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overview TABLES MCO Question 6 2 5 Not yet In a model in which X, and X, have an influence on Y, where X, and

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overview TABLES MCO Question 6 2 5 Not yet In a model in which X, and X, have an influence on Y, where X, and X, are uncorrelated, answered omitting X, from the model will: 10 Marked out of 5.00 16 18 19 P Flag question Finish attempt ... Select one: Time left 1:18:33 O Result in biased and inconsistent estimators of the intercept, but the slope coefficient will be an unbiased and consistent estimator of b 9 bias the estimator, but this bias does disappear as the sample becomes large, not have any effect on the estimated parameters O result in correct estimation of the disturbance variance (o ) O result in biased and inconsistent estimators of the intercept as well as the slope coefficient Provin Next napa FINA202 Tut 2.2. docx FINA202 Tut 2.1,.docx PINAPOP Topic 1HIPPLC Ch13 31d Ed InvF.apptx FINA202 Student....x/sx Type here to search 0 e SWhich of the following are plausible approaches in dealing with a model that exhibits serious multicollinearity? (i) Use a different functional form (ii) Use suitably modified standard errors. (ill) Use a generalised least squares procedure on (iv) Use theory as the basis for this decision. ielect one: (1) and (iv) only (1), (11), (ili), and (iv) (1), (1i), and (iv) only (li) and (iv) only (1) and (ili) only Previous page Next page2020_MC_ECON QUANT_P2/2 QUIZ NAVI DECLARATIO 1 FORMULAE SHEET AND T- & F- Jump to... Overview QE TABLES MCQ 4 Question 4 What statement best describes the Principle of Parsimony? Not yet 10 11 answered Marked out of Select one: 16 17 18 19 5.00 O Simple models may be preferred because of the ease of interpretation Flag question O the estimated parameter values should be consistent with theory, Finish attempt ... O Complex models do a better job of capturing inherent variability. Time left 1:18:56 O Diagnosing a model's fit is a key part of the data analysis phase O Model selection procedures do not necessarily lead to the correct model. FINA202 Tut 2.2_.docx FINA202 Tut 2.1. dock FINA202 Topic 1 9Rik 13/ard Ed Invepptx FINA202 Student., Xisx Type here to search e S WE DELL F10 F11 F12 PriScr ESC F1 F2 F3 F4 F5 F6 F7 F8 F9English (en) > My courses :Theme colours % Quick links @ This course Home & My courses & 2020|ECON QUANTIPWIRIMO O General @ Test 3 2020_MC_ECON QUANT_P2/2 =QUIZ NAV DECLARATIO 1 FORMULAE SHEET AND T- & F Jump to. Overview QE TABLES MCQ Question 12 2 The Durbin-Watson test will be valid if; Not yet 9 10 11 answered Marked out of Select one. 16 17 18 5 00 The model regresses through the origin Flog question the values of the explanatory variables are not fixed in repeated sampling. Finish attempt its calculated value lies in the inconclusive region. the suspected autocorrelation takes the form of an AR(1). Time left 1:17:4 The model has lagged dependent variables as one of the explanatory variables FINA202 Tut 2.2...docx A FINA202 Tut 2.1...docx FINA202 Topic 1....pptx Ch13 3rd Ed InvF. pptx A FINA202 Student...xisx Type here to search e e SDECLARATIO 1 FORMULAE SHEET AND T- & F- jump to, Overview QE TABLES MCQ Question 3 Which of the following are plausible approaches indealing with a model that exhibits Not yet autocorrelation? 10 answered (i) Use the Durbin-Watson statistic Marked out of (ii) Use a different functional form 16 17 18 5.00 (iii) Use a generalised least squares procedure Flag question (iv) Add lagged values of the independent and dependent variables to the regression equation Finish attempt. Time left 1:19:02 Select one: e (1), (i1), (ili), and (iv) (1) and (ili) only (i), (i1), and (iii) only O (ii) and (iv ) only (ii), (ili), and (iv) only FINA202 Tut 2.2...docx FINA202 lut 2 1 dor FINABOP TOPIC L.PRIM his Bid Ed Inwt putx FINA202 Student..xisx Type here to search e S DELL F10 F11 F12 PrtScr ESC F1 F2 F3 F4 F6 F7 F8 FgHome @ My courses @ 2020|ECON QUANT PW|2|MC @ General @ Test 3 2020_MC_ECON QUANT P2/2 QUIZ NAVIG DECLARATION 1 FORMULAE SHEET AND T- & F- Jump to Overview QE TABLES MCO 2 3 Question 14 When the dependent variable in a model contains measurement error: Not yet 10 answered Marked out of Select one: 16 17 18 5.00 O the variances of the estimators are higher, but the coefficients are unbiased! P Flag question O the consequences are the same as those of omitting a relevant variable. Finish attempt ... O the error terms are correlated with the explanatory variables. Time left 1:17:38 None of the answers provided here are correct O the OLS estimators are biased. FINA202 Tut 2.2...docx FINA202 Tut 2.1,.docx FINA202 Topic 1....pptx A "Ch13_3rd Ed InvF....pptx FINA202 Student.xIsx O Type here to search e e SWhich of the following are plausible approaches in dealing with a model that exhibits serious multicollinearity? (i) Use a different functional form (il) Use suitably modified standard errors. (ill) Use a generalised least squares procedure on (iv) Use theory as the basis for this decision. ielect one: (1) and (iv) only (1). (11), (lli), and (Iv) (1). (i1), and (iv) only (i1) and (iv) only (1) and (ili) only Previous page Next page13 When the dependent variable in a model contains measurement error: it of Select one: None of the answers provided here are correct uestion the consequences are the same as those of omitting a relevant variable. the variances of the estimators are higher, but the coefficients are unbiased. the OLS estimators are biased. O the error terms are correlated with the explanatory variables.A good model should have the property of parsimony, which means that: Select one: The adjusted R" must be as high as possible in the model should be as simple as possible. the estimated parameter values should be consistent with theory. the estimated parameters should have unique values. predictions of the model should be borne out by actual experience.Since consumers do not change their consumption habits readily due to psycho technological, or institutional reasons, will be a problem in a con elect one: Autocorrelation. Heteroscedasticity. Model misspecification. Both autocorrelation and heteroskedasticity. O Multicollinearity

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