explain when you want to use an IV regression instead of the OLS regression.
explain when you want to use an IV regression instead of the OLS regression.
Homework Answers
IV regression is used when we have endogenous variables in the model ( endogenous variables are variables that are influenced by variables not included in the model i.e. when explanatory variables are correlated with variables included in the error term) . Using an instrumental variable to identify the hidden correlation allows us to find the true correlation between the explanatory variable and dependent variable.
Putting simple, IV regression is used when we have endogeneity problem in our model.
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explain when you want to use an IV regression instead of the OLS
regression.
5. Discuss when you would use discriminant analysis instead of multiple regression analysis. Explain the difference...
5. Discuss when you would use discriminant analysis instead of multiple regression analysis. Explain the difference between metric and non-metric variables.
PART 2: Additional Problem 5. Derive the OLS slope estimate for the case when a regression...
PART 2: Additional Problem 5. Derive the OLS slope estimate for the case when a regression model does not contain a constant or intercept term. Show your work. Compare to the "correct" OLS slope coefficient estimate. Under what circumstances will the two be equal? Explain.
OLS regression is done when your independment varaible is what level of measurement
OLS regression is done when your independment varaible is what level of measurement
what are the problems that autocorrelation created when using OLS regression in time series data.
what are the problems that autocorrelation created when using OLS regression in time series data.
Suppose you want to estimate the model but, unfortunately, you don't observe Y. Instead, a mismeasured...
Suppose you want to estimate the model
but, unfortunately, you don't observe Y. Instead, a mismeasured
version of Y is available, . (e.g. people may not
report their true earnings, but their incomes with some error).
We assume that = Y + V where V is a
measurement error which satisfies
Cov (Y,V ) = 0
Cov (U,V ) = 0:
Given that Y is not observed, you go ahead an estimate the
regression
= 0 + 1X +
(a)...
Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very...
Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: multicollinearity. spurious regression. omitted variable bias. serial correlation.
7. When we impose a restriction on the OLS estimation that the intercept estimator is zero, we ca...
7. When we impose a restriction on the OLS estimation that the intercept estimator is zero, we call it regression through the origin. Consider a population model Y- Au + βίχ + u and we estimate an OLS regression model through the origin: Y-β¡XHi (note that the true intercept parameter Bo is not necessarily zero). (i) Under assumptions SLR.1-SLR.4, either use the method of moments or minimize the SSR to show that the βί-1-- ie1 (2) Find E(%) in terms...
shown with steps 2. () Explain what is meant by hetroscedasticity in a regression model Y...
shown with steps
2. () Explain what is meant by hetroscedasticity in a regression model Y Xp+e and causes a problem with inference in OLS (ii) In what type of data is heteroscedasticity most likely to be a problem: time series or cross- sectional data? How might you check for its presence? (5 marks) (ii) What is meant by Weighted Least Squares'? Explain how to use it in practice. (5 marks) (5 marks) (iv) Suppose you discover heteroscedasticity is a...
c) Which theorem gives th (a) State the OLS assumptions in a simple linear regression model....
c) Which theorem gives th (a) State the OLS assumptions in a simple linear regression model. (3) b] How do you modify the OLS assumptions if you have a control variable? (2) (c) Discuss the problem of omitted variable bias. (5)
2. Which of the following will cause OLS estimators for a regression Happiness = a +...
2. Which of the following will cause OLS estimators for a regression Happiness = a + B Income+u to be biased? a) Income varies a lot in the sample. b) Optimistic disposition is in the unobservable u, which is correlated with the Income. c) Omit the intercept when its true value is not zero. d) Both b) and c)
PART 2: Additional Problem 5. Derive the OLS slope estimate for the case when a regression model does not contain a constant or intercept term. Show your work. Compare to the "correct" OLS slope coefficient estimate. Under what circumstances will the two be equal? Explain.
Suppose you want to estimate the model
but, unfortunately, you don't observe Y. Instead, a mismeasured
version of Y is available, . (e.g. people may not
report their true earnings, but their incomes with some error).
We assume that = Y + V where V is a
measurement error which satisfies
Cov (Y,V ) = 0
Cov (U,V ) = 0:
Given that Y is not observed, you go ahead an estimate the
regression
= 0 + 1X +
(a)...
7. When we impose a restriction on the OLS estimation that the intercept estimator is zero, we call it regression through the origin. Consider a population model Y- Au + βίχ + u and we estimate an OLS regression model through the origin: Y-β¡XHi (note that the true intercept parameter Bo is not necessarily zero). (i) Under assumptions SLR.1-SLR.4, either use the method of moments or minimize the SSR to show that the βί-1-- ie1 (2) Find E(%) in terms...
shown with steps
2. () Explain what is meant by hetroscedasticity in a regression model Y Xp+e and causes a problem with inference in OLS (ii) In what type of data is heteroscedasticity most likely to be a problem: time series or cross- sectional data? How might you check for its presence? (5 marks) (ii) What is meant by Weighted Least Squares'? Explain how to use it in practice. (5 marks) (5 marks) (iv) Suppose you discover heteroscedasticity is a...
c) Which theorem gives th (a) State the OLS assumptions in a simple linear regression model. (3) b] How do you modify the OLS assumptions if you have a control variable? (2) (c) Discuss the problem of omitted variable bias. (5)
2. Which of the following will cause OLS estimators for a regression Happiness = a + B Income+u to be biased? a) Income varies a lot in the sample. b) Optimistic disposition is in the unobservable u, which is correlated with the Income. c) Omit the intercept when its true value is not zero. d) Both b) and c)
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