EViews 13 offers a exciting new additions and improvements to its set of econometric and statistical features. The following is a brief outline of the most important new features, followed by additional discussion and pointers to full documentation.

EViews 13 offers improvements to existing tools for analyzing data using Autoregressive Distributed Lag Models (ARDL), featuring estimation of Nonlinear ARDL (NARDL) models which allow for more complex dynamics, with explanatory variables having differing effects for positive and negative deviations from base values.

The classical ARDL framework assumes that the long-run cointegrating relationship is a symmetric linear combination of regressors. While this is a natural starting assumption, it does not match the behavioral finance and economics literature approach to modeling nonlinearity and asymmetry (Kahneman, Tversky, and Shiller, 1979). In response, Shin (2014) proposes a nonlinear ARDL (NARDL) framework in which short-run and long-run nonlinearities are modeled as positive and negative partial sum decompositions of the explanatory variables.

From the main EViews menu, click on Quick/Estimate Equation… or type the command equation in the command line to open the equation dialog. Then select the ARDL - Autoregressive Distributed Lag Models (including NARDL) from the Method dropdown to display the ARDL dialog:

In the Linear dynamic specification, you should a enter a list of variables consisting of the dependent variable followed by any symmetric ARDL distributed lag regressors. At a minimum, the edit field must contain the dependent variable.

Exogenous regressors, including deterministics, may be specified in the Fixed regressors specifications section. Trend regressors corresponding to the five deterministic cases discussed (None, Restr. constant, Constant, Restr. trend, Trend) may be specified using the Trend specification dropdown. All other exogenous regressors (those apart from the constant and the trend) should be specified in the Fixed regressors edit field.

Asymmetric distributed lag regressors may be listed under Asymmetric dynamic specifications. In particular, the Long-run and short-run edit field may be used to specify regressors which are asymmetric in both the long-run and short-run. Regressors which are asymmetric only in the long-run may be specified in the Long-run only edit field, while those which are asymmetric exclusively in the short-run are specified in the Short-run only edit field.

In addition to the support for estimating nonlinear ARDL specifications, EViews 13 offers a number of new ARDL diagnostics. These diagnostics are designed to help you examine the dynamic behavior of your variables, the long-run properties of your specification, and the appropriateness of your model.

In large sample panel datasets, a popular approach for analyzing dynamic data is the Pooled Mean Group (PMG) estimator of Pesaran, Shin and Smith (1999) (PSS). This model takes the cointegration form of the simple ARDL model and adapts it for a panel setting by allowing the intercepts, short-run coefficients, and cointegrating terms, to differ across cross-sections.

EViews 13 extends the estimation of PMG models to support:

To estimate a Panel ARDL/PMG model in EViews, open the equation dialog by selecting Quick/Estimate Equation…, or by selecting Object/New Object…/Equation and selecting PMG/ARDL from the Method dropdown menu. EViews 13 will then display the new ARDL estimation dialog:

While much of the dialog is familiar from earlier versions of EViews, notably different in the EViews 13 PMG dialog are additional entries in the Trend specification corresponding to restricted intercept and trend specifications, and new edit fields for specifying Asymmetric dynamic specifications. The Trend specification upgrades allow for a wider range of long and short-term dynamics, while the latter fields support asymmetric variables in the PMG estimator:

EViews 13 also offers a number of new PMG diagnostics and tests to help you examine the dynamic behavior of your variables, the long-run properties of your specification, and the appropriateness of your model.

Difference-in-difference (DiD) estimation is a popular method of causal inference that allows estimation of the average impact of a treatment on individuals.

EViews 13 offers tools for estimation of the DiD model using the common two-way fixed-effects (TWFE) method, as well as post-estimation diagnostics of the TWFE model, such as those by Goodman-Bacon (2021), Callaway and Sant’Anna (2021), and Borusyak, Jaravel, and Spiess (2021).

To estimate a DiD model in EViews, bring up the equation dialog by clicking on Object/New Object…/Equation or Quick/Estimate Equation… from the main menu bar in your panel workfile. EViews will detect the presence of your panel structure and in place of the standard equation dialog will open the panel equation estimation dialog. Select DiD – Difference-in-Difference in the Method dropdown display the DiD dialog:

In the Equation specification edit field you should enter the dependent variable followed by any exogenous regressors apart from the treatment variable.

The treatment variable should be entered in the Treatment Variable edit field. The treatment series should be a binary variable indicating whether the individual has been treated (i.e., is 1 if the observation in a treatment group which is post-treatment date for that group, and 0 otherwise).

The Options tab contains a single Coefficient name edit field that allows you to change the default coefficient vector.

Click on OK to perform the difference-in-difference estimation and display the output:

Estimation of Vector Error Correction (VEC) models using reduced rank regression (RRR) has been a staple of EViews for several versions. Previous versions of EViews supported a variety of built-in specifications for deterministic trends, and allowed users to add unrestricted exogenous regressors to account for short-term dynamics.

One important limitation of the existing tools for VEC estimation was the inability of users to add arbitrary exogenous variables to the cointegrating relation since the set of admissible restricted regressors was limited to the endogenous variables and potentially an intercept, and possibly a linear trend term.

EViews 13 removes this limitation on restricted regressors and improves on the prior treatment of exogenous variables in two distinct ways:

To estimate a VEC using the new features, from the main application menu of an existing var object, click on the Estimate button to open the VAR Specification estimation dialog. Alternately, you may create a new VAR object by selecting Object/New Object... group, then selecting VAR. Once the dialog appears, select Vector Error Correction in the Method dropdown menu to display the VEC estimation dialog:

The Exogenous variables section of the main estimation dialog has fields for variables that are Short-run (outside cointegrating equation) only, variables that are Long-run (inside the cointegrating equation) only, and variables that are Both long-run and short-run.

Further, the Deterministic trend specifications dropdown on the Cointegration tab offers additional predefined deterministic trend specifications.

Selecting a specification in which both intercept and trend are short-run only and clicking on OK produces estimates using the new deterministics:

The time-varying coefficients vector autoregression, or TVCVAR, is a nonlinear VAR model with coefficients that evolve smoothly over time. EViews 13 supports Bayesian TVCVAR, or BTVCVAR. The BTVCVAR is popular even among those who do not identify as Bayesian because the prior provides a convenient way to induce shrinkage in a model that needs it.

To estimate BTVCVAR in EViews, click on Quick/Estimate VAR... or run var in the command window. This will open the VAR Specification dialog. Select Bayesian TVCVAR from the VAR type dropdown menu. The dialog should now have the Basics, Prior, and Options tabs.

Endogenous variables, lags, exogenous variables, and the estimation sample are set in the Basics tab. Lags are required to be contiguous.

EViews gives users the option of setting aside a subset of the estimation sample for the purpose of specifying a prior distribution. The observations that go towards specifying the prior comprise the prior sample. The remaining observations make up the data sample, which is used to update the prior.

Prior hyper-parameters are set inside the Prior tab. To help with setting the prior, EViews maps the BTVCVAR prior hyper-parameters to a set of six scalar quantities. Users set these scalars to specify a prior sample, control the variability of the time-varying coefficients, etc.

There are four categories in the Options tab: Sampler, Display, Smoother, and Stability.

The Sampler options determine how the posterior sample is generated.

Display options determine what to report as estimation results. Users can pick either posterior median or posterior mean as their point estimate. The point estimate type selected here will be applied to the coefficients, the observation covariance matrix, and the errors. Users can also display equal-tailed credibility intervals (bands) at one or more credibility levels for the coefficients. To do so, check the box next to Show credibility intervals. Bands use shading by default. To use lines instead, check the box next to Use lines.

A simulation smoother can be selected from the dropdown menu under Smoother. EViews currently supports three simulation smoothers: the Cholesky factor algorithm (CFA), the Kalman filter and smoother (KFS), and the method of McCausland, Miller, & Pelletier (MMP). For more information, see McCausland, Miller, & Pelletier (2011) and the references therein.

To enforce stable VAR coefficients at each date within the data sample, select Cogley & Sargent from the dropdown menu under Stability. The maximum number of attempts threshold ensures that the sampler does not get stuck in an infinite loop in an attempt to obtain stable draws.

Once the BTVCVAR model has been specified, click on OK to run the posterior simulator. Progress is displayed in the bottom left corner of the EViews window. Once posterior simulation is complete, estimates and other statistics based on the posterior sample are computed.

Estimation results are presented in a spool-like object. The nodes under Output Sections in the left pane are used for navigation. For example, clicking on the Summary node will bring the summary table into focus. The checkboxes that appear below Display Coefficients are used to show/hide coefficient series that are associated to specific endogenous variables, lags, and exogenous variables. For example, unchecking the box next to C hides the coefficient series associated to the constant term in all graphs.

The introduction of new deterministic trend settings and exogenous variable support in EViews 13 VEC estimation offers corresponding improvements in cointegration testing in both group and var object settings.

To perform a Johansen cointegration test to determine the rank that should be used in estimation of the VEC select View/Cointegration Test/Johansen System Cointegration Test..., from a group window, or Views/Cointegration Test... from an estimated VAR object window using. In the latter case, the test dialog will be pre-filled with the cointegration specification, if applicable:

All new settings allow you to specify new deterministic trend specifications with restricted constants and trends, and now allow you to specify exogenous variables that appear in the long-run cointegrating relation.

In addition to the support for nonlinear ARDL specifications, EViews 13 offers a number of new ARDL diagnostics. These diagnostics are designed to help you examine the dynamic behavior of your variables, the long-run properties of your specification, and the appropriateness of your model:

EViews 13 offers enhanced presentation of ARDL results, with an emphasis on highlighting the error correction results and the cointegrating relationship.

The new error correction spool output (View/ARDL Diagnostics/Error-Correction Results) shows two representations of the coefficients in the cointegrating relationship, allowing you to easily move between viewing the long-run relationship results in the Conditional Error Correction (CEC) and the Error Correction (EC) forms.

The CEC focuses on the natural division between long-run and short-run dynamics, with the former generated by regressors entering in levels/lags, and the latter by regressors in differences

while the EC representation uses the cointegrating relation to highlight the speed of adjustment to long-run equilibrium,

The new cointegrating relation spool view (View/ARDL Diagnostics/Cointegrating Relation) lets you switch between viewing the specification, coefficient results, and a graphical display of the cointegrating relation.

To display cumulative dynamic multiplier graphs for each of the explanatory variables in an EViews equation estimated using ARDL, click on View/ARDL Diagnostics/Dynamic Multiplier Graph...

EViews will open a dialog containing display and computation settings:

Click on OK to continue. EViews will open a spool view, with each node in the spool containing the CDM graph corresponding to one of the explanatory variables.

For symmetric linear models, each graph contains a CDM along with a dashed horizontal line denoting the long-run value.

For asymmetric nonlinear models, each graph will show the positive and negative responses and limit values, along with a line showing the absolute value of the difference between the two, and if requested, a CI for the absolute difference:

The NARDL specification is quite general and can accommodate asymmetries in different combinations of short and long-run dynamics. From an estimated NARDL, you may test long-run symmetry restrictions and/or short-run symmetry restrictions.

To perform the symmetry tests on the estimated NARDL, click on View/ARDL Diagnostics/Symmetry Test from an estimated equation:

EViews 13 features improved PMG diagnostics, including display of individual cross-section error correction equations coefficients and cointegrating series, cross-section specific bounds, Hausman tests for PMG coefficient similarity, and tests of symmetry restrictions:

For general discussion, see “Linear and Nonlinear ARDL” in User’s Guide II which discusses a variety of these features in non-panel settings

The Error Correction Results view displays coefficient results for the error correction regressions for each cross-section. Select View/ARDL Diagnostics/Cross-sectional Error-correction Results to display a spool of tables with results for each cross-section:

To perform the symmetry test, select View/ARDL Diagnostics/Symmetry Test from the menu of a nonlinear asymmetric NARDL/PMG equation:

We may easily perform a Hausman test on the similarity of the PMG estimator to the mean-group (MG) and dynamic fixed effects (DFE) estimators. To conduct the test, click on ARDL Diagnostics/PMG Similarity Test:

Fortunately, EViews 13 removes all three of these limitations.

EViews 13 offers a small but surprisingly useful improvement in forecast sample setting.

Previously, forecasting via command generally required the user to first set the global smpl using a smpl statement prior to issuing the forecasting command.

For example, the following lines estimated the equation using one sample, and then forecast over two different samples,

In this case, setting the global sample prior to equation forecasting was only mildly inconvenient. However, in some settings, particularly those involving non-equation forecasting where you are generating multiple forecasts and data, setting and resetting the global sample was inconvenient at best, and prone to error.

EViews 13 solves this problem by updating most forecast-generating commands to take a forecast sample information.

In this context, there are two types of forecasting procedures.

In the first set of procedures, the forecast sample is set independently of the remainder of the procedure. For these cases, EViews 13 offers an option to allow you to set the sample range pair directly using the option:

This option is available interactively and in commands for:

The second type of forecasting procedure sets the forecast sample one-period beyond a previously specified estimation sample, and then forecasting continues from that period onward until a forecast endpoint.

In this case, EViews 13 allows you to set the forecast sample in two distinct ways: by specifying the number of periods to forecast, or by specifying the forecast end date:

These options are available interactively and in commands for:

Our earlier example changes from the commands

to