Match the following List 1 List 2 i. Dummy variables li. Dicky fuller test. ili. R2 iv. Durban Watson Test 1. Goodness of fit of regression 2. Serial Correlation 3. Qualitative effects 4. Stationary 1-4 i - 3 li - 2 iv - 1 O1-2 il -1 i -4 iv - 3 O1-3 il -4 i - 1 iv- 2 O1-1-2 li - 3 iv - 4
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- Without running any additional tests, what can you say about the joint significance of all independent variables in the model? Which statistic(s) helps you to answer this question?(Don't accept answers from Chat-GPT)You are estimating the following simple linear regression model: Edui = B0 + B1 MomEdu + ui. Where Edu is the years of schooling of an individual and MomEdu is the years of education of the individual's mother (Note: We might estimate this sort of regression to learn about intergenerational transmission of economic success.) a. Suppose you restrict your sample to individuals with MomEdui = 10 What happens to the OLS estimates? b. Suppose you have two random samples of size 100, both with the same In the first sample, half of the mothers have 12 yearsof education and half have 14 years of education. In the second sample, one quarter of of the mothers have each of 10, 12, 14. and 16 years of education. Does the variance of the OLS estimator differ between the two samples? Explain why or why not. C. Suppose you estimate the above regression using a random sample of 100 observations. Then you find another random sample of 100 with the same as the…ASAP Use the data in WAGE2.RAW for this exercise.(i) Consider the standard wage equationlog(wage) 5 b0 1 b1educ 1 b2exper 1 b3tenure 1 u.State the null hypothesis that another year of general workforce experience has the same effect on log(wage) as another year of tenure with the current employer.(ii) Test the null hypothesis in part (i) against a two-sided alternative, at the 5% significance level, by constructing a 95% confidence interval. What do you conclude?
- A) State whether the following statement(s) are true/false with justification i) If the model is linear in parameters, but there is non-linear relation between dependent and independent variables, OLS cannot be used. ii) OLS can be applied to estimate a multiple linear regression model, and each slope coefficient shows the full effect of an individual variable on the dependent variable iii) Both slope and intercept will change if dependent and independent variables are divided by factor k (=1000). B) In a left-tailed test where you reject Ho only in the lower tail, what is the p-value if Z = (-) 1.00?Investigate what factors determine the number of times a person logs into Facebook per week. It is argued that these four factors are important: number of friends, age in years, whether the person is employed, and whether the student has a Twitter account. That is: FACEBOOK LOGIN=f(FRIENDS,AGE,EMPLOYED,TWITTER) Do you think other relevant explanatory variables should also be included? Name any two such variables and explain why they should be included in the regression.MP believe that there is a causal relationship between earnings and years of schooling. They show you an estimate of the impact of years of schooling on annual earnings: There are two groups of individuals – group 1 and group 2. Group 1 on average earns £60,000 per year with the average level of post-compulsory education of 6 years, while group 2 earns on average £40,000 with the average post-compulsory education of 4 years. The MPs conclude that the annual return to an additional year of education is 25% using the following equation [(£60,000-£40,000)/40,000]/(6-4)=25%. Ignoring time preferences and discounting, and if both groups face the same cost of schooling, you need to explain to the MPs why this is not a good estimate of the annual return to an additional year of education. Propose methods to the MPs to deal with the problem
- Which of the following is a test of the statistical significance of particular regression coeffecient? a - t-test or b - {R2 or c - F-test or d - Durbin-Watson test.The results of Crum and Langer's hotel experiment: Select one: a. were contradicted by the results of correlational research based on a student survey about health, diet, and exercise and personality measures. b. were discounted because the evidence was purely correlational. c. had little impact because the statistical analysis was flawed. d. provided additional support for the idea that people's beliefs and expectations can have a powerful influence.We know that discrimination exists. It influences wages, but also many other dimensions over the life cycle which affect wages indirectly. I run OLS regression with variables wage, age, female and degree. The dependent variable is log(wage) and we replace the variables female and degree with the interaction term. However, discrimination is not included among the observed regressors. Given that omitting confounding variables from regression model can bias the coefficient estimates, omitting discrimination would lead to biased results. Could you please help me provide an example of how unobserved gender discrimination can affect my OLS estimates. [Hint: think about ways in which discrimination can invalidate OLS assumptions.].
- 1. Let kids denote the number of children ever born to a woman, and let educ denote years of education for the woman. A simple model relating fertility to years of education is kids = 0 + 1educ + u, where u is the unobserved error. (a) What kinds of factors are contained in u? Are these likely to be correlated with level of education? (b) Will a simple regression analysis uncover the ceteris paribus eect of education on fertility? Explain.Suppose we use the linear probability model to estimate the effect of household income on the willingness to avail a loan product. Given a regression equation: Willingness = 22.34 -0.04HouseholdIncome, with HouseholdIncome measured in hundreds of pesos. How is -0.04 interpreted?( TRUE OR FALSE help me find the true or false questions ) 1. In economic statistics and Econometrics, we do the same thing.( ) 2. As same in regression analysis, variables in relation analysis are all random variables.( ) 3. Known as residual, "i is an estimate of u , the random disturbance term.( ) 4. The slope coefficient of the log-log model measures the elasticity of Y with respect to X.( ) 5. In regression of standardized variables, the intercept term is always zero.( ) 6. The underlying theory may suggest a particular functional form.( ) 7. The disturbance term u is assumed to follow normal distribution.( ) 8. White test is used to check if there exists multicollinearity in the disturbance term of a regression function.( ) 9. Dummy variable can be used to test the stability of a regression model just as the function of the Chow Test.( ) 10. Where there is autocorrelation in the u , the OLS estimators are not BLUE estimators any more.( )