial correlation.
First, in addition to testing for autocorrelation at specified orders,
EViews now also tests jointly for autocorrelation for lags 1 to s.
Second, EViews augments the Edgeworth LR form of the test with the Rao F-test version
of the LM statistic as described Edgerton and Shukur (1999) whose simulations
suggest it performs best among the many variants they consider.
EViews 10 lets you specify boundaries for endogenous variables in a model through a new
Boundaries dialog page. Although the solver will not enforce the boundaries while solving
the model, EViews will warn you if any variable crosses its boundaries (i.e., solves to a
value higher than the upper boundary or less than the lower boundary)
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