The following list summarizes the EViews basic commands.

Other chapters document different aspects of the command language:

Graph creation is discussed in detail in “Graph Creation Command Summary”.

Note that with the exception of tabplace, these commands are supported primarily for backward compatibility. There is a more extensive set of table procs for working with and customizing tables. See “Table Procs”.

The following commands have object command forms (e.g. , Equation::arch). These commands are particularly suited for interactive command line use. In general, we recommend that you use the object forms of the commands.

registers the specified program file as an EViews Add-in. Note that the program file should have a “.PRG” extension, which you need not specify in the prog_name.

If you do not provide the optional path specification, EViews looks for the program file in the default EViews Add-ins directory.

Explicit path specifications containing “.\” and “..\” (to indicate the current level and one directory level up) are evaluated relative the directory of the installer program in which the addin command is specified, or the EViews default directory if addin is run from the command line.

You may use the special “<addins>”directory keyword in your path specification.

registers the file “Myaddin.prg” as a global Add-in, with the user-defined global command myaddin, no menu support, and no assigned documentation file. The description “This is my add-in” will appear in the main Add-ins management dialog. The version number is “1.0”. Note that the “.\” indicates the directory from which the program containing the addin command was run, or the EViews default directory if addin is run interactively.

registers the file “Recshade.prg” as a graph specific Add-in. The Add-in supports the object-command recshade, has an object-specific menu item “Add US Recession Shading”, and has a documentation file “Recession shade.txt”.

registers the Add-in file “Roll.prg” as an equation specific Add-in. Note that the documentation and program files are located in the “Roll” subdirectory of the default Add-ins directory. The XML file located at www.mysite.com/myaddins.xml is used when checking for available updates for the Add-in, and the current version number is set to “1.2”.

See “Add-ins” for a detailed discussion of Add-ins.

registers the specified definition file as an EViews user object. Note that the definition file should have a “.INI” extension.

If you do not provide the optional path specification, EViews looks for the program file in the default EViews user objects directory.

Explicit path specifications containing “.\” and “..\” (to indicate the current level and one directory level up) are evaluated relative the directory of the installer program in which the adduo command is specified, or the EViews default directory if adduo is run from the command line.

registers the roll class of user object, specifying a description of “Rolling Regression Object”, and using the definition file rolldef.ini, located in the same folder as the installer program.

registers the resstore class of user object, specifying the version number as “1.0”, and using the XML file located at “www.mysite.com/myuos.xml” to check for updates.

See “User Objects” for a discussion of user objects.

The first two options specify the order of the GARCH model:

After the “ARCH” keyword and options, specify the dependent variable followed by a list of regressors in the mean equation.

The default is to estimate a standard GARCH model. You may specify one of the followings keywords to estimate a different model:

estimates an ARCH(4) model with a mean equation consisting of the series SP500 regressed on a constant. The procedure will perform up to 1000 iterations, and will report Bollerslev-Wooldridge robust QML standard errors upon completion.

The commands:

estimate a TARCH(1, 1)-in-mean specification with the mean equation relating the percent change of NYS to a constant, an AR term of order 1, and a conditional variance (GARCH) term. The first line sets the default coefficient vector to 0.1, and the “s” option uses these values as coefficient starting values.

The command:

estimates a symmetric Power ARCH(2, 1) (autoregressive GARCH of order 2, and moving average ARCH of order 1) model with GED errors. The power of model is fixed at 1.5 and the GED parameter is fixed at 1.2. The mean equation consists of the first log difference of IBM regressed on a constant and the first log difference of SP500. The conditional variance equation includes an exogenous regressor R.

See “ARCH and GARCH Estimation” for a discussion of ARCH models.

See Equation::arch for the equivalent object command.

Carries out Lagrange Multiplier (LM) tests for ARCH in the residuals.

Note that a more general version of the ARCH test is available using Equation::archtest.

You must specify the order of ARCH for which you wish to test. The number of lags to be included in the test equation should be provided in parentheses after the arch keyword.

Regresses OUTPUT on a constant, LABOR, and CAPITAL, and tests for ARCH up to order 4.

Estimates a GARCH(1,1) model with mean equation of SP500 on a constant and tests for additional ARCH up to order 4. Note that when performing an archtest after an arch estimation, EViews uses the standardized residuals (the residual of the mean equation divided by the estimated conditional standard deviation) to form the test.

See “ARCH LM Test” for further discussion of testing ARCH and “ARCH and GARCH Estimation” for a discussion of working with ARCH models in EViews.

See Equation::archtest for the equivalent object command. See Equation::hettest for a more general version of the ARCH test.

The linear_regs specification is required:

The remaining specifications are optional

You may specify the lag for an individual distributed-lag variable using the “@fl(variable, lag)” syntax. For instance, if the variable X should use 3 lags, irrespective of the fixed or automatic lag settings, you may specify this by entering “@fl(x, 3)” in the regressor list.

opens example data from Greene (2008, page 685), containing quarterly US macroeconomic variables between 1950 and 2000.

The following command

creates an equation object and estimates an ARDL model with the log of real consumption as the dependent variable, and the log of real GDP as a dynamic regressor. Quarterly dummy variables are included as static regressors. Automatic model selection is used to determine the number of lags of LOG(REALCONS) and LOG(REALGDP).

The command

estimates a second model, replicating Example 20.4 from Greene, with a fixed three lags of the dependent variable and three lags of the regressor.

The line above estimates an ARDL(1,1,1) model with the log of real consumption as the dependent variable, the log of real GDP as a linear regressor, and log of real government expenditures as a dual asymmetric regressor.

extends the previous model and estimates an ARDL(1,1,1,1) model by including the log of real investments as a long-run asymmetric regressor.

The line above extends the previous model and estimates an ARDL(1,1,1,1,1) model by including the log of treasury bill rates as a short-run asymmetric regressor.

This example estimates a panel ARDL model using the workfile “OECD.wf1”. This model replicates that given in the original Pesaran, Shin and Smith 1999 paper. Model selection is not used to choose the optimal lag lengths, rather a fixed single lag of both the dependent variable and the regressor is employed.

This command estimates a QARDL(1,1) model where lag selection is fixed for both the dependent and independent regressors, and the quantile value is 0.4.

See “ARDL and Quantile ARDL” for further discussion.

Carries out Breusch-Godfrey Lagrange Multiplier (LM) tests for serial correlation in the estimation residuals from the default equation.

You must specify the order of serial correlation for which you wish to test. You should specify the number of lags in parentheses after the auto keyword, followed by any additional options.

To regress OUTPUT on a constant, LABOR, and CAPITAL, and test for serial correlation of up to order four you may use the commands:

The commands:

perform a regression of CONS on a constant, Y and lagged Y, and test for serial correlation of up to order twelve. The first line redirects printed tables/text to the “Artest.TXT” file.

See “Serial Correlation LM Test” for further discussion of the Breusch-Godfrey test.

See Equation::auto for the corresponding equation view.

Estimates models where the binary dependent variable Y is either zero or one (probit, logit, gompit).

To estimate a logit model of Y using a constant, WAGE, EDU, and KIDS, and computing Huber-White standard errors, you may use the command:

Note that this estimation uses the default global optimization options. The commands:

estimate a probit model of Y on a constant, X2, and X3, using the specified starting values. The commands:

store the estimated coefficients and coefficient covariances in the coefficient vector BETA_PROBIT and matrix COV_PROBIT.

See “Binary Dependent Variable Models” for additional discussion.

See Equation::binary for the corresponding equation method.

List the dependent variable first, followed by a list of the independent variables that have coefficients which are allowed to vary across breaks, followed optionally by the keyword @nv and a list of non-varying coefficient variables.

lowers the sequential test size from 0.10 to 0.05, and raises the trimming to 10 percent.

estimates the model with two user-specified break dates. In addition, the variable UNEMP is restricted to have common coefficients across the regimes.

See “Least Squares with Breakpoints” for discussion. See also “Multiple Breakpoint Tests”.

See Equation::multibreak for estimation of regression equations with breaks.

Performs pairwise Granger causality tests between (all possible) pairs of the listed series or group of series.

Following the keyword, list the series or group of series for which you wish to test for Granger causality.

You must specify the number of lags n to use for the test by providing an integer in parentheses after the keyword. Note that the regressors of the test equation are a constant and the specified lags of the pair of series under test.

To compute Granger causality tests of whether GDP Granger causes M1 and whether M1 Granger causes GDP, you may enter the command:

The regressors of each test are a constant and four lags of GDP and M1.

prints the result of six pairwise Granger causality tests for the three series. The regressors of each test are a constant and twelve lags of the two series under test (and do not include lagged values of the third series).

See “Granger Causality” for a discussion of Granger’s approach to testing hypotheses about causality.

See also Group::cause for the corresponding group view.

Type the name of the series or wildcard expression for series you want to copy after the ccopy keyword. The data bank files will be stored in the default directory with the same name as the series names in the DRI database. You can supply path information to indicate the directory for the data bank files.

The command:

copies the DRI series LHUR to “Lhur.DB” file in the default path directory.

copies the GDP series to the “Gdp.DB” file in the “b:” drive and the GNET series to the “Gnet.DB” file in “c:\nipadata”.

See also cfetch, clabel, store, fetch.

The cd command changes the current default working directory. The current working directory is displayed in the “Path=...” message in the bottom right of the EViews window.

Note that the default directory is not used for processing of include files (see include).

To change the default directory to “sample data” in the “a:” drive, you may issue the command:

Notice that the directory name is surrounded by double quotes. If your name does not contain spaces, you may omit the quotes. The command:

changes the default directory to “a:\test”.

changes the default directory to the cloud location, MYONEDRIVE.

Subsequent save operations will save into the default directory, unless you specify a different directory at the time of the operation.

See “Workfile Basics” for further discussion of basic operations in EViews.

See also “include”, wfsave, pagesave, and save.

Estimates models where the dependent variable is either censored or truncated. The allowable specifications include the standard Tobit model.

The command:

estimates a censored regression model of HOURS on a constant, WAGE, EDU, and KIDS with QML standard errors. This command uses the default normal likelihood, with left-censoring at HOURS=0, no right censoring, and the quadratic hill climbing algorithm.

See “Discrete and Limited Dependent Variable Models” for discussion of censored and truncated regression models.

See Equation::censored for the corresponding equation method.

cfetch reads one or more series from the DRI Basic Economics Database into the active workfile. You must have the DRI database installed on your computer to use this feature.

reads the DRI series LHUR, GDP, and GNET into the current active workfile, performing frequency conversions if necessary.

EViews’ automatic frequency conversion is described in “Frequency Conversion”.

See also ccopy, clabel, store, fetch.

See cd.

Carries out Chow breakpoint or Chow forecast tests for parameter constancy.

You must provide the breakpoint observations (using dates or observation numbers) to be tested. To specify more than one breakpoint, separate the breakpoints by a space. For the Chow breakpoint test, if the equation is specified by list and contains no linear terms, you may specify a subset of the regressors to be tested for a breakpoint after an “@” sign.

The commands:

perform a regression of M1 on a constant, GDP, and CPI with first order autoregressive errors, and employ a Chow breakpoint test to determine whether the parameters before the 1970’s, during the 1970’s, and after the 1970’s are “stable”.

To regress the log of SPOT on a constant, the log of P_US, and the log of P_UK, and to carry out the Chow forecast test starting from 1973, enter the commands:

To test whether only the constant term and the coefficient on the log of P_US prior to and after 1970 are “stable” enter the commands:

See “Chow's Breakpoint Test” for further discussion.

See Equation::facbreak, Equation::breaktest, Equation::ubreak, and Equation::rls for related equation object views.

clabel reads the description of a series from the DRI Basic Economics Database and displays it in the status line at the bottom of the EViews window.

Use this command to verify the contents of a given DRI database series name. You must have the DRI database installed on your computer to use this feature.

displays the description of the DRI series LHUR on the status line.

See also ccopy, cfetch, read, fetch.

May only be used in programs.

See also @errorcount, seterrcount, seterr, and setmaxerrs.

Closing an object eliminates its window. If the object is named, it is still displayed in the workfile as an icon, otherwise it is deleted. Closing a program or workfile eliminates its window and removes it from memory. If a workfile has changed since you activated it, you will see a dialog box asking if you want to save it to disk.

option_or_name may be either an object name or one of the following options:

closes the three objects GDP, GRAPH1, and TABLE2.

opens the LWAGE window and displays the histogram view of LWAGE, then closes the window.

closes all windows within EViews.

closes all objects in EViews, leaving workfiles, programs, and database windows open.

See “Introduction” for a discussion of basic control of EViews.

See also exit and save.

There are three forms for the coint command which depend on the form of the test you wish to perform:

uses the coint keyword followed by the test_option and the number of lags n, and if desired, an “@”-sign followed by a list of exogenous variables. The first option must be one of the following six test options:

This type of cointegration testing may be used in a non-panel workfile. For Fisher combined testing using the Johansen framework, see below. The remaining options for the Johansen cointegration test are outlined below ( “Options for the Johansen Test”).

Note that the output for cointegration tests displays p-values for the rank test statistics. These p-values are computed using the response surface coefficients as estimated in MacKinnon, Haug, and Michelis (1999). The 0.05 critical values are also based on the response surface coefficients from MacKinnon-Haug-Michelis. Note: the reported critical values assume no exogenous variables other than an intercept and trend.

where

Cointegrating equation specifications that include a constant, linear, or quadratic trends, should use the “trend=” option to specify those terms. If any of those terms are in the stochastic regressors equations but not in the cointegrating equation, they should be specified using the “regtrend=” option.

Deterministic trend regressors that are not covered by the list above may be specified using the keywords @determ and @regdeterm. To specify deterministic trend regressors that enter into the regressor and cointegrating equations, you should add the keyword @determ followed by the list of trend regressors. To specify deterministic trends that enter in the regressor equations but not the cointegrating equation, you should include the keyword @regdeterm followed by the list of trend regressors.

Note that the p-values for the test statistics are based on simulations, and do not account for any user-specified deterministic regressors.

This type of cointegration testing may be used in a non-panel workfile. The remaining options for the single equation cointegration tests are outlined below.

The following options determine the specification of the Engle-Granger test (Augmented Dickey-Fuller) equation and the calculation of the variances used in the test statistic.

The following options control the computation of the symmetric and one-sided long-run variances in the Phillips-Ouliaris test.

Basic Options:

HAC Whitening Options:

HAC Kernel Options:

The coint command tests for cointegration among the series in the group. The second form of the command should be used with panel structured workfiles.

For panel cointegration tests, you may specify the type using one of the following keywords:

Depending on the type selected above, the following may be used to indicate deterministic trends:

Additional Options:

summarizes the results of the Johansen cointegration test for the series X, Y, and Z for all five specifications of trend. The test equation uses lags of up to order four.

performs the default Engle-Granger test on the residuals from a cointegrating equation which includes a constant. The number of lags is determined using the SIC criterion and an observation-based maximum number of lags.

employs a cointegrating equation that includes a constant and linear trend, and uses a sequential t-test starting at lag 10 with threshold probability 0.15 to determine the number of lags.

conducts an Engle-Granger cointegration test on the residuals from a cointegrating equation with a constant, using a fixed lag of 5.

performs the default Phillips-Ouliaris test on the residuals from a cointegrating equation with a constant, using a Bartlett kernel and Newey-West fixed bandwidth.

estimates the long-run covariances using a Quadratic Spectral kernel, Andrews automatic bandwidth, and no degrees-of-freedom correction.

estimates a cointegrating equation with a constant and linear trend, and performs the Phillips-Ouliaris test on the residuals by computing the long-run covariances using AR(1) prewhitening, a fixed bandwidth of 4, and the Bartlett kernel.

For a panel structured workfile,

performs Pedroni’s residual-based panel cointegration test with automatic lag selection with a maximum lag limit of 3. Automatic selection based on Schwarz criterion.

See “Cointegration Testing” for details on the various cointegration tests.

See Equation::coint and Group::coint for the related object routines.

List the coint keyword, followed by the dependent variable and a list of the cointegrating variables.

Cointegrating equation specifications that include a constant, linear, or quadratic trends, should use the “trend=” option to specify those terms. If any of those terms are in the stochastic regressors equations but not in the cointegrating equation, they should be specified using the “regtrend=” option.

Deterministic trend regressors that are not covered by the list above may be specified using the keywords @determ and @regdeterm. To specify deterministic trend regressors that enter into the regressor and cointegrating equations, you should add the keyword @determ followed by the list of trend regressors. To specify deterministic trends that enter in the regressor equations but not the cointegrating equation, you should include the keyword @regdeterm followed by the list of trend regressors.

In addition to these options, there are specialized options for each estimation method.

To estimate FMOLS or CCR use the “method=fmols” or “method=ccr” options. The following options control the computation of the symmetric and one-sided long-run covariance matrices and the estimate of the coefficient covariance.

To estimate using DOLS use the “method=dols” option. The following options control the specification of the lags and leads and the estimate of the coefficient covariance.

For the default covariance calculation or “cov=hac”, the following options control the computation of the long-run variance or robust covariance:

Weighted coefficient or coefficient covariance estimation in panel DOLS requires individual estimates of the long-run variances of the residuals. You may compute these estimates using the standard default long-run variance options, or you may choose to estimate it using the ordinary variance.

For weighted estimation we have:

For the coefficient covariance estimation we have:

To estimate, by FMOLS, the cointegrating equation for LC and LY including a constant, you may use:

The long-run covariances are estimated nonparametrically using a Bartlett kernel and a bandwidth determined by the Andrews automatic selection method. The coefficient covariances are estimated with no degree-of-freedom correction.

To include a linear trend term in a model where the long-run covariances computed using the Quadratic Spectral kernel and a fixed bandwidth of 10, enter:

A model with a cubic trend may be estimated using:

Here, the long-run covariances are estimated using a VAR(2) prewhitened Parzen kernel with Newey-West nonparametric bandwidth determined using 10 lags in the autocovariance calculations.

estimates a restricted model with a cubic trend term that does not appear in the cointegrating equation using a parametric VARHAC with automatic lag length selection based on the AIC. The residuals for the regressors equations are obtained by estimating the difference specification.

To estimate by CCR, we provide the “method=ccr” option. The command

estimates, by CCR, the constant only model using a VAR(2) prewhitened Quadratic Spectral and Andrews automatic bandwidth selection.

modifies the previous estimates by adding a linear trend term to the cointegrating and regressors equations, and changing the VAR prewhitening to automatic selection using the default SIC with a maximum lag length of 5.

adds a quadratic trend term to the regressors equations only, and changes the kernel to the default Bartlett.

estimates the linear specification using DOLS with four lags and leads. The coefficient covariance is obtained by rescaling the no d.f.-correction OLS covariance using the long-run variance of the residuals computed using the default Bartlett kernel and default fixed Newey-West bandwidth.

estimates a cubic specification using 4 lags and 2 leads, where the coefficient covariance uses a Bohman kernel and fixed bandwidth of 10.

estimates the same specification using a HAC covariance in place of the scaled OLS covariance.

computes the Static OLS estimates with the usual OLS d.f. corrected coefficient covariance.

See “Cointegrating Regression” for a discussion of single equation cointegrating regression. See “Panel Cointegration Estimation” for discussion of estimation in panel settings.

See “Technical Discussion” for a discussion of VEC estimation.

See also coint.

Place a column or rowvector object in a specified column of a matrix.

Places the column vector or rowvector v into the matrix m at column n. The number of rows of m and v must match, and the destination column must already exist within m.

The third column of M1 will be set equal to the vector V1.

See also rowplace and matplace.

sends arg to the command capture window. Of particular use to add-in writers for sending notifications to users.

See “Command Capture” for further details.

See also statusline.

where src_spec and dest_spec are of the form:

There are three parts to the copy command: (1) a specification of the location and names of the source objects; (2) a specification of the location and names of the destination objects; (3) optional source and destination IDs if the copy operation involves match merging.

The source and destination objects are specified in multiple (optional) parts: (1) the container specification is the name of a workfile or database; (2) the page specification is the name of a page within a workfile or a subdirectory within a database; and (3) the object_name specification is the name of an object or a wildcard pattern corresponding to multiple objects.

The ctype specification is rarely required, but permits you to specify precisely your source or destination in cases where a database and workfile share the same name. In this case, ctype may be used to indicate the container to which you are referring by prefixing the container name with “:” to indicate the workfile, or “::” to indicate the database with the common name.

When parts of the source or destination specification are not provided, EViews will fill in default values where possible. The default container is the active workfile, unless the “::” prefix is used in which case the default container is the default database. The default page within a workfile is always the active page. The default name for the destination object is the name of the object within the source container.

If ID series are not provided in the command, then EViews will perform frequency conversion when copying data whenever the source and destination containers have different frequencies. If ID series are provided, then EViews will perform a general match merge between the source and destination using the specified ID series. In the case where you wish to copy your data using match merging with special treatment for date matching, you must use the special keyword “@DATE” as an ID series for the source or destination. If “@DATE” is not specified as an identifier in either the source or destination IDs, EViews will perform an exact match merge using the given identifiers.

If ID series are not specified, but a conversion option requiring a general match merge is used (e.g., “c=med”), “@DATE @DATE” will be appended to the list of IDs and a general date match merge will be employed.

See Link::linkto for additional discussion of the differences embodied in these choices.

The general syntax described above covers all possible uses of the copy command. The following paragraphs provide examples of the specific syntax used for some common cases of the command.

Copy an object within the default workfile page as a new object with a different name:

Copy an object from the src_page page into the default workfile page using the specified name:

Copy an object from the src_page page into the dest_page page, keeping the same name:

Copy an object from the src_page page to the default workfile page, match merging any series data using a single src_id and a single dest_id identifier series:

Copy an object from the src_page page to the dest_page match merging any series data using multiple source and destination identifier series:

Copy one or more objects from the src_page of the workfile src_workfile to the dest_page of the workfile dest_workfile, using the name or name pattern given in src_names:

Copy an object from database src_database to the default page in the container dest_container:

Note that if both a workfile and database exist matching the name provided in dest_container, EViews will favor the workfile unless the “::” prefix is used to specify explicitly that the database should be used.

When copying a group object from workfile to database:

When copying a group object from a database to a workfile:

Note that copying a group object containing expressions or auto-updating series between workfiles only copies the expressions, and not the underlying series.

If the copy command does not specify identifier series, EViews will perform frequency conversion of the data contained in series objects whenever the source and destination containers are dated, but do not have the same frequency.

If either of the pages are undated, EViews will, unless match merge options are provided (as described below), perform a raw copy, in which the first observation in the source workfile page is copied into the first observation in the destination page, the second observation in the source into the second observation in the destination, and so forth.

The following options control the frequency conversion method when copying series and group objects into a workfile page and converting from low to high frequency:

In addition, for Denton, Chow-Lin, and Litterman conversions, you must specify the indicator series by appending the keyword “@indicator” followed by the series name at the end of the copy command.

The following options control the frequency conversion method when copying series and group objects into a workfile page and converting from high to low frequency:

Note that if no conversion method is given in the command, the conversion method specified within the series object will be used as the default. If the series does not contain an explicit conversion method, the global option settings will used to determine the method.

Frequency conversion involving panel structured pages involves special handling:

In all three of these cases, all of the high-to-low conversion methods are supported, but low-to-high frequency conversion only offers Constant-match average (repeating of the low frequency observations).

Lastly, conversion involving a panel page with more than one dimension or an undated page will default to raw data copy unless general match merge options are provided.

These options are available when ID series are specified in the copy commmand.

Most of the conversion options should be self-explanatory. As for the others: “first” and “last” give the first and last non-missing observed for a given group ID; “obs” provides the number of non-missing values for a given group; “nas” reports the number of NAs in the group; “unique” will provide the value in the source series if it is the identical for all observations in the group, and will return NA otherwise; “none” will cause the copy to fail if there are multiple observations in any group—this setting may be used if you wish to prohibit all contractions.

On a match merge expansion, copying with match merging will repeat the value of the source for every observation with matching identifier values in the destination. If both the source and destination have multiple values for a given ID, EViews will first perform a contraction across IDs in the source (if not ruled out by “c=none”), and then perform the expansion by replicating the contracted value in the destination. For example, converting from a quarterly panel to an annual panel using match merge, EViews will first contract the data across IDs down to a single quarterly time series, will convert the series to an annual frequency, then will assign the annual data to each of the cross-sections in the destination page.

makes an exact copy of GOOD_EQUATION and names it BEST_EQUATION.

copies GRAPH_1 from the default page of the current workfile to GRAPH1 in the page WKLY of the workfile WF2.

copies GDP from the current workfile to GDP in the USDAT database or workfile.

copies GDP from the default database to either the open workfile MACRO1, or the database named MACRO1 if there is no open workfile with that name. If there is an open workfile MACRO1 you may use

to specify explicitly that you wish to write to the MACRO1 database.

copies POP data for 1990 through 2005 from PAGE1 to PAGE2, match merge using the ids COUNTY and the date structure of the two pages.

copies the INC data from the PANELPAGE to the COUNTYPAGE, match merging using the values of the COUNTY series, and contracting the panel data by computing means for each county using the specified sample.

match merges the POP data from the COUNTYPAGE to the PANELPAGE using the values of the COUNTY series.

copies all objects with names beginning with “SER” on page PAGE1 of workfile QUARTERLY into page PAGE6 of workfile ANNUAL using the existing names. Series objects with data that can be (high-to-low) frequency converted will take the maximum value within a low-frequency period as the conversion method. If destination series already exist with the same name as the source series, the data will be merged. If destination objects (non-series) exist with the same name as source series, they will be overwritten.

Note that since databases are read from disk, you may provide a path for the database in the container specification, as in:

which copies the object SER01 from the database DBA.EDB located in the path “C:\MY DATA\” to SER02 in the default workfile page.

makes a duplicate of all objects in the default page of the current workfile with names starting with “GD” to the database FINDAT in the root of “C:\MY DATA\”.

See “Copying Objects” for a discussion of copying and moving objects.

See also fetch, store, and Link::linkto.

where arg1, arg2, etc. are the names of series or groups.

Note that this command is a limited feature version of the group view Group::cor.

See Group::cor for more general routines for computing correlations.

See also cov. For simple functions to perform the calculations, see @cor, and @cov.

Follow the count keyword by the name of the dependent variable and a list of regressors.

The command:

estimates a normal QML count model of Y on a constant, X1, and X2, with fixed variance parameter 2, and GLM standard errors.

estimates a Poisson count model of ARREST on a constant, JOB, and POLICE, and stores the generalized residuals in the series RES_G.

estimates a Poisson count model of Y on a constant and X1, and saves the fitted values (conditional mean) in the series YHAT.

estimates the same model with QML standard errors and covariances.

See “Count Models” for additional discussion.

See Equation::count for the equivalent equation object command.

where arg1, arg2, etc. are the names of series or groups.

Note that this command is a limited feature version of the group view Group::cov.

See Group::cov for more general routines for computing covariances.

See also cor .For simple functions to perform the calculations, see @cor, and @cov.

This command has been replaced by wfcreate and pagecreate.

You must specify the number of lags n to use in computing the cross correlations as the first option. EViews will create an untitled group from the specified series and groups, and will display the cross correlation view for the group.

The following options may be specified inside the parentheses after the number of lags:

displays the cross correlogram between the log of M1 and the first difference of the log of CPI, using up to 36 leads and lags.

The first line estimates a GARCH(1,1) model and the second line retrieves the standardized residuals. The third line plots the cross correlogram squared standardized residual and the standardized residual, up to 24 leads and lags. This correlogram provides a rough check of asymmetry in the ARCH effect.

See “Cross Correlations and Correlograms” for discussion.

See Group::cross for the equivalent group view command.

Opens an unnamed group window to edit one or more series.

Follow the data keyword by a list of series and group names. You can list existing names or new names. Unrecognized names will cause new series to be added to the workfile. These series will be initialized with the value “NA”.

opens a group window containing the series in group GROUP1, and the series NEWX and NEWY.

See “Entering Data” for a discussion of the process of entering data from the keyboard.

If the specified database does not exist, a new (empty) database will be created and opened. The opened database will become the default database.

Follow the db command by the name of the database to be opened or to be created (if it does not already exist). You may include a path name to work with a database not in the default path.

You may use the “as” keyword to provide an optional shorthand_name or short text label which may be used to refer to the database in commands and programs. If you leave this field blank, a default shorthand_name will be assigned automatically.

See “Database Shorthands” for additional discussion.

See dbopen for a list of available options for working with foreign format databases.

opens the database FINDAT in the default path and makes it the default database from which to store and fetch objects. If the database FINDAT does not already exist, an empty database named FINDAT will be created and opened.

See “EViews Databases” for a discussion of EViews databases.

See also dbcreate and dbopen.

Follow the dbcopy command by the name of the existing database and a name for the copy. You should include a path name to copy from or to a database that is not in the default directory. All files associated with the database will be copied.

The following table summaries the various database formats, along with the corresponding allowable “type=” and “desttype=” keywords:

For the source specification, the following options may be required when connecting to a remote server:

For the destination specification, the following options may be required when connecting to a remote server:

makes a copy of all files associated with the database USDAT in the default path and stores it in the “c:\backup” directory under the basename “Usdat”.

See “EViews Databases” for a discussion of EViews databases.

See also dbrename and dbdelete.

Follow the dbcreate keyword by a name for the new database. You may include a path name to create a database not in the default directory. The new database will become the default database.

You may use the “as” keyword to provide an optional shorthand_name or a short text label which may be used to refer to the open database in commands and programs. If you leave this field blank, a default shorthand_name will be assigned automatically. See “Database Shorthands” for additional discussion.

The following table summaries the various database formats, along with the corresponding “type=” keywords:

DRIBase and FAME databases are not supported in EViews Standard Edition.

The following options may be required when connecting to a remote server:

creates a new database named MACRODAT in the default path, and makes it the default database from which to store and fetch objects. This command will issue an error message if a database named MACRODAT already exists. To open an existing database, use dbopen or db.

See “EViews Databases” for a discussion of EViews databases.

See also dbopen or db.

Follow the dbdelete keyword by the name of the database to be deleted. You may include a path name to delete a database not in the default path.

deletes all files associated with the TESTDAT database in the specified directory.

See “EViews Databases” for a discussion of EViews databases.

See also dbcopy and dbdelete.

Follow the dbopen keyword with the name of a database. You should include a path name to open a database not in the default path. The opened database will become the default database.

You do not need to specify a database name when opening a Datastream or FRED connection (“type=datastream” or “type=fred”) as EViews will automatically connect to the proper location.

You may use the “as” keyword to provide an optional shorthand_name or a short text label which is used to refer to the open database in commands and programs. If you leave this field blank, a default shorthand_name will be assigned automatically.

See “Database Shorthands” for additional discussion.

By default, EViews will use the extension of the database file to determine type. For example, files with the extension “.EDB” will be opened as an EViews database. You may use the “type=” option to specify an explicit type.

The following table summaries the various database formats, along with the corresponding “type=” keywords:

In addition, specific types may require installation of additional software. For details see, “Notes on Particular Formats”.

The following options may be required when connecting to a remote server:

opens a database named US1 in the C:\DATA directory. The command:

opens a database in the default path. If the specified database does not exist, EViews will issue an error message. You should use db or dbcreate to create a new database.

See “EViews Databases” for a discussion of EViews databases.

See also db and dbcreate.

Follow the dbpack keyword by a database name. You may include a path name to pack a database not in the default path.

packs the database named FINDAT in the default path.

See “Packing the Database” for additional discussion.

See also dbrebuild.

Rebuild a seriously damaged database into a new database file.

Follow the dbrebuild keyword by the name of the database to be rebuilt, and then a new database name.

If you issue the command:

EViews will attempt to rebuild the database TESTDAT into the database FIXED_TESTDAT in the default directory.

See “Maintaining the Database” for a discussion.

See also dbpack.

dbrename renames all files associated with the specified database.

Follow the dbrename keyword with the current name of an existing database and the new name for the database.

Renames all files associated with the TESTDAT database in the specified directory to MYDAT in the default directory.

See “EViews Databases” for a discussion of EViews databases.

See db and dbcreate. See also dbcopy and dbdelete.

Follow the keyword by a list of the names of any objects you wish to remove from the current workfile. Deleting does not remove objects that have been stored on disk in EViews database files.

You can delete an object from a database by prefixing the name with the database name and a double colon. You can use a pattern to delete all objects from a workfile or database with names that match the pattern. Use the “?” to match any one character and the “*” to match zero or more characters.

If you use delete in a program file, EViews will delete the listed objects without prompting you to confirm each deletion.

To delete all objects in the workfile with names beginning with “TEMP”, you may use the command:

To delete the objects CONS and INVEST from the database MACRO1, use:

See “Object Commands” for a discussion of working with objects.

See “EViews Databases” for a discussion of EViews databases.

unregisters the specified program file as an EViews Add-in.

If you do not provide the optional path specification, EViews looks for the program file in the default EViews Add-ins directory.

Explicit path specifications containing “.\” and “..\” (to indicate the current level and one directory level up) are evaluated relative EViews default directory.

You may use the special “<addins>” directory keyword in your path specification.

unregisters the Add-in associated with file “Myaddin.prg”.

Alternatively,

unregisters the Add-in whose proc name matches “myaddin”. Note that this name may not match the program name.

unregisters the graph “Recshade” specific Add-in. In cases, where more than 1 Add-in has the same proc name, the type is useful to differentiate which is to be unregistered.

See “Add-ins” for a detailed discussion of Add-ins.

List the dependent variable, followed by an optional list of exogenous regressors, followed by an “@” and then the binary treatment variable. You should not include a constant in the specification.

estimates an equation by difference-in-difference with ASMRS as the outcome variable, and POST as the treatment variable.

estimates an equation by difference-in-difference with LEMP as the outcome variable, TREATED as the treatment variable, and LPOP as an exogenous regressor.

See “Difference-in-Difference Estimation” for a discussion of difference-in-difference models.

do is most useful in EViews programs where you wish to run a series of commands without opening windows in the workfile area.

The first line redirects table output to a file on disk. The second line carries out a unit root test of GDP without opening a window, and prints the results to the disk file.

See “Object Commands” for a discussion of working with objects.

“Object View and Procedure Reference” provides a complete listing of the views of the various objects.

See also show.

You must create an EViews database to store the converted DRI data before you use this command. This command may be very time-consuming.

Follow the command by listing the name of an existing EViews database into which you would like to copy the DRI data. You may include a path name to specify a database not in the default path.

The first line creates a new (empty) database named DRIBASIC in the default directory. The second line copies all the data in the DRI Basic Economics database into in the DRIBASIC database. The last example copies the DRI data into the database DRIDBASE that is located in the C:\MYDATA directory.

See “EViews Databases” for a discussion of EViews databases.

See also dbcreate and db.

List the dependent variable first, followed by a list of the independent variables. Use a “C” if you wish to include an unpenalized intercept term.

Note that PDL and ARMA terms are not permitted in elastic net specifications.

or

where weight_value is a non-negative value, coef_min is a non-positive minimum coefficient value, and coef_max is a non-negative maximum coefficient value.

There are two forms of the special expression.

In the abbreviated form, you specify the variable name followed by the penalty weight value.

In the more general form, you specify the variable name followed by one or more keyword expressions in arbitrary order, with the “wgt=” argument specifying the penalty weight, “cmin=” with a non-positive minimum coefficient value, and “cmax=” with a non-negative maximum coefficient value.

When specifying individual regressor behavior using @vw, keep in mind that:

EViews will normalize the individual penalty weights so that they sum to the number of coefficients.

The command

Similarly,

estimates a Lasso model with regressor population standard deviation scaling, with the remaining settings as before, while

estimates the equivalent ridge regression specification.

The command

We may use the @vw specifications to assign individual penalties and coefficient restrictions

estimates an elastic net model with the coefficient of LCAVOL restricted to be less than or equal to 0.4, and the coefficient of PGG45 having a relative penalty weight of 1.2, and a maximum value of 0.075.

Identical specifications may be estimated using vectors of penalty weights and coefficient restrictions,

and

since the penalty weight for PGG45 in the vector is overwritten by the individual weight specified using the @vw.

Note that in neither case is the intercept penalized, even though the corresponding element of LWGT is equal to 1 since the specification of “C” is always implicitly treated as “@VW(C, 0)”.

See “Elastic Net and Lasso” for a discussion of elastic net, ridge regression, and Lasso models.

See Equation::enet for the object version of this command.

unregisters the specified program file as an EViews Add-in.

If you do not provide the optional path specification, EViews looks for the program file in the default EViews Add-ins directory.

Explicit path specifications containing “.\” and “..\” (to indicate the current level and one directory level up) are evaluated relative EViews default directory.

You may use the special “<addins>” directory keyword in your path specification.

unregisters the Add-in associated with file “Myaddin.prg”.

Alternatively,

unregisters the Add-in whose proc name matches “myaddin”. Note that this name may not match the program name.

unregisters the graph “Recshade” specific Add-in. In cases, where more than 1 Add-in has the same proc name, the type is useful to differentiate which is to be unregistered.

See “Add-ins” for a detailed discussion of Add-ins.

The exec command executes a program. The program may be located in memory or stored in a program file on disk.

If you wish to pass one or more options to the program, you should enclose them in parentheses immediately after the filename. If the program has arguments, you should list them after the filename.

EViews first checks to see if the specified program is in memory. If the program is not located, EViews then looks for the program on disk in the EViews Add-ins directory, or in the specified path. The program file should have a “.PRG” extension, which you need not specify in the prog_name.

will run the program “Rollreg.prg” in the EViews add-in directory.

will run the program “Recshade” in the EViews add-in directory, setting the _this object to GRAPH01.

will run the program “Simul.prg” in the path “c:\myfiles\”, with program option string “h=3”, the %0 argument set to “XHAT”, and with the maximum error count set to 4.

Note that in contrast to the run command, exec will not stop executing a running program after returning from the executed program. For example if you have a program containing:

the print statement will be executed after running the “Simul.prg” program. If you replace exec with run, the program will stop after executing the commands in “Simul.prg”.

See “Executing a Program” and “The Active Object Keyword” for further details.

See also run and include.

You will be prompted to save objects and workfiles which have changed since the last time they were saved to disk. Be sure to save your workfiles, if desired, since all changes that you do not save to a disk file will be lost.

See also close and save.

No longer supported. See the replacement command pagestruct.

Carries out a factor breakpoint test for parameter constancy.

You must provide one or more series to be used as the factors with which to split the sample into categories. To specify more than one factor, separate the factors by a space. If the equation is specified by list and contains no linear terms, you may specify a subset of the regressors to be tested for a breakpoint after an “@” sign.

The commands

perform a regression of the log of SPOT on a constant, the log of P_US, and the log of P_UK, and employ a factor breakpoint test to determine whether the parameters are stable through the different values of SEASON.

To test whether only the constant term and the coefficient on the log of P_US are “stable” enter the commands:

See “Factor Breakpoint Test” for further discussion.

See also Equation::facbreak and Equation::rls.

where:

and the available options are specific to the factor estimation method (see “Factor Methods”).

The factest command allows you to estimated a factor analysis model without first declaring a factor object and then applying an estimation method. It provides a convenient method of interactively estimating transitory specifications that are not to be named and saved with the workfile.

Estimation of a factor analysis specification using factest only differs from estimation using a named factor and a factor estimation procedure (e.g., Factor::ipf) in the use of the “method=” option and in the fact that the command results in an unnamed factor object.

The command:

estimates a factor analysis model for the series in G1 using GLS. The result is an unnamed factor object. (Almost) equivalently, we may declaring and estimate the factor analysis object using the Factor::gls estimation method procedure

which differs only in the fact that the former yields an unnamed factor object and the latter saves the object F1 in the workfile.

The command:

estimates the factor model using the partial correlation for the series in GROUP01, conditional on the series SER1 and SER2. The command is equivalent to:

except the latter names the factor object F2.

See “Factor Analysis” for a general discussion of factor analysis. The various estimation methods are described in “Estimation Methods”.

See Factor::gls, Factor::ipf, Factor::ml, Factor::pf, and Factor::uls.

fetch reads one or more objects from EViews databases or databank files into the active workfile. The objects are loaded into the workfile using the object in the database or using the databank file name.

If you fetch a series into a workfile with a different frequency, EViews will automatically apply the frequency conversion method attached to the series by setconvert. If the series does not have an attached conversion method, EViews will use the method set by Options/Date-Frequency in the main menu. You can override the conversion method by specifying an explicit conversion method option.

The fetch command keyword is followed by a list of object names separated by spaces. The default behavior is to fetch the objects from the default database (this is a change from versions of EViews prior to EViews 3.x where the default was to fetch from individual databank files).

You can precede the object name with a database name and the double colon “::” to indicate a specific database source. If you specify the database name as an option in parentheses (see below), all objects without an explicit database prefix will be fetched from the specified database. You may optionally fetch from individual databank files or search among registered databases.

You may use wild card characters, “?” (to match a single character) or “*” (to match zero or more characters), in the object name list. All objects with names matching the pattern will be fetched.

To fetch from individual databank files that are not in the default path, you should include an explicit path. If you have more than one object with the same file name (for example, an equation and a series named CONS), then you should supply the full object file name including identifying extensions.

The following options are available for fetch of group objects:

The database specified by the double colon “::” takes precedence over the database specified by the “d=” option.

The following options control the frequency conversion method when copying series and group objects into a workfile page and converting from low to high frequency:

The following options control the frequency conversion method when copying series and group objects to a workfile, converting from high to low frequency:

If no conversion method is specified, the series-specific or global default conversion method will be employed.

To fetch M1, GDP, and UNEMP from the default database, use:

To fetch M1 and GDP from the US1 database and UNEMP from the MACRO database, use the command:

You can fetch all objects with names starting with “SP” by searching all registered databases in the search order. The “c=f” option uses the first (nonmissing) observation to convert the frequency of any matching series with a higher frequency than the destination workfile frequency:

You can fetch M1 and UNEMP from individual databank files using:

To fetch all objects with names starting with “CONS” from the two databases USDAT and UKDAT, use the command:

The command:

will fetch all series beginning with the letter “A”, followed by any single character, and ending with the string “income”.

Use the “notifyillegal” option to display a dialog when fetching the series MYILLEG@LNAME that will suggest a valid name and give you to opportunity to name the object before it is inserted into a workfile:

The command:

Fetches the series US_GDP, US_INV and US_CONS into workfile series with the same names.

Fetches the database series IBM US EQUITY, and MSFT US EQUITY into workfile series with names IBM and MSFT.

Specifying the “tr” option causes EViews 10 to truncate long series names to 24 characters (by default) instead of 300. This will let programs written for EViews 9 continue to work with EViews 10:

The “tr” option can also be set to a number between 0 and 300:

See “EViews Databases” for a discussion of databases, databank files, and frequency conversion. Appendix A. “Wildcards” describes the use of wildcard characters.

See also store, and copy.

See Series::setconvert for information on default conversion settings.

When the regressor contains lagged dependent values or ARMA terms, fit uses the actual values of the dependent variable instead of the lagged fitted values. You may instruct fit to compare the forecasted data to actual data, and to compute forecast summary statistics.

(Note that we recommend that you instead use the equation proc Equation::fit since it explicitly specifies the equation of interest.)

Not available for equations estimated using ordered methods; use Equation::makemodel to create a model using the ordered equation results.

Following the fit keyword, you should type a name for the forecast series and, optionally, a name for the series containing the standard errors and, for ARCH specifications, a name for the conditional variance series.

Forecast standard errors are currently not available for binary, censored, and count models.

Options for forecasting from a functional coefficients estimated equation.

The first line estimates a linear regression of CONS on a constant, CONS lagged once, INC, and INC lagged once. The second line stores the static forecasts and their standard errors as C_HAT and C_SE. The third and fourth lines compute the +/- 2 standard error bounds. The fifth line plots the actual series together with the error bounds.

The first line estimates a logit specification for Y with a conditional mean that depends on a constant, WAGE, and EDU. The second line computes the fitted probabilities, and the third line computes the fitted values of the index. The fourth line computes the probabilities from the fitted index using the cumulative distribution function of the logistic distribution. Note that YF and YHAT should be identical.

Note that you cannot fit values from an ordered model. You must instead solve the values from a model. The following lines generate fitted probabilities from an ordered model:

The first line estimates an ordered probit of Y on a constant, X, and Z. The second line makes a model from the estimated equation with a name OPROB1. The third line solves the model and computes the fitted probabilities that each observation falls in each category.

To perform dynamic forecasting, use forecast.

See “Forecasting from an Equation” for a discussion of forecasting in EViews and “Discrete and Limited Dependent Variable Models” for forecasting from binary, censored, truncated, and count models.

See “Forecasting” for a discussion of forecasting from sspace models.

See Equation::forecast and Equation::fit for the equivalent object commands.

See Equation::makemodel and Model::solvefor forecasting from systems of equations or ordered equations.

forecast computes the forecast using the default equation for all observations in a specified sample. In some settings, you may instruct forecast to compare the forecasted data to actual data, and to compute summary statistics.

(Note that we recommend that you instead use the equation proc Equation::forecast since it explicitly specifies the equation of interest.)

You should enter a name for the forecast series and, optionally, a name for the series containing the standard errors. Forecast standard errors are currently not available for binary or censored models. forecast is not available for models estimated using ordered methods.

The following lines:

estimate a linear regression over the period 1970Q1–1990Q4, computes dynamic forecasts for the period 1991Q1–1995Q4, and plots the forecast as a line graph.

estimates an ARMA(1,1) model, computes the forecasts and standard errors with and without the ARMA terms, and plots the two forecast standard errors.

To perform static forecasting, see fit.

See “Forecasting from an Equation” for a discussion of forecasting in EViews and “Discrete and Limited Dependent Variable Models” for forecasting from binary, censored, truncated, and count models.

See “Forecasting” for a discussion of forecasting from sspace models.

See Equation::forecast and Equation::fit for the equivalent object commands.

See Equation::makemodel and Model::solvefor forecasting from systems of equations or ordered equations.

If you follow the keyword freeze with an object name but no view of the object, freeze will use the default view for the object. You may provide a destination name for the object containing the frozen view in parentheses.

creates an untitled table that contains the results of the unit root test of the series GDP.

freezes a graph named GRA1 that contains multiple line graphs of the series in the group RATES, realigns the individual graphs, and displays the resulting graph.

creates a graph object named MYGRA that combines three graph objects GRA1, GRA2, and GRA3, and displays MYGRA in two columns.

creates a graph object MYGRA that combines the three graph objects GRA1, GRA2 and GRA3, and displays MYGRA in two columns. If the object MYGRA already exists, it would be replaced by the new object.

See “Object Commands” for discussion. See also “Object Basics” for further discussion of objects and views of objects.

Freezing graph views is described in “Creating Graph Objects”.

Follow the frml keyword with a name for the object, and an assignment statement. The special keyword “@CLEAR” is used to return the auto-updating series to an ordinary numeric or alpha series.

To define an auto-updating numeric or alpha series, you must use the frml keyword prior to entering an assignment statement. The following example creates a series named LOW that uses a formula to compute its values.:

The auto-updating series takes the value 1 if either INC is less than or equal to 5000 or EDU is less than 13, and 0 otherwise, and will be re-evaluated whenever INC or EDU change.

If FIRST_NAME and LAST_NAME are alpha series, then the formula declaration:

creates an auto-updating alpha series FULL_NAME.

You may apply a frml to an existing series. The commands:

makes the previously created series Z an auto-updating series containing the average of series X and Y. Note that once a series is defined to be auto-updating, it may not be modified directly. Here, you may not edit Z, nor may you generate values into the series.

Note that the commands:

while similar, produce quite different results, since the absence of the frml keyword in the second example means that EViews will generate fixed values in the series instead of defining a formula to compute the series values. In this latter case, the values in the series Z are fixed, and may be modified.

One particularly useful feature of auto-updating series is the ability to reference series in databases. The command:

creates a series called GDP that obtains its values from the series GDP in the database USDATA. Similarly:

creates an auto-updating series that is the log of the values of GDP in the database USDATA.

To turn off auto-updating for a series or alpha, you should use the special expression “@CLEAR” in your frml assignment. The command:

sets the series to numeric or alpha value format, freezing the contents of the series at the current values.

See “Auto-Updating Series” for a discussion of updating series.

See also Link::link.

List the funcoef keyword, the dependent variable and a list of the regressor variables, followed by the “@” symbol and the name of the functional coefficient series.

We consider examples for three equations that estimate FCOEF using UNRATE as the dependent variable, UNRATE(-1 to -2) as independent variables, and LWAGE(-4) as the functional coefficient variable.

evaluates over a custom uniform grid from 0 to 10 with length 100.

evaluates over a custom grid provided by the values of a workfile vector called VECGRID.

estimates using local polynomial fitting with main polynomial degree 3 and auxiliary polynomial degree 5. The latter is employed deriving bias, variance, and bandwidths.

See “Functional Coefficient Regression” for additional discussion on functional coefficients estimation.

Generate series or alphas.

generates a numeric series that takes the values from the series X and adds 3.

creates an alpha series formed by concatenating the alpha series FIRST_NAME and LAST_NAME.

See “Working with Data” for discussion of generating series data.

See Series::series and Alpha::alpha for a discussion of the expressions allowed in genr.

List the glm keyword, followed by the dependent variable and a list of the explanatory variables, or an explicit linear expression.

If you enter an explicit linear specification such as “Y=C(1)+C(2)*X”, the response variable will be taken to be the variable on the left-hand side of the equality (“Y”) and the linear predictor will be taken from the right-hand side of the expression (“C(1)+C(2)*X”).

Offsets may be entered directly in an explicit linear expression or they may be entered as using the “offset=” option.

In addition to the specification options, there are options for estimation and covariance calculation.