Using 5% significance as the guide, interpret the pair-wise Granger causality. The following are the EViews results of Pairwise Granger Causality tests.Table 3: Pairwise Granger Causality TestsSample: 2000Q1 2021Q4Lags: 2Null Hypothesis:LX1 does not Granger Cause LYLY does not Granger Cause LX1LX2 does not Granger Cause LYLY does not Granger Cause LX2LX3 does not Granger Cause LYLY does not Granger Cause LX3LX4 does not Granger Cause LYLY does not Granger Cause LX4LX2 does not Granger Cause LX1LX1 does not Granger Cause LX2LX3 does not Granger Cause LX1LX1 does not Granger Cause LX3LX4 does not Granger Cause LX1LX1 does not Granger Cause LX4LX3 does not Granger Cause LX2LX2 does not Granger Cause LX3LX4 does not Granger Cause LX2LX2 does not Granger Cause LX4LX4 does not Granger Cause LX3LX3 does not Granger Cause LX4Obs88888888888888888888F-Statistic4.113294.881061.041492.523203.461192.116785.041562.474480.889663.400366.635008.491616.624624.783472.110271.003782.024121.835413.654903.15128Prob.0.04130.02620.38060.11860.06240.16000.02390.12290.43440.06490.01030.00440.01040.02770.16080.39320.17170.19860.05500.0766

Given Data: *Significance = 5% *Other given values are F-statistics and probabilities.

Answered: Sample: 2000Q1 2021Q4 Lags: 2 Null…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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T or F A household is frequently used in analyses because more consistent data are usually collected at that level.
(Yi, X1i, X2i) satisfy the assumptions of the attachment. You are interestedin β1, the causal effect of X1 on Y. Suppose that X1 and X2 are uncorrelated.You estimate β1 by regressing Y onto X1 (so that X2 is not included in theregression). Does this estimator suffer from omitted variable bias? Explain.
The model initial setup is• The number of vacancies posted in the economy during a particular month is v = 100.• There were initially u = 225 unemployed workers at the beginning of the month.• Suppose that the average wage is 0.6 relative to their productivity. Unemployed workersreceive benefits that are half the wage, i.e., b = 0.3 relative to productivity.• Suppose that the number of matches between firms and workers are given by thefollowing matching functionsM = Au^0.5v^0.5• Suppose that 45 workers were able to find a job by the end of the month.Answer the following questions.a) What is the monthly job finding rate?b) What is the probability that an average vacancy is filled in a month?c) What is the job creation cost, k?d) What is the value of A?e) Explain how introducing a searching cost to this model would affect the queue length,the number of vacancy and unemployment rate, and wage.
You are trying to determine whether or not variables x2 and x3 jointly affect y. You set up a null and alternative hypothesis appropriate to this question. Before forming your test statistic, what are the two models you need to estimate? None of these is the correct pair of models to estimate. y = Bo + B1x1 + B2x2 + B3x3 + B4x4 + u and y = Bo + v y = Bo + B1x1 + B2x2 + B4x4 + w and y = Bo + B1x1 + B3X3 + B4X4 + e y = Bo + B1x1 + B2x2 + B3x3 + B4x4 + u and y = Bo + B1x1 + B4x4 + v
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19. The White test test statistic is n*R² ~x²(S-1). Where is the R² in the test statistic measured? A. The original econometric model when estimated using the White Robust Estimator B. The average from all the auxiliary regressions estimated with each explanatory variable as a function of the other explanatory variables C. The original econometric model before any test of heteroskedasticity has been performed D. The auxiliary regression of residuals as a function of the explanatory variables generating the heteroscedasticity 20. With annual times series data of 50 observations, how many observations are available to estimate the consumption econometric model CONt = Bo+ ß₁DISPYt + B₂CONt-1+ut A. 50 B. 47 C. 45 D. 49 21. Suppose the estimated simple linear demand equation for single-game tickets for a major football league is q=4.58-0.51p where q is the number of tickets sold measured in thousands and p is the dollar price of a ticket. Further assume that the point of the means_is…
"Why can’t I just test the individual coefficients one at a time"? is this approach is unreliable?
We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings. We use the following model to estimate the effect of a training program on annual earnings in 2018: ln⁡(earn2018)=β0+β1train+β2ln⁡(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education exper = the individual's years of experience We find: ln⁡(earn2018)^= 11.672 + 0.041train + 0.821ln⁡(earn2016) + 0.037educ + 0.012exper (5.864) (0.019) (0.258) (0.013) (0.007) n=1278, R2= 0.048 Which of the following is the correct interpretation of the…
explain the p value
The management of a large firm would like to know something about employee morale and the other variables to which it may be related. To study these relationships, each employee in a simple random sample of 27 employees is interviewed and tested to obtain information concerning morale level and the average number of satisfying extra work social contacts experienced during a week. Whether or not the duties the employee usually performs are appropriate to the job title is also determined. The dependent variable Y is measure of morale, the quantitative independent variable, X1 is average number of social contacts, and the following dummy variable coding is used to quantify the qualitative variable: X₂ = 1 if yes, X₂ = 0 if no. The model including dummy variable and the interaction term was being estimated and a part of computer outputs is below:
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