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Econometrics Midterm || with 100% Error-free Answers.
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  • Econometrics
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  • Econometrics

What is econometrics? correct answers Tools of economic theory, math, and statistical inference are applied to the analysis of economic phenomena. How do we do econometrics? What are the steps involved? correct answers 1. Make a hypothesis 2. Collect data 3. Specify the model 4. Estimate the ...

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  • September 10, 2024
  • 10
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers

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Econometrics Midterm || with 100% Error-free Answers.
What is econometrics? correct answers Tools of economic theory, math, and statistical inference
are applied to the analysis of economic phenomena.

How do we do econometrics? What are the steps involved? correct answers 1. Make a hypothesis
2. Collect data
3. Specify the model
4. Estimate the parameters
5. Check model specification - is the model good?
6. Test the hypothesis
7. Use the model to predict or forecast or answer questions

Simple linear model correct answers yi= B1 + B2Xi + ui
Bs are parameters/coefficients, X is regressor, u is error term

What is the error term? Why do we have an error term? correct answers omitted explanatory
variables,
aggregation of variables (consumption function)
model misspecification
may be determined by expectations rather than actual values
functional misspecification
x instead of x^2
measurement error

Linear correct answers linear in parameters:every term consists of a straightforward parameter
multiplied by a variable

linear in variables: every term consists of a straightforward variable multiplied by a parameter

when to reject the null correct answers reject: t-stat > t-critical
*don't forget to explain why: explanatory variable seen as significant at the % level
B2hat - t-crit*se(B2hat)< B2hat < B2hat+ t-crit*se(B2hat)

Method of OLS correct answers minimize the sum of the squared errors in order to have a very
fitted line

What is OLS? correct answers ordinary least squares
most popular application of regression analysis

What does OLS do? correct answers we sum the squared errors

Why do we use OLS? correct answers ◦ OLS is simple to do (compared to other regression
techniques)
◦ OLS has some nice properties - the regression line goes through the means
of X and Y and the sum of the residuals is zero (or very close to zero).

, ◦ OLS is the BEST estimator under a certain set of assumptions

How do we estimate the coefficients using OLS in a two variable model? correct answers
B2=sum(Xi-Xbar)(Yi-Ybar)/
sum(Xi-Xbar)^2

B1= Ybar - B2Xbar

- Be able to explain the intuition correct answers the goal is to minimize the sum of the squared
errors

Explanatory variable correct answers same as independent variable and regressor

Dependent variable correct answers Y or what is being explained by regressing data

Be able to draw a graph of a regression model and label all the parts (like the coefficients, the
error terms, etc) correct answers (look up in notes)

Causation and correlation correct answers Example:
A one dollar increase in income suggests a 51 cent increase in average
consumption
◦ Suggests or is correlated with (not causes)

Scaling and units and interpretation correct answers

Demeaning - what is it? Why do we do it? Interpret coefficient. correct answers We regress Y on
X* when X*=Xi-Xbar

gives us a sensible interpretation of the intercept by fitting Y around the sample mean of X.

example: if mean is 13.67 years of schooling then an individual at the mean is predicted to earn
$19.63.

Goodness of Fit correct answers The r2, the coefficient of determination, gives us an overall
measure of
the "goodness of fit" of the regression
◦ How well the regression line fits the actual Y values

Coefficient of determination correct answers R^2 predicts the overall measure of goodness of fit

R2 correct answers r2 measures the percentage of total variation in Y explained by the regression
model

TSS correct answers TSS = ESS + RSS

total sum of squares

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