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What's New in EViews 10

Updated Interface

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:

Workfile Snapshots

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.

Spreadsheet Live Statistics

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.

Spreadsheet Live Statistics

Long Object Names

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.

Enhanced Logging Abilities

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.

New Data Handling Features

Improved integration with R

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.

Attribute importing and exporting

EViews 10 supports automatic reading and writing of custom attributes, or series meta-data, from Excel, text and html files.

These automatic tools make importing data into EViews even easier than ever.

World Bank data

EViews 10 offers a new custom interface to World Bank data. World Bank Open Data provides access to a list of datasets that offer access to global development data.

Each dataset contains dozens of topics from countries all around the world. EViews allows you to browse by topic or by country, or perform a seach amongst either.

World Bank Databases

United Nations data

Similar to the World Bank database interface, EViews 10 introduces an interface to the United Nations' wide range of data available through their website.

United Nations data

Eurostat data

EViews 10 also offers a custom interface to the Eurostat database. The interface includes a custom browser for navigation and retrieval of Eurostat's high quality Europe based data.

European Central Bank data

The final new database connection is to the European Central Bank (ECB)'s data.

ECBData data

Tableau® integration

EViews 10 now supports saving series objects in a workfile page to a Tableau Data Extract file (.tde). The TDE file can then be used by Tableau Desktop or exported to a Tableau Server.

Tableau Saving

Saving in JSON format

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.

Bubble Plots

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

Bubble Plots


Observation labels can be added to annotate each bubble.

Bubble Plots

Graph Series Updating

EViews 10 makes working with graphs easier. You can now add and remove series via the mouse and keyboard, allowing you to quickly manipulate your visualisations.

Graph Updating


You can drag a series from your workfile directly onto your graph object, and then delete one simply by clicking on the series to be removed and pressing the delete key.

New Default Graph Styles

EViews 10 introduces a new default graph template. Some of the new design elements of this template include a different aspect ratio, white background, grid lines, and thicker lines in line graphs.

Template


Along with the new default template, a number of other new templates are included for you to choose from.

Templates


Magazine Template

Miscellaneous Improvements

EViews 10 includes new graph options in our graphs. These are:

  • The ability to add text to graph views.
  • New line and bar colors.
  • The ability to add minor ticks to the data axis.
  • Manually set the number of grid lines.
  • Pin graph flyovers as permanent text.
  • Increased options for text positioning.

Table Sorting

In EViews 10 you may easily sort the rows of a table using values in one or more columns. To sort, you must be in table edit mode.

Table Sorting

Econometrics and Statistics

EViews 10 New Econometrics and Statistics: Computation

Season Adjustment Methods

EViews 10 offers two new seasonal adjustment methods, both of which allow you to perform adjustment on non-quarterly or monthly data.



Season-trend Decomposition (STL)

STL decomposition is a seasonal adjustment method that decomposes a series into seasonal, trend and remainder components using a filtering algorithm based upon LOESS regressions.

STL has two main advantages over other seasonal adjustment methods; it works on any frequency of data, and can be calculated on time series data with irregular patterns and missing values.

STL Decomposition

MoveReg Weekly Adjustment

EViews 10 offers a front-end interface to the U.S. Bureau of Labor’s MoveReg weekly seasonal adjustment program

Like its sister quarterly data based X-13 package, MoveReg has the ability to control for both outlier and holiday effects when performing the adjustment.

MoveReg

Improved Special Function Computation

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

Smooth Threshold Regression (STR and STAR)

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.

Robust Standard Error Additions

EViews has included both White and Heteroskedasticity and Autocorrelation Consistent Covariance (HAC) estimators of the least-squares covariance matrix for over twenty years.

EViews 10 expands upon these robust standard error options with the addition of a family of heteroskedastic consistent covariance, and clustered standard errors.

Heteroskedastic Consistent (HC) Covariance Estimators

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.

Cluster-Robust Covariance Estimators

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.

VARs with Linear Restrictions

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 problem.

VAR Linear Restrictions

Structural VAR Restrictions

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.

VAR Historical Decomposition

In EViews 10 you may now, from an estimated standard VAR, easily perform historical decomposition, the innovation-accounting technique proposed by Burbridge and Harrison (1985).

Historical decomposition decomposes forecast errors into components associated with structural innovations (computed by weighting ordinary residuals).

VAR Historical Decomposition

VAR Historical Decomposition

Improved nonlinear forecasting

Dynamic forecasting using simulation methods is now supported from the equation forecast dialog.

Additional Autoregressive Distributed Lag (ARDL) Tools

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.

ARDL Improvements

EViews 10 New Econometrics and Statistics: Testing and Diagnostics

VAR Structural Residuals

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.

VAR Structural Residuals

VAR Structural Residuals

Improved VAR Serial Correlation Testing

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.

Model Boundary Checking

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)

Model Bounds

 
   
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