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**Eviews Sample Assignment Solved by the Experts**

- How to interpret coefficients in the linear model if variables are

in logs? Presented By our EViews Assignment Experts.

How do you Interpreate the coefficients on dummies.

Models that are not linear in the variables can often be made to take a

linear form by applying a suitable transformation or manipulation.

For example, consider the following exponential regression model Yt = AXβt eut

it can be made into a classical linear regression model by logarithmic

transformation as yt = α + βxt + ut where α = ln(A), yt = ln Yt and xt = ln Xt

The coefficient estimates are interpreted as elasticities

(strictly, they are unit changes on a logarithmic scale). Thus a coefficient

estimate of β is interpreted as stating that ‘a rise in X of 1% will lead on

average, everything else being equal, to a rise in Y of β %’. Dummy variables are

qualitative variables because they are often used to numerically represent

a qualitative entity. Examples male = 0, female = 1. The coefficients on

the dummy variables can be interpreted as the average differences in the

values of the dependent variable for each category, given all of the other

factors in the model. Sample Question solved by All Homework Assignments Experts.

- OLS estimates are BLUE. Explain it. Does it affect the residuals in

your model? By Statistics Assignment Help Experts.

Estimators αˆ and βˆ determined by OLS will have a number of desirable properties,

and are known as Best Linear Unbiased Estimators (BLUE).

● ‘Estimator’ -- αˆ and βˆ are estimators of the true value of α and β

● ‘Linear’ -- αˆ and βˆ are linear estimators -- that means that the formulae

for αˆ and βˆ are linear combinations of the random variables (in this case, y)

● ‘Unbiased’ -- on average, the actual values of αˆ and βˆ will be equal to

their true values A brief overview of the classical linear regression model

● ‘Best’ -- means that the OLS estimator βˆ has minimum variance among the

class of linear unbiased estimators.

The primary property of OLS estimators is that they satisfy the criteria of

minimizing the sum of squared residuals. The observed values of X are uncorrelated

with the residuals. The sum of the residuals is zero. The sample mean of the

residuals is zero. The predicted values of y are uncorrelated with the residuals. This is how out Expert team id going to help you with your EViews Assignment Help.