2) According to VAR regression results given below, what can you say about the nature of causality (Use 5 % level for significance)? Comment on the causal relationship between Y and X by justification. Use Granger Causality methodology to determine the validity of a causal relationship. Also, comment on why the Granger Causality test is appropriate for the table below to investigate the causal relationship between Y and X. Dependent Variable: Y Variable Lag Y 1 Y 2 X 1 X 2 0.083 Test for joint significance, Dependent Variable: Y Variable Significance Level 0.002 0.123 Y X Significance Level 0.002 0.009 0.012 Dependent Variable: X Variable Lag Significance Level X X Y Y 1 2 1 2 X Y Test for joint significance, Dependent Variable: X Variable Significance Level 0.012 0.056 0.087 0.045 0.046 0.458

2) According to VAR regression results given below, what can you say about the nature of causality (Use 5 % level for significance)? Comment on the causal relationship between Y and X by justification. Use Granger Causality methodology to determine the validity of a causal relationship. Also, comment on why the Granger Causality test is appropriate for the table below to investigate the causal relationship between Y and X. Dependent Variable: Y Variable Lag Y 1 Y 2 X 1 X 2 0.083 Test for joint significance, Dependent Variable: Y Variable Significance Level 0.002 0.123 Y X Significance Level 0.002 0.009 0.012 Dependent Variable: X Variable Lag Significance Level X X Y Y 1 2 1 2 X Y Test for joint significance, Dependent Variable: X Variable Significance Level 0.012 0.056 0.087 0.045 0.046 0.458

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

See similar textbooks

Similar questions

(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…
You are the owner of a restaurant located in a beach resort in Hawaii and want to use regression analysis to estimate the demand for your fresh seafood dinners. You have collected data on the daily quantity of seafood dinners sold over the last summer season. In order to correctly specify your regression equation, which of the following variables should be considered? Select one: A. the prices charged for souvenirs in local stores B. the prices charged for scuba diving excursions at the resort C. the wages paid to your chef and servers D. the daily number of vacationers at the resort
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. List 2 factors in your model that might be causing the Multicollinearity and give a reason
The data for this question is given in the file 1.Q1.xlsx(see image) and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.
Suppose you work for North Dakota DNR Grand Forks office. DNR would like to know whether they should set aside some conservation land, previously slated to be logged, for a potential state park. You are helping do a travel-cost analysis to estimate the benefits of the set-aside. You collected data from 500 visitors who came to a state park in a neighboring state. You ran regression analysis and controlled for these visitors' age, income, education, employment status, and other important factors that might affect the number of visits. With all the information, you have developed the following relationship: (a) (b) Cost to Visit $20 $40 $80 # of Visits Per Person Per Year 8 6 2 Graph the demand curve for the number of visits as a function of the "price" -- the travel cost. Based on demographic information about the people living in the vicinity of the proposed park, you have estimated that 10,000 people will take an average of 4 visits per year. For the average person, calculate: (1) The…
Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the estimated coefficients for each of the variables from part b? Be specific. Note Don't forget to include dummy variables.
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. what are the implications of Heteroscedasticity if this potential issue in your model?
q9-
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. List 2 factors in your model that might be causing the Heteroscedasticity and give a reason
b. The following model is a simplified version of the multiple regression model used by BEST Econometrics Group to study the trade off between time spent sleeping and working to look at other factors affecting sleep: sleep Bo + B₁totwrk + B₂educ + Page + μ₁ where sleep and totwork (total work) are measure in minutes per week and educ and age are measured in years. (i) If adults trade sleep for work, what is the sign of B₁? (ii) What signs do you think ₂ and 3 will have? (iii) Using the data in SLEEP75.RAW, the estimated equation is sleep = 3,638.25-.148totwrk-11.13educ - 2.20age, n = 706, R² = .113 If someone works five hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ (v) Would you say totwrk, educ, and age explain much of the variation in sleep? (vi) What other factors might affect the time spent sleeping? (vii) Are these likely to be correlated with totwrk?
Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity. Based on Addressing Heteroscedasticity list one test you would employ to test this potential issue?
7. The Stata data set "college_gpa" has data on students' college GPAS, high school GPAS, ACT scores, lectures skipped during the academic year, and other characteristics. We wish to examine the predictors of college GPAS. You must submit your do-file (commands). a) Regress college GPA on high school GPA and write the estimated regression line. b) Interpret the intercept of your regression in a sentence. Does this coefficient make sense?n Explain c) Interpret the coefficient on high school GPA in a sentence. Does this coefficient make sense? Explain. reg colGPA hsGPA Source df MS Number of obs 141 %3D F(1, 139) 33.80 Model 5.58478164 1 5.58478164 Prob > F e.0000 Residual 22.9668501 139 .165229138 R-squared Adj R-squared 0.1956 %3D 0.1898 %3D Total 28.5516318 140 .203940227 Root MSE .40648 colGPA Coef. Std. Err. t P>|t| [95% Conf. Interval] hsGPA .6242948 .1073816 5.81 0.000 .4119822 .8366073 _cons 890254 3669263 2.43 0.017 647755