Answered: How can we use Eviews to tell if a…

How can we use Eviews to tell if a regression suffers from first order autocorrelation and what are the consequences of autocorrelation on the OLS estimator?

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

See similar textbooks

Similar questions

A rural state wants to encourage high school graduates to continue their education and attend college. The state collected information on a random sample of high school seniors from across the state 7 years ago and is now observing how many years of education they completed. They believe students decide to achieve more education when they are more capable, have easier access to college education, and the opportunity cost of attending are lower. To explore the factors that affect the years of education completed they have used multiple regression to estimate the years of completed education as a function of: Unemployment rate - the unemployment rate in the county (3.9 – 16.8) County Hr Wage - average starting hourly manufacturing wage in the county Test - student score on college admission test (0 to 100 scale) Dist to college - Distance to near college (measured in 100’s of miles) Tuition - Tuition charged at nearest state university (measured in $1000s)…
A clothing manufacturer wants to estimate the amount of scrap cloth generated each day by its fabric cutting machines. Eight potential independent variables have been identified. These include the following. = amount of cloth run through cutting machines (in square feet) X2 = machine cutting speed (in feet per minute) age of machine (in years) The manufacturer selects 6 of the candidate independent variables to use in a multiple regression model for estimating y, the amount of scrap cloth (in square feet). Using data collected from 24 different cutting machines operating on different days, the model y = Bo+B1*1 +B,x2+ + Bax, is fit to the data. Fill in the blanks in the analysis of variance (ANOVA) table associated with this model. Do all ... calculations to at least three decimal places.
21
You estimated a single regression relating the annual bonus given to employees of big corporations to the age of those employees. Your simple regression shows a positive value for the slope coefficient. You are absolutely convinced that the true effect of age on bonus amount is negative, so you think you have an omitted variable bias. If you are correct, which of the following is true about the omitted variable? There is some (omitted) variable that positively affects bonus amount and that is positively correlated with age. There is some (omitted) variable that negatively affects bonus amount and that is completely uncorrelated with age. There is some (omitted) variable that positively affects bonus amount and that is negatively correlated with age. There is some (omitted) variable that positively affects bonus amount and that is completely uncorrelated with age.
A sample of 100 bears was treated like it was the whole population of Jellystone Park bears, and the mean weight was found to be 800 pounds with standard deviation 12 pounds, the mean blood pressure was 150 with standard deviation 6, and the sample correlation coefficient of weight with blood pressure was found to be .4. Based on this information, what is the regression slope for the SLR equation for predicting blood pressure using observed weight for Jellystone bears?
Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x percent of women using HRT and y breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper *Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. HRT Use Breast Cancer Incidence 46.30 103.30 40.60 105.00 39.50 100.00 36.60 93.80 30.00 83.50 (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) 9= (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women) would be…
When looking at your residual analysis and seeing all of the desirable graphs demonstrate a strong correlation between your independent and dependent variables, is this enough evidence to say that your independent variable makes a good predictor for your dependent variable? If yes, state why. If no, explain what other factors you need to consider.
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, yˆ=b0+b1x�^=�0+�1�. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.050.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperatures and Snow Accumulations Average Temperature (℉℉) 3939 2525 1515 4242 4242 2424 3232 2020 3030 3737 Snow Accumulation (in.in.) 66 1515 2929 66 1414 2626 2323 1212 1616 77 Copy Data Regression equation: yˆ=�^= Is the equation appropriate? Yes
To determine if the listing price of a house influences the selling price; a financial analyst sampled fifty houses and collected data on sale price, Y,(in $'000) and listed price,X,(in$'000) and fitted a regression model to the data. (a) Calculate the value of the test statistic if the regression sum of squares of fitted model was 25818.5 and error sum of squares was 1341 (round off answer to two decimal points)
please anwer whatever you are allowed too. Thank you. A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.68b=0.168 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -1.417 1.417 0.702 -0.702 (c) If a country increases its life expectancy, the happiness index will increase decrease (d) If the life expectancy is increased by 0.5 years in a certain country, how much will the happiness index change? Round to two decimal places.(e) Use the regression line to predict the happiness index of a country with a life expectancy of 69 years. Round to two decimal places.
In the following model, "employed" is a dummy indicating a person is employed: donation = B + B edu + Bemployed + uT Running this model will produce the same results of differential in donation between employed people and unemployed people as running two separate regressions for employed people and unemployed people. A. True B. False
A colleague of yours is completing a final report on the causes of the frequency of cyberbullying. In this report, she is asked to identify the causes that most strongly impacted the frequency of cyberbullying. She conducts an OLS regression. What statistic do you advise her to use in her discussion? Why?