OLS regression is used when our independent variables have scale or nominal level of measurement.
Nominal: In this level of measurement, numbers are used to categorize the data. For example, if gender is your variable, the responses will be male or female. A dichotomous nominal variable has only two categories.
Ordinal: Ordinal level variables have a meaningful order to them such as rank. For example there is an order to “drink size” (small, medium, large), however there is not a consistent distance between sizes.
Scale: Numeric variables that have equal intervals between each value, for example age.
OLS regression is done when your independment varaible is what level of measurement
explain when you want to use an IV regression instead of the OLS regression.
what are the problems that autocorrelation created when using OLS regression in time series data.
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.
Derive the OLS estimator \hat{β}₀ in the regression model yi=β₀+ui. Show all of the steps in your derivation.
Consider the following regression equation with the ususal assumptions of the Linear Regression Model. State whether the following are True or False. Give reasons for your answer.i) The OLS Sample regression equation passes through the point of sample means ii) The sum of the estimated () equals the sum of the observed ; or the sample mean of the estimated () equals the sample mean of the observed .iii) The OLS residuals (i = 1, …, N) are uncorrelated with...
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.
15. What are the properties of multiple regression model OLS estimators? If we have many different models, how can we choose between them?
What is the intuition behind the answer?
Imagine that we run the regression y; = Bo+B1xe and recover the OLS estimates of Bo and B1. If our regressions assumptions hold for the above specification. What is the relationship with the coefficients of the reverse regression x = ao + a1Yi + u;: (a) ao a CORRECT) - (b) @о — — Во = (с) dо — Во &1=-B1 (d) do — Во
Imagine that we run the regression y; =...
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)
Question 8 3 pts 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: spurious regression. omitted variable bias. multicollinearity. serial correlation.