20- If OLS is used in the presence of autocorrelation, which of the following will be likely consequences?* i) Coefficient estimates may be inconsistent ii) Hypothesis tests could reach the wrong conclusions iii) Coefficient estimates made from the model could be biased iv) Standard errors and t-statistics may be misleading O a. (ii) and (iv) only O b. (1) and (ii) only O c. (1), (i), and (iii) only O d. (1), (1), (ii) nd (iv) only

20- If OLS is used in the presence of autocorrelation, which of the following will be likely consequences?* i) Coefficient estimates may be inconsistent ii) Hypothesis tests could reach the wrong conclusions iii) Coefficient estimates made from the model could be biased iv) Standard errors and t-statistics may be misleading O a. (ii) and (iv) only O b. (1) and (ii) only O c. (1), (i), and (iii) only O d. (1), (1), (ii) nd (iv) only

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter5: Business And Economic Forecasting

Section: Chapter Questions

Problem 7E: The Questor Corporation has experienced the following sales pattern over a 10-year period: Compute...

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The Questor Corporation has experienced the following sales pattern over a 10-year period: Compute the equation of a trend line (similar to Equation 5.4) for these sales data to forecast sales for the next year. (Let 2004=0,2005=1, etc., for the time variable.) What does this equation forecast for sales in the year 2014? Use a first-order exponential smoothing model with a w of 0.9 to forecast sales for the year 2014.
Given the regression equationY = 43 + 10Xa. What is the change in Y when X changes by +8?b. What is the change in Y when X changes by -6?c. What is the predicted value of Y when X = 11? d. What is the predicted value of Y when X = 29? e. Does this equation prove that a change in X causes a change in Y?
Given the regression equationY = -50 + 12Xa. What is the change in Y when X changes by +3?b. What is the change in Y when X changes by -4?c. What is the predicted value of Y when X = 12?d. What is the predicted value of Y when X = 23?e. Does this equation prove that a change in X causes a change in Y?
Hello, please help me to solve the question (c) and (d) below.Consider this regression model (1) : Yt = β0 + β1 Ut + β2 Vt + β3 Wt + β4 Xt + εt ; where t= 1, ..., 75.We use OLS to estimate the parameters, producing the following model:Ŷt = 1.115 + 0.790 Ut − 0.327 Vt + 0.763 Wt + 0.456 Xt (0.405) (0.178) (0.088) (0.274) (0.017) Given that:R2 = 0.941; Durbin Watson stat DW = 1.907; RSS = 0.0757.(To answer the question, use the 5% level of significance, state clearly H0 and H1 that are tested, the test statistics that are used, and interpret the decisions.) (a) Describe the concepts of unbiasedness and efficiency. State the conditions required of regression (1) in order that the OLS estimators of the model parameters possess these properties. (b) Perform the following tests on the parameters of regression (1): (i) test whether the parameters β1, β2, β3 and β4 are individually statistically significant; (ii) test the overall significance of the regression model;…
In general, what is true about the relationship between the Sum of Squared Residuals in the restricted and unrestricted model? a. SSRr = R-squared * SSRur b. SSRr < SSRur c. SSRr > SSRur d. SSRr = SSRur
The Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985 Correlation matrix: X1 X2 X1 1 X2 0.950 1 Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?
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?
Consider the following multiple regression Price=118.9+0.594BDR+23.5Bath+0.195Hsize+0.004Lsize+0.095Age−48.5Poor, R2=0.75, SER=41.5 (22.7) (2.56) (8.56) (0.017) (0.00049) (0.315) (10.7) 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 . Suppose you wanted to test the hypothesis that BDR equals zero. That is, H0: BDR=0 vs H1: BDR≠0 Report the t-statistic for this test. The t-statistic is ________ (Round your response to three decimal places)
You estimated the following regression. What value would you predict for Y, if X = 47? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 324 -------------+---------------------------------- F(1, 322) = 354.54 Model | 3686788 1 3686788 Prob > F = 0.0000 Residual | 3348384.74 322 10398.7104 R-squared = 0.5241 -------------+---------------------------------- Adj R-squared = 0.5226 Total | 7035172.74 323 21780.7206 Root MSE = 101.97 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 24.51522 1.301971 18.83 0.000 21.95378 27.07666 _cons | 98.70791 117.4919 0.84 0.401…
DEPENDENT VARIABLE Qc R- SQUARE P- VALUE ON F 64 0.8093 0.0001 INDEPENDENTVARIABLE PARAMETER ESTIMATE STANDARD ERROR T-RATIO P-VALUE INTERCEPT 8.20 4.01 2.04 0.0461 PC -3.54 1.64 -2.16 0.0357 M 0.64287 0.19 3.38 0.0014 PA 0.7854 0.38 2.07 0.0439 10. Write the resulting regression equation. Q = f( P, M, PR) where Qc = demand for cement/month (in yards) Pc = the price of cement per yard, M = country’s tax revenues per capita, and PR = the price of asphalt per yard.
True or False? WLS is preferred to OLS when an important variable has been omitted from the model.
Please no written by hand The assumption of normally distributed errors means that... A. errors can be ignored when doing regression modelling. B. the OLS estimators can also be assumed to be normally distributed since they are a linear functions of the errors. C. the OLS estimators can also be assumed to be normally distributed since they are BLUE. D. the OLS estimators can also be assumed to be normally distributed since they are minimum variance. E. the regression model will not be subject to specification error.