Download the App!

Get 24/7 study help with the Numerade app for iOS and Android! Enter your email for an invite.

Get the answer to your homework problem.

Try Numerade free for 7 days

Like

Report

The file TRAFFIC2 contains 108 monthly observations on automobile accidents, traffic laws, and some other variables for California from January 1981 through December $1989 .$ Use this data set to answer the following questions.(i) During what month and year did California's seat belt law take effect? When did the highway speed limit increase to 65 miles per hour?(ii) Regress the variable log(totacc) on a linear time trend and 11 monthly dummy variables, using January as the base month. Interpret the coefficient estimate on the time trend. Would you say there is seasonality in total accidents?(iii) Add to the regression from part (ii) the variables wkends, unem, spdlaw, and beltlaw. Discuss the coefficient on the unemployment variable. Does its sign and magnitude make sense to you?(iv) In the regression from part (iii), interpret the coefficients on spdlaw and beltlaw. Are the estimated effects what you expected? Explain.(v) The variable prefat is the percentage of accidents resulting in at least one fatality. Note that this variable is a percentage, not a proportion. What is the average of prefat over this period? Does the magnitude seem about right?(vi) Run the regression in part (ii) but use prefat as the dependent variable in place of log(totacc). Discuss the estimated effects and significance of the speed and seat belt law variables.

The simple average of prcfat over the period $1981-1989$ is 0.88563$\%$

No Related Courses

Chapter 10

Basic Regression Analysis with Time Series Data

No Related Subtopics

04:15

Oakland passengers The sca…

09:37

The file CEOSAL2 contains …

13:44

Health The U.S. National C…

07:13

The percent of female wage…

10:32

Driving Fatalities We saw …

06:21

Income and housing revisit…

04:10

The graphic in Applied Exa…

13:09

Table 12.20 shows the life…

02:11

Modeling Data The table li…

07:33

Use the data in MEAP9 3 to…

09:26

Use the data in COUNTYMURD…

15:04

Use the data in DISCRIM to…

16:03

Use the data in KIELMC, on…

16:23

You need to use two data s…

08:40

DATA ANALYSIS: METEOROLOG…

12:37

The data in MEAPOl are for…

05:47

Three different roads feed…

31:17

Athletic Records An analys…

12:25

Use the data in TWOYEAR fo…

02:30

Exercises 33 and 34 refer …

Hartman. The variable belt law is the binary variable. It changes from 0 to 1 AT T equals 61 and that value is associate it with the date January 1986. For variable speed law, another binary variable. It changes from 0 to 1 AT T equals 77 and that corresponds to a date of May 19 87. This is the old L s result when we regress Total accident log of total accident on a time trend and monthly dummies, The coefficient of the time trend is 0.2 e and significant at the 1% level so controlling for seasonality total accident group by about 3% for a month over this period. And we can convert that to an annual rate of you take 12 times three, which is 3.6% of annual rate. Look at the estimates on the monthly dummies. We find a strong evidence of seasonality here. The base is January and when you look at the magnitude of other dummy coefficients, you may find that Ali February dummy has a negative coefficient. The one with the highest coefficient is December dummy. We can conclude that Ali February has a lower number of total accidents than January, which is the basement okay, and total accidents peak in December. Definitely there are 9.6% more accidents in December over January. In the average year, we can also test for joint significance of the monthly dummies. We will find the F statistic to be by 0.15 degrees of freedom are 11 and 95 respectively, and the P value of this F statistic is almost zero. So we are able to reject the non hypotheses in other words, the data such as a strong favor to the joint significance of the monthly dummies or seasonality. Let's move to Part three, where we add new variables to the right hand side of the equation. The coefficients are reported in blue, and it's standard Arrow is in green. The estimates on unemployment rate is negative and significant. The sign of this estimate makes sense if we see unemployment as a measure of economic activity. When economic activity increases, unemployment decreases, we expect more driving, and so more accidents. A 1% point increase in the unemployment rate reduces total accident okay by about 2.1% part four. When we look at the estimates of speed, law and belt law, we see that they are negative and positive, respectively. These seem counterintuitive what the binary speed law implies. That speed limit was increased from 55 to 65 mines per hour. That should increase the total accidents, but we have a negative estimates here for belt law about law. Binary variable implies that the law makes it mandatory for driver to wear seat belt. That should decrease the total accidents. However, we have a positive estimate. Our explanation could be for speed love. As speed limit increase, people become more cautious and that brings down the number of total accidents or the lot on seat belt. The possible explanation is the opposite. As people are asked to wear seat belts, they could become Yeah, more reckless. Well, yeah, that eventually increases the number of total accident. Part five. We wouldn't have a new dependent variable, the percent of fatality per accident. The percent of accidents result in a fatality. The average is 0.886 which mean all accident results to a less than 1% of fatality. The highest value of this variable is 1.217 meaning there was one month where 1.5% of all accidents resulting in a fatality. We will explain this new variable with the Tam Trine seasonality and for new variables. In part three, I will report the estimates on the new variables only. We have the estimates of unemployment rate to be negative and significant speed law and belt law. The is not very significant, but they have our expected sign from previous part. The Beta head of speed law is positive, implying that higher speed limit. Okay, increase the percent off fight. All accident. Okay, 5.67% each point. Yeah. The beta head of seat belt law is negative, implying that Okay, The change in seat Bella is expected to decrease the percent of fatal accident. Yeah, 5.3 However, this variable again is not statistically significant. Statistically different from zero. There is no significant even at the 10% level. This one is significant. Alright. For economic activity, which is proxy seed by unemployment rate, a lower unemployment rate is an indicator of better economic activity. Thank. And with growing economic activities, federal accidents the percent of fatal accidents increase. This could be because Mawr trucks are on the roads and these probably increase the chance that an accident becomes a fatality.

View More Answers From This Book

Find Another Textbook

Oakland passengers The scatterplot below shows the number of passengers depa…

The file CEOSAL2 contains data on 177 chief executive officers and can be us…

Health The U.S. National Center for Health Statistics tracks the major cause…

The percent of female wage and salary workers who are paid hourly rates is g…

Driving Fatalities We saw in a review exercise in Chapter 4 on Calculating t…

Income and housing revisited In Chapter 6, Exercise 32, we learned that the …

The graphic in Applied Example 13.6 reports the effect each at-fault traffic…

Table 12.20 shows the life expectancy for an individual born in the United S…

Modeling Data The table lists the approximate values $V$ of a mid-sized seda…

Use the data in MEAP9 3 to answer this question.(i) Estimate the model

Use the data in COUNTYMURDERS to answer this question. Use only the year $19…

Use the data in DISCRIM to answer this question. These are ZIP code-level da…

Use the data in KIELMC, only for the year $1981,$ to answer the following qu…

You need to use two data sets for this exercise, JTRAIN2 and JTRAIN3. The fo…

DATA ANALYSIS: METEOROLOGY The table shows the maximum daily high tem…

The data in MEAPOl are for the state of Michigan in the year $2001 .$ Use th…

Three different roads feed into a particular freeway entrance. Suppose that …

Athletic Records An analysis similar to that of the previous exercise can be…

Use the data in TWOYEAR for this exercise.(i) The variable stotal is a s…

Exercises 33 and 34 refer to the following setting. Thirty randomly selected…

06:59

Use the data in KIELMC for this exercise.(i) The variable dist is the di…

00:13

Refer to Example 13.9 and the data in CRIME 4(i) Suppose that, after dif…

Consider the analysis in Computer Exercise $\mathrm{C} 11$ in Chapter 4 usin…

03:41

If the corporate income tax induces businesses toreduce their capital in…

02:32

Marshall and Lily work at a local department store.Marshall, who greets …

06:17

The data in ECONMATH contain grade point averages and standardized test scor…

05:55

Use the data in PHILLIPS to answer these questions.(i) Estimate the mode…

27:04

Use the data in $\mathrm{HTV}$ to answer this question. See also Computer Ex…

Use the entire panel data set in AIRFARE for this exercise. The demand equat…

05:31

Consider the following model to explain sleeping behavior:sleep $=\beta_…