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- The index of industrial production (IP) is a monthly time series that measures the quantity of industrial commodities produced in a given month. This problem uses data on this index for the United States. All regressions are estimated over the sample period 1986:M1 to 2017:M12 (that is, January 1986 through December 2017). Let Ye=1200 x In(IP/IP-1). Suppose that a forecaster estimates the following AR(4) model for Ye Ŷ=0.749 +0.071Y1 + 0.170Y 2 + 0.216Y 3 + 0.167Y(-4 (0.488) (0.088) (0.053) (0.078) (0.064) The forecaster wants to use this AR(4) to forecast the value of Ye in January 2018, using the following values of IP for July 2017 through December 2017: Date IP 2017:M7 2017:M8 2017:M9 2017:M10 2017:M11 2017:M12 105.01 104.56 104.82 106.58 106.86 107.30 The forecast value is a. The forecast value can not be computed with the given values. O b. 4.104 6.485 Od. 0.787NoneSuppose we estimated our multiple linear regression, all of the variables have p values below 0.05 (so they are statistically different from zero) but the intercept has p-value equal to p=0.343. What does it mean?
- Disposable income, the amount left after taxes have been paid, is one measure of the health of the economy. Using U.S. Energy Information Administration data for selected years from 2015 and projected to 2040, the U.S. real disposable income per capita (in dollars) can be approximated by the equation I = 707.6t + 39,090 where t is the number of years after 2015. (a) What t-value corresponds to 2021? t = (b) Find the predicted U.S. per capita real disposable income (to the nearest $10) in 2021. $ (c) In what year is the U.S. per capita real disposable income expected to exceed $55,000? Extreme Protection, Inc. manufactures helmets for skiing and snowboarding. The fixed costs for one model of helmet are $6600 per month. Materials and labor for each helmet of this model are $55, and the company sells this helmet to dealers for $85 each. (Let x represent the number of helmets sold. Let C, R, and P be measured in dollars.) (a) For this helmet, write the function for monthly total costs…I estimate a multiple linear regression model with three explanatory variables and a sample size of 363 observations. The RSS of this model is 0.428217, the adjusted R squared is 0.453103. When I regress the depended variable on a constant only, I find that the RSS of this simple model is 0.789537 The value of the realized F stat for the test on the significance of the regression is: Select one: Ⓒa. 100.9721 and the null is that all betas except the intercept are simultaneously equal to zero O b. 145.056123 and the null is that the model is in the overall significant for the population statistically at 1% significance level 145.056123 and the null is that all betas are simultaneously equal to zero Gm. My Moodle O c. O d. 145.056123 and the null is that all betas except the interecpt are simultaneously equal to zero O e. 100.9721 and the null is that all betas are simultaneously equal to zero Clear my choiceA researcher wants to study the determinants of crime in Turkey. For this she has data 81 Turkish provinces over 17 years. She estimates by OLS the following regression crmpc = Bo + D; + B1polpc + a;+U where crmpc is the crime rate per head of population, polpc is the number of police officers per 1000 individuals. The parameter D, refers to year fixed effects and is one in yeart and zero for all other years. a, is the unobserved province fixed effect and u, is a random unobservable term (idiosyncratic shock). The First Difference estimator (Check all that apply). Your answer: O allows covariance between u, and polpc does not allow covariance between a, and polpc allows covariance between a, and polpc does not allow covariance between u, and polpc O O
- The data for this question is given in the file 1.Q1.xlsx(see image) and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.2. Consider the following regression model and the result of estimation: distance Bo + B,angle + u %3D Where: distance = distance (in feet) traveled by a baseball, angle = the angle (in degrees) the baseball was hit, %3! u= regression error. Dependent Variable: DISTANCE Method: Least Squares Sample: 1 13 Included observations: 13 Variable Coefficient Std. Error t-Statistic Prob. C. ANGLE 32.93084 0.785542 5.146819 1.981191 0.0003 0.0731 169.4891 1.556309 a) Breusch-Godfrey test has been performed that produced the following result. Discuss the test result. Breusch-Godfrey Serial Correlation LM Test: Null hypothesis: No serial correlation at up to 2 lags F-statistic 6.534685 Prob. F(2,9) 7.698535 Prob. Chi-Square(2) 0.0177 0.0213 Obs R-squared b) RESET test has been performed that produced the following result. Discuss the test result. Ramsey RESET Test Equation: EQ01 Specification: DISTANCE C ANGLE Omitted Variables: Powers of fitted values from 2 to 3 Value 475.8260 60.71504 df…This exercise refers to the drunk driving panel data regression summarized below. Regression Analysis of the Effect of Drunk Driving Laws on Traffic Deaths Dependent variable: traffic fatility rate (deaths per 10,000). Regressor Beer tax Drinking age 18 Drinking age 19 Drinking age 20 Drinking age Mandatory jail or community service? Average vehicle miles per driver Unemployment rate Real income per capita (logarithm) Years State Effects? Time effects? (1) 0.41* (0.056) 1982-88 no no (2) (3) (4) -0.62** -0.76*** -0.42 (0.39) (0.33) (0.38) 0.023 (0.078) -0.014 (0.084) -0.023 -0.075 (0.053) (0.064) 0.034 -0.109*** (0.058) (0.058) no yes yes no yes Clustered standard errors? yes yes F-Statistics and p-Values Testing Exclusion of Groups of Variables Time effects=0 (5) -0.76** (0.36) 0.041 0.083 (0.111) (0.115) 0.006 0.015 (0.005) (0.011) -0.068* (0.016) 1.66* (0.66) 1982-88 1982-88 1982-88 1982-88 yes yes yes yes yes yes (6) -0.46 (0.39) -0.004 (0.022) 0.043 (0.101) 0.007 (0.005) -0.064*…
- Consider the following regression model and corresponding output for a dataset with n = 104 observations: y=ß₁+ß2x2+ß³¸*¸+4 3 4x4+u Variable β Std. Error t P>|t| X2 -0.012 0.006 -2.289 0.022 X3 0.596 0.014 41.139 0.000 X4 0.52 1.06 Constant 8.860 1.766 5.017 0.000 What is the marginal effect of x4 on y? (approximate at least to 3 decimal places)A 95% confidence interval for the slope of a simple linear regression model is found to be (0.0046, 0.0252). This means that Select one: a. We are 95% confident the value of the predictor variable will increase by between 0.0046 and 0.0252 units. b. We are 95% confident that the average value of the response value lies between 0.0046 and 0.0252 c. We are 95% confident that the average value of response increases by between 0.0046 and 0.0252 units for every unit increase in the predictor. d. At 5% level of significance, there is no statistical evidence of a linear relationship between response and predictor2