LINEAR REGRESSION MODEL USING EVIEWS

LINEAR REGRESSION MODEL USING EVIEWS 1

ADESETE AHMED ADEFEMI

LINEAR REGRESSION MODEL USING EVIEWS

ADESETE, AHMED ADEFEMI

APRIL 2017



LINEAR REGRESSION MODEL USING EVIEWS

Econometrics may be defined as a social science which is concerned with the empirical

measurement and quantitative analysis of economic phenomena which is made possible with

the use of tools of statistics , mathematics and economic theory and decision taken with the

use of different tools of inferences. In other words, econometrics can also be defined as the

empirical analysis of several economic issues which is broken down with the use of an

econometric model and this model finally aids decision making in relation to economic

theory or otherwise depending on the inferences from the model. Econometrics can be

theoretical or applied. Econometrics in general deals with model building. This leads us to the

question that what is a model? And what are the assumptions guiding a model in

econometric? This paper aims to discuss the various assumption guiding a model and also the

violation of this assumptions and how this violations affect a model. Also solution to this

violations would be discussed. All this would be demonstrated using the Eviews 7.0.

“A model therefore, according to Advanced English Dictionary, is defined as a hypothetical

description of a complex entity or process”

Therefore this gives us a clearer picture of what a model is , an econometric model therefore

involves the hypothetical representation of reality into function based on economic theory.

An econometric model helps give a clearer picture of complex economic phenomena and also

aids decision making when subjected to econometric analysis .

A model has both mathematical representation and econometric representation . Let us

consider a simple linear regression model using the simple law of demand . From demand

theory, other factors held constant, there is a negative relationship between quantity demand



and price, that is, price is the only factor that influences demand and the higher the price of a

commodity, the lower the quantity demanded of that commodity and the lower the price of a

commodity, the higher the quantity demanded of that commodity. Therefore from this, let just

consider the demand for rice in Nigeria adopting the demand theory.

DEMAND FOR RICE

Q = f(P) -----------------------Mathematical representation(model)

Q = α + β(P) + ε ---------------------Econometric model

Q------------Dependent variable or regressand

P------------Explanatory variable or regressor

α------------Autonomous rice demand

ß------------Coefficient of price of rice

ε------------error term

ASSUMPTIONS OF LINEAR REGRESSION MODEL

Linearity: the model is a linear function of its parameter vector β or its explanatory

variable.

Normality: The error term from the regression function must be normally distributed

The mean of the error should be equal to zero

The error term from the regression function should not be serially correlated

The error term from the regression function should be homoscedastic

The model should be free from omitted variable bias

There should be no multicollinearity, that is, there should be no high correlation between

or among the explanatory variables



PRACTICAL EXAMPLE USING EVIEWS

Now let us consider a practical example :

From the earlier example on the demand for rice

Note: we would be making use of an annual data which range from 1970-2013

Please ensure you have eviews installed on your device.

STEP 1:

Get the data set ready in excel format which would look like this :

YEARS WRP RIM 1970 544.0899 1000 1971 456.8466 6000 1972 486.0543 11000 1973 937.5587 2000 1974 1373.979 4000 1975 816.7586 6000 1976 554.5917 94000 1977 551.4044 446000 1978 650.2489 789000 1979 527.9535 242000 1980 629.7379 394000 1981 702.9055 686000 1982 430.0942 666000 1983 416.3877 903000 1984 385.4647 629000 1985 329.9331 569000 1986 271.2938 462000 1987 285.2688 642000 1988 346.3487 344000 1989 375.6759 164000 1990 327.6236 224000 1991 358.0412 296000 1992 321.4414 440000 1993 272.7124 382000



1994 319.5891 300000 1995 349.1712 300000 1996 375.8236 350000 1997 353.2384 731000 1998 370.1532 900000 1999 308.2422 950000 2000 254.3951 1250000 2001 225.7056 1906000 2002 253.523 1897000 2003 248.2061 1448000 2004 279.5221 1369000 2005 326.4045 1650000 2006 339.0297 1500000 2007 342.0653 1800000 2008 632.2696 1750000 2009 575.3483 1750000 2010 488.9069 2400000 2011 498.4627 3200000 2012 523.2434 2800000 2013 476.9968 2800000



STEP TWO

Navigate to the eviews package installed on your device and open the package

The eviews environment looks like this :



STEP THREE

Create a new workfile by clicking File/New/Workfile on the toolbar of the Main window of

eviews

Then a workfile range appears which looks like this:



Click on OK after the date has been set and workfile name has been specified and this

would take us to the workfile environment which includes constant C and resid



STEP FOUR



Now, the workfile environment is ready which specifies that there are 44 observations , let us

load our data into eviews. There are different method of doing this , we can use the drag and

drop method , import method or the copy and paste method. Lets try the copy and paste

method.

Navigate to the microsoft excel file in which the data is located and open it

Copy the data

After the data has been copied , go and paste it in the eviews work environment

The workfile environment should appear like this:



STEP FIVE

The data has been successfully loaded , the next step here is to proceed to estimation of the

equation by navigating via Quick/Estimate equation that is by clicking on quick then

navigate to estimate equation .

You should have something like this :



In the box , model to be estimated should be typed including the constant , c

Note : log of the variables considered is what would be estimated

log(rim) c log(wrp)



Since we are using the ordinary least square method to estimate the above equation then there

is no need to change the method except other method would be considered , proceed to click

OK

Here is the result of the equation. The model above proves that from the conventional law of

demand , demand is a function of price other factors held constant . In other article how to

determine significance of factors , interpretation of the Adjusted R2 , F statistic would be

discussed.

LINEAR REGRESSION MODEL USING EVIEWS

Documents

Transcript of LINEAR REGRESSION MODEL USING EVIEWS