9. Conduct a hypothesis test for whether consumption and income are linearly related usinga = 0.01. The standard error of the slope is 0.144.Step 1: State the null and alternative hypotheses.Step 2: Choose the significance level.Step 3: Calculate the test statistic.Step 4: Find the critical value(s).Step 5: Compare the test statistic to the critical value(s).Step 6: State the conclusion.

Step 1: Null Hypothesis (H0): β1 = 0The slope of the linear relationship between consumption and…

Answered: 9. Conduct a hypothesis test for…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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1. When considering a Simple Linear Regression model, a. Describe a test that is performed to decide whether there is a statistically significant linear relationship between the dependent an independent variables? b. What are the hypotheses for the test? c. What assumptions does the test make? d. What is the formula for the test statistic used in the test? e. What is the consequence of failing to reject the null hypothesis, Ho?
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
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…
Suppose you have time series data and estimate the following model y = β0 + β1x1 + ε. When you estimate the model, you find that there are correlated observations (serial correlation). You choose to ignore this issue. What are the consequences? A/ The predictions are biased, but the hypothesis tests are valid. B/ The predictions are not biased, but the hypothesis tests are invalid. C/ Both the predictions and the hypothesis tests are valid. D/ Both the predictions and the hypothesis tests are not valid. Choose the correct answer and briefly explain.
General Linear Model: TimeOnline versus Aci Factor Information Type Levels Values d, s Factor ActivityT Fixed 2 Fixed 3 m, o, y Age Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value ActivityT 31084 31083.6 37.49 0.000 Age 2. 11485 5742.7 6.93 0.010 ActivityT Age 2 2936 1468.1 1.77 0.212 Enor 12 9949 829.1 Total 17 55454 O Ho: Average time spent online by age does not differ across activity type O Ho: u1-u2-u3=u4: Average time spent online does differ across activity type O Ho: u1-u2-u3; Average time spent online does differ across activity type O Ho: u1-u2: Average time spent online does not differ across activity type
Econometrics Thomas Eisensee and David Stromberg wanted to measure how much news coverage of a foreign disaster impacted the amount of disaster relief provided by the U.S. government. They argue that the simple relationship would be biased. Let X = Minutes of News Coverage and Y= Disaster Aid. Choose a variable X2 that could bias the simple relationship. This variable should impact the amount of coverage and impact the amount of aid for reasons other than purely news coverage. Eisensee and Stromberg introduce an instrument Z = During the Olympics. Explain how Z could satisfy the relevant and exogenous criteria. Explain how you could use Z to estimate the impact of X on Y free from X2 bias. Hint: you should mention two stages.
Without running any additional tests, what can you say about the joint significance of all independent variables in the model? Which statistic(s) helps you to answer this question?
Being able to read regression results can help the manager use the information to make right decisions particularly in developing a marketing strategy. Assume that you are interested in finding whether the advertisement has a significant positive effect on sales. Which of the following is correct? A. lower standard errors of the estimates are better than higher standard errors B. as a rule of thumb, you are correct 95 % of the time in concluding that there is a positive and significant relationship between the advertising expenditures and sales if the coefficient attached to advertising expenditure is positive and the “t” value is at least 2 C. there is a positive significant relationship between advertising expenditure and sales if both the lower bound and the upper bound of the confidence interval are positive. D. the R2 shows the proportion of the variation in the sales as explained by the model which consists of the advertising expenditure plus some other determinants of sales…
1.5 Version B. Consider a panel data regression model Yit = Bo + B1Xit + a; + eit where the unobserved heterogeneity a; is a fixed effect, and the idiosyncratic error et is white noise. Is the random effect estimator of B1 consistent? Explain briefly.
A regression analysis of company profits and the amount of money the company spent on advertising produced a R² = 0.72. Which of these is TRUE? 1. This model can correctly predict the profit for 72% of companies. II. 72% of the variance in company profit can be accounted for by the model. III. On average, companies spend about 72% of their profits on advertising. OA. None OB. I and III OC. II only OD. III only OE. I only
= 3+5Z+v, Suppose we have the following causal model Y where cov(Z, v) = 0. However, a precise measure of Z is not available. Instead, we have a noisy measure X of Z, that is, X = Z + w with var(w) > 0. Suppose the error w is a classical measurement error so that cov(w, Z) = cov(w, v) = 0. What can you conclude about the limit of the OLS estimator, namely ß₂2 = cov(X, Y)/var(X)? 0 B² = 0 B 5 OB > 0 U
1.5 Version B. Consider a panel data regression model Yit = Bo + B1 Xit + a; + eit where the unobserved heterogeneity a; is a fixed effect, and the idiosyncratic error eit is white noise. Is the random effect estimator of B1 consistent? Explain briefly.

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