Introduction to

Econometric Views

2016

“Written by

N.Nilgün Çokça

WHAT IS EVIEWS?

EViews provides sophisticated data analysis, regression, and forecasting tools on

Windows-based computers. With EViews you can quickly develop a statistical relation from

your data and then use the relation to forecast future values of the data. Areas where EViews

can be useful include: scientific data analysis and evaluation, financial analysis,

macroeconomic forecasting, simulation, sales forecasting, and cost analysis.

EViews is a new version of a set of tools for manipulating time series data originally

developed in the Time Series Processor software for large computers. The immediate

predecessor of EViews was MicroTSP, first released 1981. Though EViews was developed by

economists and most of its uses are in economics, there is nothing in its design that limits its

usefulness to economic time series. Even quite large cross-section projects can be handled in

EViews.

EViews takes advantage of the visual features of modern Windows software. You can

use your mouse to guide the operation with standard Windows menus and dialogs. Results

appear in Windows and can be manipulated with standard Windows technique. Alternatively,

you may use Eviews’ powerful command and batch processing language. You can enter and

edit commands in the command window. You can create and store the commands in programs

that document your research project for later execution.

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Basic Terms of Eviews

The Initial Screen

When you are using Eviews, you can tell it what to do by typing simple commands, by

using menus, or running program files. The following discussion will focus on the commands

and menus. Program files are just collections of commands.

Work Files

The work file is held in the computer’s RAM memory during a session and contains all

the data entered or created during a session. You have several options to start a new work file.

In either case, you must tell Eviews what kind of data you will be using. All data are either time

series or cross-sectional data. Time series have a frequency of Annual, Semi_Annual,

Quarterly, Monthly, or Daily. Cross-sectional data are undated. Each series, once created, is

stored as a single variable with a series name.

Using the Menu System

You can create a new data series using the FILE/NEW/WORKFILE option from the main

menu. EViews will ask for the frequency of the data and the earliest and latest dates you will

be using in the session. For the first assignment, the data are annual for 1979 to 1988. The first

date can be entered as 1979 or as 79. An untitled workfile will appear as in the figure below.

This workfile will have two objects: C and RESID.

Using a Command Line Entry

If you use the command line to create the workfile, enter

create A 1979 1988

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If you use the command line to create the workfile enter

Some Frequency Specifications

If your data are You enter at the CREATE prompt

Undated (e.g., cross-sectional data) The number of observations you will enter

Annual A 70 80

or

A 1970 1980

Semi-Annual S 70 80

or

S 1970 1980

Quarterly Q 70.1 80.4

or

Q 1970.1 1980.4

where the number after the dot is the quarter

Monthly M 70.01 80.12

or

M 1970.01 1980.12

where the number after the dot is the month

Entering Data

Once the workfile is specified, select OBJECT from the workfile menu bar and SERIES

from the New Object dialog box. Enter a name in the space provide. Click OK. The name you

entered will appear in the workfile list.

At this point, you just have a name for a series that will be annual data from 1979 to 1988,

but no data; the series is empty. To enter data (or edit the data later), double click the series

name. a spreadsheet form will appear as shown below. click EDIT +/- to toggle data entry.

Enter data as you would expect, say. Using a spreadsheet. Click EDIT +/- to save it. Select

FILE/SAVE from the main menu to save the workfile.

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Rules for Naming A Series

1. Every name must begin with a letter.

2. Subsequent characters may be letter or digits

3. Maximum of 8 characters

4. Do not use the following reserved names:

ABS, ACOS, AR, ASIN, C, CON, CNORM, COEF, COS, D, DLOG,

DNORM, ELSE, ENDIF, EXP, LOG, LOGIT, LPT1, LPT2, MA, NA,

NRND, PDL, RESID, RND, SAR, SIN, SMA, SQR, and THEN.

EViews accepts both capital letters and lower case letters in the name you give to your

series and other objects, but it treats name as all capital letters., even if entered in lower case.

Its messages to you will follow normal capitalization rules. For example, ‘SALES’, ‘sales’, and

‘sAles’ are all the same object in EViews. For the sake of uniformity, we have written all

examples of input using names in lower case, but you should feel free to use capital letters

instead.

Viewing Data

You can either type SHOW seriesnames in the Command Window or double click the

series name in the workfile display. The command line entry version is shown in the next

display.

To view more than one series, select several at once by clicking on them (using the Ctrl

key) and then selecting OPEN GROUP. The selected series will now be visible.

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Importing Data

An effective way to manage your data is to use Excel. Excel will allow you to use a

familiar interface plus give you some added capabilities, especially for small datasets.

Generating a New Series

One of the most powerful features of EViews is the ability to use and to process

mathematical expressions. EViews contains an extensive library of built-in operators and

functions that allow you to perform complicated mathematical operations on your data with just

a few keystrokes. In addition to supporting standard mathematical and statistical operations,

EViews provides a number of specialized functions for automatically handling the leads, lags

and differences that are commonly found in time series data.

An EViews expression is a combination of numbers, series names, functions, and

mathematical and relational operators. In practical terms, you will use expressions to describe

all mathematical operations involving EViews objects. As in other programs, you can use these

expressions to calculate a new series from existing series, to describe a sample of observations,

or to describe an equation for estimation or forecasting. However, EViews goes far beyond this

simple use of expressions by allowing you to use expressions virtually anywhere you would

use a series.

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The most common operators and functions are

Operator/function Explanation

+ add

- subtract

* multiply

/ divide

^ raise to a power

> greater than; X>Y has the value 1 if X exceeds Y and 0 otherwise

< less than; X<Y has the value 1 if Y exceeds X and 0 otherwise

<> not equal; X<>Y has the value 1 if X differs from Y and 0

otherwise

<= less than or equal; X<=Y has the value 1 if X does not exceed Y

and 0 otherwise

>= greater than or equal; X>= Y has the value 1 if Y does not exceed

X and 0 otherwise

AND combines logical operators; X>Y AND Z>Y has the value 1 if

BOTH conditions are met and 0 otherwise

OR combines logical operators; X>Y OR Z>Y has the value 1 if

EITHER condition is met and 0 otherwise

log(X) natural log of X

exp(X) exponentiation, eX

abs(X) absolute value of X

sqr(X) square root of X

C(X) value of a coefficient X where X is a number from 1 to 100

dnorm(X) standard normal density function of X

cnorm(X) standard cumulative normal distribution function of X

nrnd returns a normally distributed random number with variance equal

to 1

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LEARN BY DOING WITH DEMO FILE

The first step in the project is to read the data into an EViews workfile.

To create a workfile to hold your data, select File/New/Workfile…, which opens a dialog

box where you will provide information about your data:

For our example, quarterly data are observed from the first quarter 1952 to the end of the

1996. You should set the workfile frequency quarterly, and specify the start date 1952:1, and

the end date 1996:4.

Once you have filled out the dialog, click on the OK button. EViews will create an untitled

workfile, and will display the workfile window.

Notice that the workfile window displays two pairs of dates: one for the range dates

contained in the workfile, and the second for the current workfile sample. Note also that the

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workfile contains the coefficient vector C and the series RESID. All EViews workfiles will

contain these two objects.

The next step is to import data into the workfile. The data for the four variables used in

the analysis have been provided in an Excel file named DEMO.XLS.

To read these data, click on Procs/Import/Read Text-Lotus-Excel …, which opens the file

open dialog.

Locate the DEMO.XLS file and double click on the file name. EViews will open the

Excel spreadsheet import dialog:

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The default settings for order of data, upper-left data cell, and the sample to import should

be appropriate for this Excel file since the names of the series are in the first row of the Excel

file, you simply enter the number of series, 4, in the Names for series or Number of series if

name in file field of the dialog box. Click OK and EViews will import the four series. These

series will appear as icons

Verifying the Data

The first you should do is to verify that the data have been read correctly. We will create

a group object that allows us to examine all four series. Click on the name GDP in the workfile

window, and then press CTRL and click on M1, PR, and RS. All four of the series should be

highlighted.

Now place the cursor anywhere in the highlighted area and double click the left mouse

button. EViews will open a pop-up menu providing you with several options:

Choose Open Group.

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EViews will create an untitled group object containing all four of the series.

You should compare the spreadsheet. View

with the top of the Excel worksheet to insure that the first part of the data has been read

correctly.

Once you are satisfied that the data are correct, you should save the workfile by clicking the

Save button in the workfile window. DEMO2.WF1 for file name. A saved workfile can be

opened later by selecting File/Open/Workfile … from the main menu.

Examining the Data

We can use basic EViews tools to examine the data in a variety of ways. For example, if

you select View/Multiple Graphs/Line from the group object toolbar, EViews displays line

graphs of each of the series.

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You can select View/Descriptive Stats/Individual Samples to compute descriptive

statistics for each of the series:

or click on View/Correlations to display the correlation matrix of the four series:

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We can also examine characteristics of the individual series. Since our regression analysis

below will be expressed in logarithms, we will work the log of M1. Select Quick/Show .. then

enter log (M1), and click OK.

Now select View/ Descriptive Statistics/ Histogram Stats from the series toolbar to display the

descriptive statistics for LOG(M1):

We can construct a smoothed version of the

histogram by selecting View/ Distribution Graphs / Kernel Density … and clicking on OK to

accept the default options:

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Estimating a Regression Model

We now estimate a regression model for M1 using data over the period from 1952:1

1992:4 and use this estimated regression to construct forecasts over the period 1993:1-2003:4.

The model specification is

ttttt PRRSGDPM )log()log()1log( 4321

where log(M1) is the logarith of the money supply, log(GDP) is the log of income, RS is

the short term interest rate, and log(PR) is the log first difference of the price level (the

approximate rate of inflation).

To estimate the model, we will create an equation object, Select Quick from the main

menu and choose Estimate Equation … to open the estimation dialog. Enter the following

equation specification:

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Here we list the name of the dependent variable, followed by the names of each of the

regressors, separated by spaces. We use expressions involving log and dlog to represent the log

transformations of M1 and GDP, and the difference of the log transformation for PR. The built-

in series name C stands for the constant in the regression.

The dialog is initially set to estimate the equation using the LS-Least Squares method for

the Sample 1952:1 1996:4. You should change the Sample to 1952:1 1992:4 to estimate the

equation for the subsample of observations.

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Click OK to estimate the equation using least squares and to display the regression results:

Note that the equation is estimates from 1952:2 to 1992:4 since one observation is

dropped to account for the dlog difference term. The estimated coefficients are statistically

significant, with t-statistic values well in excess of 2. The overall regression fit, as measured by

the R2 statistic, indicates a very tight fit. You can select View/Actual, Fitted, Residual/Graph

in the equation toolbar to display a graph of the actual and fitted values for the dependent

variable, along with the residuals:

Specification and Hypothesis Tests

We can use the estimated equation to perform hypothesis that the coefficients of the

model. For example, to test the hypothesis that the coefficient on the price term is equal to 2,

we will perform a Wald test. First determine the coefficient of interest by selecting

View/Representations from the equation toolbar:

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Note that the coefficients are assigned in the order that the variables appear in the

specification so that the coefficient for the PR term is labeled C(4). To test the restriction on

C(4) you should select View/Coefficient Tests/Wald-Coefficient Restrictions …, and enter the

restriction c(4) =2. EViews will report the results of the Wald test:

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The low probability values indicate that the null hypothesis that C(4)=2 is strongly

rejected.

The Durbin-Watson statistic can be difficult to interpret. To perform a more general

Breusch-Godfrey test for serial correlation in the residuals, select View/Residual Tests/Serial

Correlation LM Test .. from the equation toolbar, and specify an order of serial correlation to

test against. Entering 1 yields a test against first-order serial correlation:

Working with Data

This chapter describes EViews’ powerful language for using expressions and generating

and manipulating the data in series and groups. We first describe the fundamental rules for

working with mathematical expressions in EViews, and then describe how to use these

expressions in working with series and group data.

Using Expressions

One of the most powerful features of EViews is the ability to use and to process

mathematical expressions. EViews contains an extensive library of built-in operators and

functions that allow you to perform complicated mathematical operations on your data with just

a few keystrokes. In addition to supporting standard mathematical and statistical operations,

EViews provides a number of

specialized functions for

automatically handling the leads, lags

and differences that are commonly

found in time series data.

An EViews expression is a

combination of numbers, series

names, functions, and mathematical

and relational operators. In practical

terms, you will use expressions to

describe all mathematical operations

involving EViews objects.

As in other programs, you can

use these expressions to calculate a new series from existing series, to describe a sample of

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observations, or to describe an equation for estimation or forecasting. However, EViews goes

far beyond this simple use of expressions by allowing you to use expressions virtually anywhere

you would use a series.

Operators

EViews expressions may include operators for the usual arithmetic operations. The

operators for addition (+), subtraction (-), multiplication (*), division (/) and raising to a power

(^) are used in standard fashion so that

5 + 6 * 7.0 / 3

7 + 3e-2 / 10.2345 + 6 * 10^2 + 3e3

3^2 - 9

are all valid expressions. Notice that explicit numerical values may be written in integer,

decimal, or scientific notation.

In the examples above, the first expression takes 5 and adds to it the product of 6 and 7.0

divided by 3 (5+14=19); the last expression takes 3 raised to the power 2 and subtracts 9 (9–9

= 0). These expressions use the standard order of evaluation as described below.

The “-” and “+” operators are also used as the unary minus (negation) and unary plus

operators. It follows that

2-2

-2+2

2+++++++++++++-2

2---2

all yield a value of 0.

EViews follows the usual order in evaluating expressions from left to right, with operator

precedence order (from highest precedence to lowest):

unary minus (-), unary plus (+)

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^

*, /

+, subtraction (-)

<, >, <=, >=, =

and, or

The last two sets of operators are used in logical expressions.

To enforce a particular order of evaluation, you can use parentheses. As in standard

mathematical analysis, terms which are enclosed in parentheses are treated as a subexpression

and evaluated first, from the innermost to the outermost set of parentheses. We strongly

recommend the use of parentheses when there is any possibility of ambiguity in your

expression.

To take some simple examples,

-1^2

evaluates to (–1)^2=1 since the unary minus is evaluated prior to the power operator.

-1 + -2 * 3 + 4

evaluates to –1 + –6 + 4 = –3. The unary minus is evaluated first, followed by the

multiplication, and finally the addition.

(-1 + -2) * (3 + 4)

evaluates to –3 * 7 = –21. The unary minuses are evaluated first, followed by the two

additions, and then the multiplication.

3 * ((2+3)*(7+4) + 3)

evaluates to 3 * (5*11 + 3) = 3 * 58 =174.

Working with Series

One of the primary uses of expressions is to generate new series from existing data or to

modify the values in an existing series. Used in combination with samples, expressions allow

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you to perform sophisticated transformations of your data, saving the results in new or existing

series objects.

To create or modify a series, select Quick/Generate Series… or click on the Genr button

on the workfile toolbar. EViews opens a window prompting you for additional information.

You should enter the assignment

statement in the upper edit box, and the

relevant sample period in the lower edit box.

The assignment statement is actually an

implicit loop over observations. Beginning

with the first observation in the sample,

EViews will evaluate the assignment

statement for each included observation.

There are several ways to specify an

assignment statement.

Basic Assignment

You can type the series name, followed by an equals sign and then an expression. For

every element of the sample, EViews will evaluate the expression on the right-hand side of the

equality, and assign the value to the destination series on the left-hand side, creating the series

if necessary.

For example, if there is no series named Y,

y = 2*x + 37*z

will first create the Y series and fill it with NAs. Then, for every observation in the current

sample, EViews will fill each element of the Y series with the value of the expression. If Y does

exist, EViews will only replace Y values in the current sample with the value of the expression.

All observations not in the sample will be unchanged.

One special form of assignment occurs when the right-hand side expression is a constant

expression:

y = 3

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y = 37 * 2 + 3

EViews will simply loop through all of the observations in the sample and assign the

value of the constant.

Using Samples

By modifying the sample of observations used in assignment, you can splice together

series using multiple Genr commands. For example, if we enter three Genr commands with

different samples, first,

Upper window: y = z

Lower window: @all if z<=1 and z>-1

followed by a Genr with

Upper window: y = -2 + 3*z

Lower window: @all if z>1

and finally,

Upper window: y = -.9 + .1*z

Lower window: @all if z<=-1

we can generate Y as a piecewise linear function of the series Z.

Note that while it is possible to perform these types of operations using loops and IF-

statements, we strongly urge you to use Genr and sample statements wherever possible since

the latter approach is much more efficient.

Basic Single Equation Analysis

Single equation regression is one of the most versatile and widely used statistical

techniques. Here we describe the use of basic regression techniques in EViews: specifying and

estimating a regression model, performing simple diagnostic analysis, and using your

estimation results in further analysis.

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For more advanced and specialized techniques, testing, and forecasting see: Weighted

Least Squares, Two-Stage Least Squares (TSLS), Nonlinear Least Squares, ARIMA/ARIMAX

Models, Generalized Method of Moments (GMM), GARCH models, and Qualitative and

Limited Dependent Variable Models (Binary, Ordered, Censored, and Count). These

techniques and models all build upon the basic ideas presented here.

Where appropriate, we will also provide you with specialized references for specific

topics.

Equation Objects

Single equation regression estimation in EViews is performed using the equation object.

To create an equation object in EViews: select Objects/New Object/Equation or Quick/Estimate

Equation… from the main menu, or simply type the keyword equation in the command window.

Next, you will specify your equation in the Equation Specification dialog box that

appears, and select an estimation method. Below, we provide details on specifying equations in

EViews. EViews will estimate the equation and display results in the equation window.

The estimation results are stored as part of the equation object so they can be accessed at

any time. Simply open the object to display the summary results, or to access EViews tools for

working with results from an equation object. For example, you can retrieve the sum-of-squares

from any equation, or you can use the estimated equation as part of a multi-equation model.

Specifying an Equation in EViews

When you create an equation object, the Equation Specification dialog box appears.

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You need to specify three things in this dialog: the equation specification, the estimation

method, and the sample to be used in estimation.

In the upper edit box, you can specify the equation: the dependent (left-hand side) and

independent (right-hand side) variables and the functional form. There are two basic ways of

specifying an equation: by list and by formula. The listing method is easier but may only be

used with unrestricted linear specifications; the formula method is more general and can be

used to specify nonlinear models or models with parametric restrictions.

Specifying an Equation by List

The simplest way to specify a linear equation is to provide a list of variables that you wish

to use in the equation. First, include the name of the dependent variable or expression, followed

by a list of explanatory variables. For example, to specify a linear consumption function, CS

regressed on a constant and INC, type the following in the upper field of the Equation

Specification dialog:

cs c inc

Note the presence of the series name C in the list of regressors. This is a built-in EViews

series that is used to specify a constant in a regression. EViews does not automatically include

a constant in a regression so you must explicitly list the constant (or its equivalent) as a

regressor. The internal series C does not appear in your workfile, and you may not use it outside

of specifying an equation. If you need a series of ones, you can generate a new series, or use

the number 1 as an auto-series.

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You may have noticed that there is a pre-defined object C in your workfile. This is the

default coefficient vector—when you specify an equation by listing variable names, EViews

stores the estimated coefficients in this vector, in the order of appearance in the list. In the

example above, the constant will be stored in C(1) and the coefficient on INC will be held in

C(2).

Lagged series may be included in statistical operations using the same notation as in

generating a new series with a formula—put the lag in parentheses after the name of the series.

For example the specification:

cs cs(-1) c inc

tells EViews to regress CS on its own lagged value, a constant, and INC. The coefficient

for lagged CS will be placed in C(1), the coefficient for the constant is C(2), and the coefficient

of INC is C(3).

You can include a consecutive range of lagged series by using the word to between the

lags. For example,

cs c cs(-1 to -4) inc

regresses CS on a constant, CS(-1), CS(-2), CS(-3), CS(-4), and INC. If you don't include

the first lag, it is taken to be zero. For example,

cs c inc(to -2) inc(-4)

regresses CS on a constant, INC, INC(-1), INC(-2), and INC(-4).

You may include auto-series in the list of variables. If the auto-series expressions contain

spaces they should be enclosed in parentheses. For example,

log(cs) c log(cs(-1)) ((inc+inc(-1)) / 2)

specifies a regression of the natural logarithm of CS on a constant, its own lagged value,

and a two period moving average of INC.

Typing the list of series may be cumbersome, especially if you are working with many

regressors. If you wish, EViews can create the specification list for you. First, highlight the

dependent variable in the workfile window by single clicking on the entry. Next, CTRL-click

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on each of the explanatory variables to highlight them as well. When you are done selecting all

of your variables, double click on any of the highlighted series, and select Open/Equation…

The Equation Specification dialog box should appear with the names entered in the

specification field. The constant C is automatically included in this list; you must delete the C

if you do not wish to include the constant.

Specifying an Equation by Formula

You will need to specify your equation using a formula when the list method is not general

enough for your specification. Many, but not all, estimation methods allow you to specify your

equation using a formula.

An equation formula in EViews is a mathematical expression involving regressors and

coefficients. To specify an equation using a formula, simply enter the expression in the dialog

in place of the list of variables. EViews will add an implicit additive disturbance to this equation

and will estimate the parameters of the model using least squares.

When you specify an equation by list, EViews converts this into an equivalent equation

formula. For example, the list,

log(cs) c log(cs(-1)) log(inc)

is interpreted by EViews as,

log(cs) = c(1) + c(2)*log(cs(-1)) + c(3)*log(inc)

Equations do not have to have a dependent variable followed by an equal sign and then

an expression. The = sign can be anywhere in the formula, as in:

log(urate) + c(1)*dmr = c(2)

The residuals for this equation are given by:

e=log(urate)c(1)dmr-c(2)

EViews will minimize the sum-of-squares of these residuals.

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If you wish, you can specify an equation as a simple expression, without a dependent

variable and an equal sign. If there is no equal sign, EViews assumes that the entire expression

is the disturbance term. For example, if you specify an equation as

c(1)*x + c(2)*y + 4*z

EViews will find the coefficient values that minimize the sum of squares of the given

expression, in this case (C(1)*X+C(2)*Y+4*Z). While EViews will estimate an expression of

this type, since there is no dependent variable, some regression statistics (e.g. R-squared) are

not reported and the equation cannot be used for forecasting. This restriction also holds for any

equation that includes coefficients to the left of the equal sign. For example, if you specify,

x + c(1)*y = c(2)*z

EViews finds the values of C(1) and C(2) that minimize the sum of squares of

(X+C(1)*Y–C(2)*Z). The estimated coefficients will be identical to those from an equation

specified using:

x = -c(1)*y + c(2)*z

but some regression statistics are not reported.

The two most common motivations for specifying your equation by formula are to

estimate restricted and nonlinear models. For example, suppose that you wish to constrain the

coefficients on the lags on the variable X to sum to one. Solving out for the coefficient

restriction leads to the following linear model with parameter restrictions:

y = c(1) + c(2)*x + c(3)*x(-1) + c(4)*x(-2) + (1-c(2)-c(3)-c(4))*x(-3)

To estimate a nonlinear model, simply enter the nonlinear formula. EViews will

automatically detect the nonlinearity and estimate the model using nonlinear least squares. For

details, see Nonlinear Least Squares.

You can elect to use a different coefficient vector. To create a new coefficient vector,

choose Objects/New Object… and select Matrix-Vector-Coef from the main menu, type in a

name for the coefficient vector, and click OK. In the New Matrix dialog box that appears, select

Coefficient Vector and specify how many rows there should be in the vector. The object will

be listed in the workfile directory with the coefficient vector icon (the little ).

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You may then use this coefficient vector in your specification. For example, suppose you

created coefficient vectors A and BETA, each with a single row. Then you can specify your

equation using the new coefficients in place of C:

log(cs) = a(1) + beta(1)*log(cs(-1))

Estimating an Equation in EViews

Estimation Methods

Having specified your equation, you now need to choose an estimation method. Click on

the Method: entry in the dialog and you will see the drop-down menu listing estimation

methods.

Standard, single-equation regression is performed using the LS-Least Squares method.

Equations estimated by ordinary least squares and two-stage least squares, GMM, and

ARCH can be specified with a formula. Nonlinear equations are not allowed with binary,

ordered, censored, and count models, or in equations with ARMA terms.

Estimation Sample

You should also specify the sample to be used in estimation. EViews will fill out the

dialog with the current workfile sample, but you can change the sample for purposes of

estimation by entering your sample string or object in the edit box (see Sample for details).

Changing the estimation sample does not affect the current workfile sample.

If any of the series used in estimation contain missing data, EViews will temporarily

adjust the estimation sample of observations to exclude those observations (listwise exclusion).

EViews notifies you that it has adjusted the sample by reporting the actual sample used in the

estimation results:

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Dependent Variable: Y

Method: Least Squares

Date: 08/19/97 Time: 10:24

Sample(adjusted): 1959:01 1989:12

Included observations: 340

Excluded observations: 32 after adjusting endpoints

Here we see the top of an equation output view. EViews reports that it has adjusted the

sample. Out of the 372 observations in the period 59.01–89.12, EViews uses the 340

observations with observations for all of the relevant variables.

You should be aware that if you include lagged variables in a regression, the degree of

sample adjustment will differ depending on whether data for the pre-sample period are available

or not. For example, suppose you have nonmissing data for the two series M1 and IP over the

period 59.01–89.12 and specify the regression as

m1 c ip ip(-1) ip(-2) ip(-3)

If you set the estimation sample to the period 59.01–89.12, EViews adjusts the sample to:

Dependent Variable: M1

Method: Least Squares

Date: 08/19/97 Time: 10:49

Sample: 1960:01 1989:12

Included observations: 360

since data for IP(–3) are not available until 59.04. However, if you set the estimation

sample to the period 60.01–89.12, EViews will not make any adjustment to the sample since

all values of IP(–3) are available during the estimation sample.

Some operations, most notably estimation with MA terms and ARCH, do not allow

missing observations in the middle of the sample. When executing these procedures, an error

message is displayed and execution is halted if an NA is encountered in the middle of the

sample. EViews handles missing data at the very start or the very end of the sample range by

adjusting the sample endpoints and proceeding with the estimation procedure.

~ 30 ~

Estimation Options

EViews provides a number of estimation options. These options allow you to weight the

estimating equation, to compute heteroskedasticity and auto-correlation robust covariances, and

to control various features of your estimation algorithm.

Equation Output

When you click OK in the Equation Specification dialog, EViews displays the equation

window displaying the estimation output view:

Using matrix notation, the standard regression may be written as:

y=X

where y is a T-dimensional vector containing observations on the dependent variable, X

is a Tk matrix of independent variables, is a k-vector of coefficients, and is a T-vector of

disturbances. T is the number of observations and k is the number of right-hand side regressors.

In the output above, y is log(M1), X consists of three variables C, log(IP), and TB3, where

T=372 and k=3.

~ 31 ~

Coefficient Results

Regression Coefficients

The column labeled “Coefficient” depicts the estimated coefficients. The least squares

regression coefficients b are computed by the standard OLS formula

b= (X’X)-1X’y

If your equation is specified by list, the coefficients will be labeled in the “Variable”

column with the name of the corresponding regressor; if your equation is specified by formula,

EViews lists the actual coefficients, C(1), C(2), etc.

For the simple linear models considered here, the coefficient measures the marginal

contribution of the independent variable to the dependent variable, holding all other variables

fixed. If present, the coefficient of the C is the constant or intercept in the regression—it is the

base level of the prediction when all of the other independent variables are zero. The other

coefficients are interpreted as the slope of the relation between the corresponding independent

variable and the dependent variable, assuming all other variables do not change.

Standard Errors

The “Std. Error” column reports the estimated standard errors of the coefficient estimates.

The standard errors measure the statistical reliability of the coefficient estimates—the larger

the standard errors, the more statistical noise in the estimates. If the errors are normally

distributed, there are about 2 chances in 3 that the true regression coefficient lies within one

standard error of the reported coefficient, and 95 chances out of 100 that it lies within two

standard errors.

The covariance matrix of the estimated coefficients is computed as,

XbykTsXXs

),/(' ,)( 21'2

and the standard errors of the estimated coefficients are the square roots of the diagonal

elements of this matrix. You can view the whole covariance matrix by choosing

View/Covariance Matrix.

~ 32 ~

t-Statistics

The t-statistic, which is computed as the ratio of an estimated coefficient to its standard

error, is used to test the hypothesis that a coefficient is equal to zero. To interpret the t-statistic,

you should examine the probability of observing the t-statistic given that the coefficient is equal

to zero. This probability computation is described below.

Probability

The last column of the output shows the probability of drawing a t-statistic as extreme as

the one actually observed, under the assumption that the errors are normally distributed, or that

the estimated coefficients are asymptotically normally distributed.

This probability is also known as the p-value or the marginal significance level. Given a

p-value, you can tell at a glance if you reject or accept the hypothesis that the true coefficient

is zero against a two-sided alternative that it differs from zero. For example, if you are

performing the test at the 5% significance level, a p-value lower than .05 is taken as evidence

to reject the null hypothesis of a zero coefficient. If you want to conduct a one-sided test, the

appropriate probability is one-half that reported by EViews.

For the above example output, the hypothesis that the coefficient on TB3 is zero is

rejected at the 5% significance level but not at the 1% level. However, if theory suggests that

the coefficient on TB3 cannot be positive, then a one-sided test will reject the zero null

hypothesis at the 1% level.

The p-values are computed from a t-distribution with T-k degrees of freedom.

~ 33 ~

Summary Statistics

R-squared

The R-squared (R2) statistic measures the success of the regression in predicting the

values of the dependent variable within the sample. R2 is the fraction of the variance of the

dependent variable explained by the independent variables. The statistic will equal one if the

regression fits perfectly, and zero if it fits no better than the simple mean of the dependent

variable. It can be negative if the regression does not have an intercept or constant, or if the

estimation method is two-stage least squares.

EViews computes the (centered) R2 as

/T ,ˆ ,)()'(

ˆ'ˆ1

1

2

T

iiyyXby

yyyyR

where ̂ is the residual and y is the mean of the dependent (left-hand) variable.

Adjusted R-squared

One problem with using R2 as a measure of goodness of fit is that the R2 will never

decrease as you add more regressors. In the extreme case, you can always obtain an R2 of one

if you include as many independent regressors as there are sample observations.

The adjusted R2, commonly denoted as 2R , penalizes the R2 for the addition of regressors

which do not contribute to the explanatory power of the model. The adjusted R2 is computed as

kT

TRR

1)1(1 22

The 2R is never larger than the R2, can decrease as you add regressors, and for poorly

fitting models, may be negative.

Standard Error of the Regression (S.E. of regression)

The standard error of the regression is a summary measure based on the estimated

variance of the residuals. The standard error of the regression is computed as:

XbykTs ˆ ,)/(ˆ'ˆ

~ 34 ~

Sum of Squared Residuals

The sum of squared residuals can be used in a variety of statistical calculations, and is

presented separately for your convenience:

T

iii bXy

1

2)(ˆ'ˆ

Mean and Standard Deviation (S.D.) of the Dependent Variable

The mean and standard deviation of y are computed using the standard formulas:

T

t iy

T

ii TyysTyy

1

22

1

)1/()( , /

F-Statistic and Probability

The F-statistic tests the hypothesis that all of the slope coefficients (excluding the

constant, or intercept) in a regression are zero. For ordinary least squares models, the F-statistic

is computed as

))(1(

)1(2

2

kTR

kRF

Under the null hypothesis with normally distributed errors, this statistic has an F-

distribution with k-1 numerator degrees of freedom and T-k denominator degrees of freedom.

The p-value given just below the F-statistic, denoted Prob(F-statistic), is the marginal

significance level of the F-test. If the p-value is less than the significance level you are testing,

say .05, you reject the null hypothesis that all slope coefficients are equal to zero. For the

example above, the p-value is essentially zero, so we reject the null hypothesis that all of the

regression coefficients are zero. Note that the F-test is a joint test so that even if all the t-statistics

are insignificant, the F-statistic can be highly significant.

Working With Regression Statistics

The regression statistics reported in the estimation output view are stored with the

equation and are accessible through special @-functions. You can retrieve any of these statistics

~ 35 ~

for further analysis by using these functions in genr, scalar, or matrix expressions. If a particular

statistic is not computed for a given estimation method, the function will return an NA.

There are two kinds of @-functions: those that return a scalar value, and those that return

matrices or vectors.

Functions that return scalar values:

@r2 R-squared statistic

@rbar2 adjusted R-squared statistic

@se standard error of the regression

@ssr sum of squared residuals

@dw Durbin-Watson statistic

@f F-statistic

@logl value of the log-likelihood function

@aic Akaike information criterion

@sc Schwarz information criterion

@jstat scalar containing the J-statistic (for GMM)

@regobs number of observations in regression

@meandep mean of the dependent variable

@sddep standard deviation of the dependent variable

@ncoef total number of estimated coefficients

@coefs(i) coefficient i, where i is given by the order in which the

coefficients appear in the representations view

@stderrs(i) standard error for coefficient i

@tstats(i) t-statistic value for coefficient i

@cov(i,j) covariance of coefficients i and j

~ 36 ~

Functions that return vector or matrix objects:

@coefs vector of coefficient values

@stderrs vector of standard errors for the coefficients

@tstats vector of ratios of coefficients to standard errors

@cov matrix containing the coefficient covariance matrix

If you use these functions without reference to an equation object, EViews will use the

default equation. For example, the command:

series y = @dw

creates a series named Y and assigns to every observation, the value of the Durbin-Watson

statistic for the default equation.

We strongly recommend, however, to precede the statistic with the name of an equation

object and a period. This accesses the appropriate statistic for the named equation. For example:

series y = eq1.@dw

assigns to Y the value of the Durbin-Watson for the equation EQ1.

Functions that return a vector or matrix object should be assigned to the corresponding

object type. For example, you should assign the @tstats to a vector:

vector tstats = eq1.@tstats

and the covariance matrix to a matrix:

matrix mycov = eq1.@cov

You can also access individual elements of these statistics:

scalar pvalue = 1-@cnorm(@abs(eq1.@tstats(4)))

scalar var1 = eq1.@covariance(1,1)

For details on vectors and matrices, see Working with Matrices.

~ 37 ~

Working with Equations

Views of an Equation

Representations. Displays the equation in three forms: EViews command form, as

an algebraic equation with symbolic coefficients, and as an equation with the

estimated values of the coefficients.

You can cut-and-paste from the representations view into any application that supports

the Windows clipboard.

Estimation Output. Displays the equation output results described above.

Actual, Fitted, Residual. These views display the actual and fitted values of the

dependent variable and the residuals from the regression in tabular and graphical

form. Actual, Fitted, Residual Table displays these values in a table with a plot of

the residuals down the right side.

~ 38 ~

Note that the actual value is always the sum of the fitted value and the residual. Actual,

Fitted, Residual Graph displays a standard EViews graph of the actual values, fitted values, and

residuals. Residual Graph plots only the residuals, while the Standardized Residual Graph plots

the residuals divided by the estimated residual standard deviation.

Covariance Matrix. Displays the covariance matrix of the coefficient estimates as a

spreadsheet view. To save this covariance matrix as a matrix object, use the @cov

function.

Coefficient Tests, Residual Tests, and Stability Tests. These views are described in

detail in Specification and Diagnostic Tests.

Procedures of an Equation

Specify/Estimate… brings up the Equation Specification dialog box so that you can

modify your specification. You can edit the equation specification, or change the

estimation method or estimation sample.

Forecast… forecasts or fits values using the estimated equation. Forecasting using

equations is discussed in Forecasting.

Make Model creates an untitled model containing the estimated equation.

~ 39 ~

Update Coefs from Equation places the estimated coefficients of the equation in the

coefficient vector. You can use this procedure to initialize starting values for various

estimation procedures.

Make Regressor Group creates an untitled group comprised of all the variables used

in the equation (with the exception of the constant).

Make Residual Series… saves the residuals from the regression as a series in the

workfile. Depending on the estimation method, you may choose from three types of

residuals: ordinary, standardized, and generalized. For ordinary least squares, only

the ordinary residuals may be saved.

Default Equation

Following estimation, EViews often holds the estimated coefficients and their covariance

matrix, the residuals, and some summary statistics in an untitled equation object. These results

are available for use in a variety of subsequent computations, including Specification and

Diagnostic Tests, Forecasting, and Model Simulation. Untitled equations will not, however, be

saved with the workfile.

You may use the Name button on the equation toolbar to name your equation and place

it in the workfile. The equation will be saved with the workfile when the latter is saved. Once

named, you can access the information in the equation at any time, even if you have just

estimated several other models, or have not worked with the workfile in months.

For your convenience, EViews keeps track of a default equation. The default equation is

the equation that is active or was the most recently active equation. The name of the default

equation is shown at the upper right hand corner of the workfile window.

Residuals from an Equation

The residuals from the default equation are stored in a series object called RESID. RESID

may be used directly as if it were a regular series, except in estimation.

RESID will be overwritten whenever you estimate an equation and will contain the

residuals from the latest estimated equation. To save the residuals from a particular equation

for later analysis, you should save them in a different series so they are not overwritten by the

~ 40 ~

next estimation command. For example, you can copy the residuals into a regular EViews series

called RES1 by the command

series res1 = resid

Even if you have already overwritten the RESID series, you can always create the desired

series using EViews’ built-in procedures if you still have the equation object. If your equation

is named EQ1, open the equation window and select Procs/Make Residual Series, or enter

eq1.makeresid res1

to create the desired series.

Regression Statistics

You may refer to various regression statistics through the @-functions described above.

For example, to generate a new series equal to FIT plus twice the standard error from the last

regression, you can use the command

series plus = fit + 2*eq1.@se

To get the t-statistic for the second coefficient from equation EQ1, you could specify

eq1.@tstats(2)

To store the coefficient covariance matrix from EQ1 as a named symmetric matrix, you

can use the command

sym ccov1 = eq1.@cov

See Working with Regression Statistics for additional details.

Storing and Retrieving an Equation

As with other objects, equations may be stored to disk in data bank or database files. You

can also fetch equations from these files.

Equations may also be copied-and-pasted to, or from, workfiles or databases.

~ 41 ~

EViews even allows you to access equations directly from your databases or another

workfile. You can estimate an equation, store it in a database, and then use it to forecast in

several workfiles.

Using Estimated Coefficients

The coefficients of an equation are listed in the representations view. By default, EViews

will use the C coefficient vector when you specify an equation, but you may explicitly use other

coefficient vectors in defining your equation.

These stored coefficients may be used as scalars in generating data. While there are easier

ways of generating fitted values (see Forecasting), for purposes of illustration, note that the

command:

series cshat = eq1.c(1) + eq1.c(2)*gdp

forms the fitted value of CS, CSHAT, from the OLS regression coefficients and the

independent variables from the equation object EQ1.

Note that while EViews will accept a series generating equation which does not explicitly

refer to a named equation:

series cshat = c(1) + c(2)*gdp

we strongly recommend that you always use named equations to identify the appropriate

coefficients. In general, the coefficient vector C will contain the correct coefficient values only

immediately following estimation or a coefficient update. Using a named equation, or selecting

Procs/Update coefs from equation, guarantees that you are using the correct coefficient values.

An alternative to referring to the coefficient vector is to reference the @coefs elements of

your equation. For example, the examples above may be written as

series cshat=eq1.@coefs(1)+eq1.@coefs(2)*gdp

EViews assigns an index to each coefficient in the order that it appears in the

representations view. Thus, if you estimate the equation

equation eq01.ls y=c(10)+b(5)*y(-1)+a(7)*inc

~ 42 ~

where B and A are also coefficient vectors, then

· eq01.@coefs(1) contains C(10)

· eq01.@coefs(2) contains B(5)

· eq01.@coefs(3) contains A(7)

This method should prove useful in matching coefficients to standard errors derived from

the @stderrs elements of the equation. The @coefs elements allow you to refer to both the

coefficients and the standard errors using a common index.

If you have used an alternative named coefficient vector in specifying your equation, you

can also access the coefficient vector directly. For example, if you have used a coefficient vector

named BETA, you can generate the fitted values by issuing the commands

equation eq02.ls cs=beta(1)+beta(2)*gdp

series cshat=beta(1)+beta(2)*gdp

where BETA is a coefficient vector. Again, however, we recommend that you use the

@coefs elements to refer to the coefficients of EQ02. Alternatively, you can update the

coefficients in BETA prior to use by selecting Procs/Update coefs from equation from the

equation window. Note that EViews does not allow you to refer to the named equation

coefficients eq02.beta(1) and eq02.beta(2). You must use the expressions, eq02.@coefs(1) and

eq02.@coefs(2), instead.

Estimation Problems

Exact Collinearity

If the regressors are very highly collinear, EViews may encounter difficulty in computing

the regression estimates. In such cases, EViews will issue an error message “Near singular

matrix.” When you get this error message, you should check to see whether the regressors are

exactly collinear. The regressors are exactly collinear if one regressor can be written as a linear

combination of the other regressors. Under exact collinearity, the regressor matrix X does not

have full column rank and the OLS estimator cannot be computed.

~ 43 ~

You should watch out for exact collinearity when you are using dummy variables in your

regression. A set of mutually exclusive dummy variables and the constant term are exactly

collinear. For example, suppose you have quarterly data and you try to run a regression with

the specification

y c x @seas(1) @seas(2) @seas(3) @seas(4)

EViews will return a “Near singular matrix” error message since the constant and the four

quarterly dummy variables are exactly collinear through the relation:

c = @seas(1) + @seas(2) + @seas(3) + @seas(4)

In this case, simply drop either the constant term or one of the dummy variables.

~ 44 ~

Function Reference for EViews

Operators

All of the operators described below may be used in expressions involving series and

scalar values. When applied to a series expression, the operation is performed for each

observation in the current sample. The precedence of evaluation is listed in Operator

Precedence. Note that you can enforce order-of-evaluation using appropriate parentheses.

Expression Operator Description

+ add x+y adds the contents of X and Y

– subtract x–y subtracts the contents of Y from X

* multiply x*y multiplies the contents of X by Y

/ divide x/y divides the contents of X by Y

^ raise to the power x^y raises X to the power of Y

> greater than x>y takes the value 1 if X exceeds Y, and 0

otherwise

< less than x<y takes the value 1 if Y exceeds X, and 0

otherwise

= equal to x=y takes the value 1 if X and Y are equal,

and 0 otherwise

<> not equal to x<>y takes the value 1 if X and Y are not

equal, and 0 if they are equal

<= less than or equal to x<=y takes the value 1 if X does not exceed

Y, and 0 otherwise

>= greater than or equal to x>=y takes the value 1 if Y does not exceed

X, and 0 otherwise

and logical and x and y takes the value 1 if both X and Y are

nonzero, and 0 otherwise

or logical or x or y takes the value 1 if either X or Y is

nonzero, and 0 otherwise

Date and Observation Functions

These series functions allow you to identify the period associated with a given

observation, or the value associated with a given date/observation. For example, if you create

the series

series y = @month

~ 45 ~

with a monthly workfile, Y will contain a numeric indicator for each month (1 through

12). If you create the series

series z = @quarter

for the same workfile, EViews will fill Z with the numeric quarter indicator (1 through 4)

associated with each observation.

These functions are not available in workfiles with a lower frequency. For example, you

can use the @month function in a daily or monthly workfile but not in a quarterly or annual

workfile. Similarly, you cannot use the @day function in a monthly or annual workfile.

Function Name Description

@day observation day for daily or weekly workfiles, returns the observation

day in the month for each observation.

@elem(x,d) element returns the value of the series X, at date (or

observation) d. d must be specified in double quotes

" " or using the @str function.

@month observation month returns the month of observation (for monthly, daily,

and weekly data) for each observation.

@quarter observation quarter returns the quarter of observation (except for annual,

semi-annual, and undated data) for each observation.

@year observation year returns the year associated with each observation

(except for undated data) for each observation.

Basic Mathematical Functions

The following functions are designed to perform basic mathematical operations. When

applied to a series, they return a value for every observation in the current sample. When applied

to a matrix object, they return a value for every element of the matrix object. The functions will

return NA values for observations where the input values are NAs, and for observations where

the input values are not valid. For example, the square-root function @sqrt, will return NA

values for all observations that are less than zero.

Note that the logarithmic functions are base-e (natural logarithms). To convert the natural

logarithm into , you should use the relationship: .

~ 46 ~

Function Name Examples/Description

@abs(x), abs(x) absolute value @abs(-3)=3; abs(2)=2

@ceiling(x) smallest integer not less

than

@ceiling(2.34)=3;

@ceiling(4)=4

@exp(x), exp(x) exponential, @exp(1)=2.71813

@fact(x) factorial, x! @fact(3)=6; @fact(0)=1

@factlog(x) natural logarithm of the

factorial,

@factlog(3)=1.7918;

@factlog(0)=0

@floor(x) largest integer not greater

than

@floor(1.23)=1;

@floor(3)=3

@inv(x) reciprocal, 1/x @inv(2)=0.5

@log(x), log(x) natural logarithm, @log(2)=0.693;

log(2.71813)=1

@mod(x,y) floating point remainder returns the remainder of x/y

with the same sign as x. If

y=0. the result is 0.

@nan(x,y) recode NAs in x to y returns x if x<>na, and y if

x=na

@recode(x,condition,z) recode by condition returns z if condition is true;

otherwise returns x (x and z

can be series or scalars)

@round(x) round to the nearest integer @round(-97.5)=–98;

@round(3.5)=4

@sqrt(x), sqr(x) square root @sqrt(9)=3; sqr(4)=2

Time Series Functions

The following functions facilitate working with time series data. Note that NAs will be

returned for observations for which lagged values are not available. For example, d(x) returns

a missing value for the first observation in the workfile, since the lagged value is not available.

~ 47 ~

Function Name Description

d(x) first difference , where L is the lag operator

d(x,n) n-th order difference

d(x,n,s) n-th order difference

with a seasonal

difference at s

dlog(x) first difference of the

logarithm

dlog(x,n) n-th order difference of

the logarithm

dlog(x,n,s) n-th order difference of

the logarithm with a

seasonal difference at s

@movav(x,n) n-period backward

moving average

@movav(x,3)=

@movsum(x,n) n-period backward

moving sum

@movsum(x,3)=

@pch(x) one-period percentage

change (in decimal)

@pcha(x) one-period percentage

change—annualized (in

decimal)

@pcha(x)=1 where n is the lag

associated with one-year (n=4 for

quarterly data, etc.)

@pchy(x) one-year percentage

change (in decimal)

, where n is the lag associated with

one-year (n=12 for annual data, etc.)

@seas(n) seasonal dummy returns 1 when the quarter or month

equals n and 0 otherwise

@trend, @trend(n) time trend returns a trend series, normalized to

0 in period n, where n is a date or

observation number; if n is omitted,

then the series is normalized at the

first observation in the workfile

~ 48 ~

Descriptive Statistics Functions

These functions compute descriptive statistics for the current sample, excluding missing

values if necessary. To determine the number of observations available for a given series, use

the @obs function. Note that where appropriate, EViews will perform casewise exclusion of

data with missing values. For example, @cov(x,y) and @cor(x,y) will use only those

observations for which data on both X and Y are available.

Function Name Description

@cor(x,y) correlation returns the correlation between X and Y

@cov(x,y) covariance returns the covariance between X and Y

@inner(x,y) inner product returns the inner product of X and Y

@obs(x) number of observations returns the number of non-missing

observations for X in the current sample

@mean(x) mean average of the values in X

@median(x) median computes the median of the X (uses the

average of middle two observations if the

number of observations is even)

@min(x) minimum minimum of the values in X

@max(x) maximum maximum of the values in X

@stdev(x) standard deviation square root of the unbiased sample

variance (division by n1)

@sum(x) sum returns the sum of X

@sumsq(x) sum-of-squares sum of the squares of X

@var(x) variance variance of the values in X (division by n)

~ 49 ~

Regression Statistics

Single Equation Regression Statistics

There are two kinds of @-functions: those that return a scalar value, and those that return

matrices or vectors. These functions may be used in generating new series; see Working with

Regression Statistics. See Systems of Equations Regression Statistics for statistics from Pool,

Sspace, System, VAR objects.

Functions that return a scalar:

Function Description

@coefs(i) coefficient i

@cov(I,j) covariance of coefficients i and j

@dw Durbin-Watson statistic

@f F-statistic

@jstat scalar containing the J-statistic (for GMM)

@logl value of the log-likelihood function

@meandep mean of the dependent variable

@ncoef total number of estimated coefficients

@r2 R-squared statistic

@rbar2 adjusted R-squared statistic

@regobs number of observations in regression

@schwarz Schwarz information criterion:

@sddep standard deviation of the dependent variable

@se standard error of the regression

@ssr sum of squared residuals

@stderrs(i) standard error for coefficient i

@tstats(i) t-statistic value for coefficient i

Functions that return vector or matrix objects:

@coefs vector of coefficient values

@stderrs vector of standard errors for the coefficients

@tstats vector of ratios of coefficients to standard errors

@cov matrix containing the coefficient covariance matrix

~ 50 ~

Systems of Equations Regression Statistics

These regression statistics functions apply to Pool, Sspace, System, and VAR objects.

See Single Equation Regression Statistics for statistics from a single equation.

Functions that return a scalar:

Function Description

@coefs(i) coefficient i (except for VARs)

@cov(i,j) covariance of coefficients i and j

@dw(j) Durbin-Watson statistic for equation j

@jstat scalar containing the J-statistic (for GMM)

@logl value of the log-likelihood function (where appropriate)

@meandep(j) mean of the dependent variable for equation j

@ncoef(j) number of coefficients in equation j

@ncross number of cross-sections in the pool (Pools only)

@ncrossest number of cross-sections used in last pool estimation (Pool only)

@neqn Number of equations in system

@r2(j) R-squared statistic for equation j

@rbar2(j) adjusted R-squared statistic for equation j

@regobs(j) number of observations in regression for equation j

@residcov(i,j) covariance of residuals from equation i with those in equation j in a VAR or

system object. @residcov must be preceded with a named object, e.g.

var1.@residcov(2,2)

@sddep(j) standard deviation of the dependent variable for equation j

@se(j) standard error of the regression for equation j

@ssr(j) sum of squared residuals for equation j

@stderrs(i) standard error for coefficient i (except for VARs)

@sysncoef total number of coefficients in the system

@tstats(i) t-statistic value for coefficient i (except for VARs)

Functions that return vector or matrix objects:

@coefs vector of coefficient values

@stderrs vector of standard errors for the coefficients

@tstats vector of ratios of coefficients to standard errors

@cov matrix containing the coefficient covariance matrix