Okun's Law equation, y = a + bx + e, reflects the relationship between a country'sgrowth rate (y) and unemployment rate change (x). The regression uses quarterlyseasonally-adjusted data covering a 10-year period to estimate the Okun's Lawequation coefficients, a and b. The regression results suggestRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotal.0.5816500240.3383177970.3265020442626981009df100SS1197 595615599386.4576539100 584.0532695Coefficients Standard ErrorIntercept0.301072098 0.345330555UnRateChange -5.665498514 1:058781687MS197.59561556.901029535t Stat0.871837414-5.350960053F28.63277349P-value0.3870197081.68197E-06Significance F168197E-06 a) a 1% increase of the unemployment rate change leads to a 5.67% decrease ofthe real GDP growth rate.b) a 1% increase of the unemployment rate change leads to a 1.68% decreaseof the real GDP growth rate.c) the relationship is not significantly different from zero.d) a 1% increase of the real GDP growth rate leads to a 1.68% increase of theunemployment rate change.O e) a 1% increase of the real GDP growth rate leads to a 5.67% decrease of theunemployment rate change.A

The functional form of Okun's law n regression equation y=a+bx+e Where x denotes unemployment rate…

a) a 1% increase of the unemployment rate change leads to a 5.67% decrease of the real GDP growth rate. b) a 1% increase of the unemployment rate change leads to a 1.68% decrease of the real GDP growth rate. c) the relationship is not significantly different from zero. d) a 1% increase of the real GDP growth rate leads to a 1.68% increase of the unemployment rate change. e) a 1% increase of the real GDP growth rate leads to a 5.67% decrease of the unemployment rate change.

a) a 1% increase of the unemployment rate change leads to a 5.67% decrease of the real GDP growth rate. b) a 1% increase of the unemployment rate change leads to a 1.68% decrease of the real GDP growth rate. c) the relationship is not significantly different from zero. d) a 1% increase of the real GDP growth rate leads to a 1.68% increase of the unemployment rate change. e) a 1% increase of the real GDP growth rate leads to a 5.67% decrease of the unemployment rate change.

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

Chapter4A: Problems In Applying The Linear Regression Model

Section: Chapter Questions

Problem 1E

See similar textbooks

Similar questions

Explain what is meant by an error term. What assumptions do we makeabout an error term when estimating an ordinary least squares regression?
An OLS regression should be used when the independent variable is nominal. A. True B. False
wages = B1 + B2educ + ßzexper + e where wages denotes hourly wages. We estimate the regression in R and obtain the output ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.0 1.0 2 0.0455 * ## educ 0.5 0.5 1 0.3173 ## exper 2.0 0.5 4 6.33e-05 *** ## --- ## Signif. codes: ****' 0. 001 '**' 0.01 **' 0.05 ' 0.1 ' ' 1 Build a 90% confidence interval for B3 using a normal approximation. (Use that if Z ~ N(0, 1) and z1-a satisfies P(Z > z1-a) = a, then zo9 = 1.28, zo95 = 1.64, Z0.975 = 1.96, zo.99 = 2.33, and z0.995 = 2.58). Oa. [2 – 1.64 x (0.5), 2 + 1.64 x (0.5)] O b. [2 – 1.28 × (0.5), 2 + 1.28 x (0.5)] c. [2 – 1.28 x (0.5)², 2 + 1.28 × (0.5)²] O d. [2 – 1.64 × (0.5)², 2 + 1.64 × (0.5)²] O e. [2 – 1.96 × (0.5)², 2 + 1.96 × (0.5)²] O f. [2 – 1.96 × (0.5), 2 + 1.96 × (0.5)]
"In the regression model InY=b0+b1*InX+u, the coefficient b1 is interpreted as" O the intercept O A covariance O A regressor O An elasticity
How should I interpret the coefficients on a regression with a naural log of a dependent variable? Ex: Ln(wage)=B1+B2Experience+B3Male...+Ui
ii. Interpret both slope coefficients of the estimated OLS regression of wage (in rupees) on education (in years) and experience (in years). What is the overall predicted wage change if education and experience change by one year each? = Wage 30000+1500 education + 500 experience What can you infer about the shape (U-shape/inverse U-shape) of the relationship between job performance (the number of tasks completed per day) and hours of sleep a person gets from the following estimated OLS regression? At what level of sleep does the job performance peak? Job Performance 20+5 hours of sleep-0.25 (hours of sleep)2
A scatter plot shows data for the cost of a vintage car from a dealership (y in dollars) in the year a years since 1990. The least squares regression line is given by y-25,000 + 500z. Interpret the y intercept of the least squares regression line. Select the correct answer below O The predicted cost of a vintage car from a dealership in the year is 820.000 O The predicted cost of a vintage car from a dealershpin the year 1090 is 85,000. O The predicted cost of a vintage car from a dealershp in the year 1990 is sse. The yintercept should not be interpreted.
Analysis of Variance Source DF SS MS Regression 1 02364 13 14 Residual Error Total 11.3240 What is the value of SSR (Sums of Squares for Regression)?
Discuss the FIVE (5) importance of adding error term in the regression model.
You have data on the training regime of 100m elite runners. For each runner you observe their best run of the year (in second) (pb), the number of hours they train each week (tr) and a dummy variable equal to 1 if thei are male (male). Using OLS you get the following regression: pb= 36.2 1.3male -0.92tr +0.009tr² -0.09male tr +0.001male * (tr²) How many hours should a male elite runner train each week to minimize the time of their best run of the year? (round to the closest decimal)
To determine whether the revenue made trom selling ice creams in a smal shop (in USD) and the temperature (in Celcius degrees) are related, a researcher decides to employ the simple linear regression analysis. Using a random set of days in which the temperature varies between 20 and 35, she comes up with the following regression line: Ice Cream Revenue- 200 90 Temperature Based on this analysis, what can she conclude? O A)At 20 Celcius degrees. the shop owner can xpect to sell 5000 USD O B)AI 30 Celoius degrees, the shop owner can expect to sel 2000 usD O C)As the temperature goes up by one degree Celoius, the revenue increases by approximately 00% OD)On average ce cream revenue is about 200 USD when the temperature is O degrees Celcus O E)As the temperature goes up by one degree Celcius. the reverue inereases by approximately 200 USD.
How do you interpret the R-squared obtained from running this regression?