Assume that, using 65 observations of the time series x you estimate the AR(2) model,x=0.2+0.32.x,-+0.41x,2and you test for autocorrelation up to order 7 using an auxiliary regression of the fitted residuals. Your Breusch-Godfrey test for autocorrelation has avalue of 31.15, what is the R2 from your auxiliary regression correct to 3 significant figures?0.4940.4790.5560.537

From the given information, xt=0.2+0.32xt-1+0.41xt-2+ut There are 65 observations for the time…

Answered: Assume that, using 65 observations of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Suppose that a researcher, using data on class size (CS) and average test scores from 102 third-grade classes, estimates the OLS regression TestScore=515.196+ (-5.7618)x CS, R² = 0.06, SER=11.4. A classroom has 23 students. The regression's prediction for that classroom's average test score is (Round your response to two decimal places.) Last year a classroom had 20 students, and this year it has 24 students. The regression's prediction for the change in the classroom average test score is (Round your response to two decimal places.) The sample average class size across the 102 classrooms is 21.19. The sample average of the test scores across the 102 classrooms is (Hint: Review the formulas for the OLS estimators.) (Round your response to two decimal places.)
5. You estimated the following regression. Which of the following is the estimated regression line? Source | SS df MS Number of obs = 379 -------------+---------------------------------- F(1, 377) = 24045.16 Model | 401185353 1 401185353 Prob > F = 0.0000 Residual | 6290117.58 377 16684.662 R-squared = 0.9846 -------------+---------------------------------- Adj R-squared = 0.9845 Total | 407475471 378 1077977.44 Root MSE = 129.17 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 10.08326 .065026 155.07 0.000 9.955396 10.21111 _cons | 87.07298 7.855763 11.08 0.000 71.62637 102.5196…
Consider the following multiple regression Price = 118.8 + 0.554BDR+23.8Bath +0.144Hsize +0.005Lsize +0.104Age - 48.8Poor, R² = 0.79, SER = 41.1 (0.00046) (0.382) (10.9) (23.8) (2.69) (8.92) (0.016) The numbers in parentheses below each estimated coefficient are the estimated standard errors. A detailed description of the variables used in the data set is available here i Suppose you wanted to test the hypothesis that BDR equals zero. That is, Report the t-statistic for this test. Is the coefficient on BDR statistically different from zero at the 5% significance level? A. Yes. B. No. Ho: BDR=0 vs H₁: BDR#0 A. Yes. B. No. The t-statisticis 0.206 (Round your response to three decimal places) Typically five-bedroom houses sell for much more than two-bedroom houses. Is this consistent with your previous answer and with the regression more generally? A homeowner purchases 2391 square feet from and adjacent lot. Construct a 95% confidence interval for the change in the value of her house.…
Suppose that n = 50, i.i.d observations for (Y₁, X₁) yield the following regressions results: Ỹ= 49.2 + 73.9 X, SER = 13.4, R²=0.78 (23.5) (16.4) Another researcher is interested in the same regression, but makes an error when entering the data into a regression program: The research enters each observation twice, ending up with 100 observations (with observation 1 entered twice, observation 2 entered twice and so forth). Using these 100 observations, what results will be produced by the regression program? Complete the spaces in the equation below. Report the intercept and slope to one decimal place, but report the R2 to two decimal places. Ŷ = + * X, R² =
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. What is the subpopulation mean when x = 7 servings per day?
Suppose a data set of 200 observations (n = 200) was analyzed using OLS to examine the relationship between CEO salary and various measures of firm performance. The regression results are as follows, with standard errors in parentheses: logy = 5 + 0.2logx₁0.03x₂ +0.002x3 (0.2) (0.04) (0.004) where? (0.009) y = CEO salary in thousands of dollars X₁ = annual firm sales X₂ = percentage of sales lost to competitors X3 = return on stock in percent R² = 0.290 Suppose you want to test the null hypothesis that percentage of sales lost to competitors has no ceteris paribus effect on the salaries of CEOS. For the one-sided alternative hypothesis ß₂ < 0 and 1% rejection rule (i.e., at the 1% level), you would_ the null hypothesis that ß₂ = 0.
please answer correctly:
A regression of average monthly expenditure (AME, measured in euros) on average monthly income (AMI, measured in euros) using a random sample of college-educated full-time workers earnings €100 to €1.5 million yields the following: AME = 710.7 + 8.8 × AMI, R2 = 0.030, SER = 540.30 d. What does the regression predict will be the expenditure of a person with an income of €100? With an income of €200? e. Will the regression give reliable predictions for a person with an income of €2 million? Why or why not? ( f. Given what you know about the distribution of earnings, do you think it is plausible that the distribution of errors in the regression is normal? (Hint: Do you think that the distribution is symmetric or skewed? What is the smallest value of earnings, and is it consistent with a normal distribution?).
After studying the milk yields of a randomly selected sample of 900 cows, researchers felt that the presence of white spots (WS;) on a cow's body led to a different level of milk production (MP;). The researchers estimate the OLS regression: where WS, is a binary variable of the form: WS₁ = MP = 5.36 +0.26 x WS₁, (2.23) (0.06) 1, if the th cow has white spots 0, if the th cow doesn't have white spots The 95% confidence interval for the coefficient ₁ is (₁). (Round your answers to four decimal places.) Based on this interval, we will the hypothesis P₁ = 0 at the 5% significance level. Which of the following statements describes the way in which the coefficient of the indicator variable could be interpreted in this case? O A. The coefficient is the sum of the conditional expectation of the presence of white spots on a cow with respect to the average milk production. OB. The coefficient is the rate of change in the milk production due to a unit change in the number of cows with white…
If you have a b of 0.56 in a regression equation, what does this mean? For every one-unit increase in x, you get an increase of 0.56 in y r = .31 On average, the variability of real scores around the regression line is 0.56 For every 1 standard deviation increase in x, you get an increase of 0.56 standard deviations in y
With t test statistics and its p-value, given in the regression output, we test: O a. a joint hypothesis about significance of all parameters except constant O b. a single hypothesis about insignificance of R^2 O C. a single hypothesis about significance of parameter O d. a joint hypothesis about insignificance of all regression parameters
This table reports the regression coefficients when the returns of the size-institutionalownership portfolio (columns 1 and 2) returns are regressed on three variables: a constant(column 3), the stock market returns (column 4), and the change of the value weighted discountof the closed end fund industry (column 6). Columns 5 and 7 report the corresponding t-statistics of the coefficient estimates. Note that a t-statistic with an absolute value above 1.96means the coefficient estimate is significantly different from 0 at the 1% level. Column 8reports the R square of the regressions. Column 9 reports the mean institutional ownership ofeach portfolio. The last column reports the F-statistics for a multivariate test of the null hypothesis that the coefficient on ΔVWD in the Low (L) ownership portfolio is equal to theHigh (H) ownership portfolio. Two-tailed p-values are in parentheses. 1. What is the main finding of this Table? 2. What is the explanation for…

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