Download the App!

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

Use the data for the year 1990 in INFMRT for this exercise.(i) Reestimate equation (9.43), but now include a dummy variable for the observation on theDistrict of Columbia (called DC). Interpret the coefficient on DC and comment on its size andsignificance.(ii) Compare the estimates and standard errors from part (i) with those from equation (9.44). Whatdo you conclude about including a dummy variable for a single observation?

Get the answer to your homework problem.

Try Numerade free for 7 days

Like

Report

(i)The coefficient of $D C$ is 16.03498It is interpreted as the infant mortality rate being 16.03498 for the State of District of Columbia(ii) It shall be noted that there is only one observation related to $D C$ in the data corresponding tothe year 1990 out of the set of 51 observations, hence, it can be concluded that including adummy variable for a single observation yield identical results vis-à-vis excluding the dummy variable for that single observation as far as the coefficient estimates and their respective standard error is considered

No Related Courses

Chapter 9

More on Specification and Data Issues

No Related Subtopics

04:11

Use CONSUMP for this exerc…

03:31

In exercise $2,10$ observa…

0:00

2.2. Refer to Exercise 11.…

02:27

Refer to exercise $9,$ whe…

03:49

(i) In the wage equation i…

03:24

Use the data in FERTIL3 fo…

05:33

Use the data in BARIUM for…

04:16

$$\begin{array}{l}{\te…

all right. Hello, everybody. Today we're gonna be doing some more, uh, econometrics modeling. Um, so, as always, if you look at the top first thing we have to do install, package will bridge if you haven't already. This is our This contains our data sets. Ah, you also need to say that we're using the package through this line. Live very Wooldridge. And then just down here, I have a comment of the initial equation that we will be working off. You know, that. Just gonna keep that up their toe, help us understand. So first thing we need to do is we need to pull our data set. Our data set is the infant mortality data. If I do view infant mortality really quick, you'll see this here. Um, sorry of this is glinting out for you. Um, yeah, that's our data set, but, uh, notice were specifically asked to only use the data from year 1990. So, actually, we view this again. You can see 1987 and 1990 for a lot of these case. So how we're gonna do this is we're gonna use the subset. We're going to say incident mortality. 1990 is equal to the subset of infant mortality. Where are subset? Function is equal to the criteria were going to use you. Just one year is equal to 99. We pulled this subset and we'll see now if I Ah, Now, if we you the infant mortality for just 1990 all of these years or 1990 again. I apologize for this weird glitches nous Okay, so first we need to, um, do our same model is above infant mortality is log of piecing, plus lot of physic plus log of popular, But we're going to include a dummy variable for the observation on D. C. So what we're gonna do is we are going to um Actually, this is pretty simple for us because you look over here. We already have a B C variable. And what this does is it has a one, right? Basically a true statement for the one observation that is from D C instead of all of the other states. So Okay, that's pretty simple. We're gonna have our model. Um, actually, let me call this with BC. We're gonna be equal to l m. That's a linear model. Our equation is, infant mortality is equal to a lot of piecing. Plus log of is a lot of popular plus D c. And then our data set is the infant mortality, specifically from 1990. Um, just you you just view that again and make sure I have more. Okay. Sorry. So that's an example of variable naming kind of messing you up. I assumed it was the same as the data set, but it is not all right now we have our linear model done. If I type in our summary for that model, we will get all of our answers. Um, and if we want to look at the estimate and standard er for at the coefficient for D. C will see that it has a pretty large coefficient, considering these other ones which are, you know, um, you know, in that sorry. Ah, this one is only negative. 2.7. These two are about less than absolute value. One, this is 16. And you know, that becomes even more evident if you look over here. The probability, because this has a probability of 8.43 times 10 to the negative 12 which means it is a very significant result. But okay. Now what we want to do is we want to actually interpret. Sorry. Now, we want to compare these estimates with a different model. This different model. Ah, well, actually exclude D. C from the data set with our original formula. So how we're gonna do this is we're gonna create another subset called without BC, right. Ah, and that's gonna be l m. That's gonna be subset off Instant, more Taliban 1990. Where are subset criteria? Now, instead of worrying about the year, we're just gonna safety C is equal to zero. I'll exclude any data points about BC. And if I do, um, if I used the count function, you'll see their 51 items in the 1990 data set, but in. But once we exclude BC, we should have only 50 items, which we do because we just took out at one data point. Now, if I make, um, I make a model for without d C, this is gonna be in. Fillmore is logged, he sing Well, uh, let's log plus log popular on. We're gonna ignore our d c dummy variable. And this is going to be the data set from, um, without E c. Okay, if I pull a summary of bristling, what we'll see is we have basically the exact same coefficients. 23.9548 point 3.94 point negative 0.56699 point 566 exact same coefficients what that means. So that's the observation, right? When you include D. C is a dummy variable versus when you simply excluded from the data. All together you get the exact same coefficients for everything else. Um, and the respect of standard errors are also the exact same. All right, cool. So we can and we can understand that including a dummy variable for a single observation like this D c. Right. This only accounts. That single observation that's basically doing the same thing is just excluding that observation altogether. All right, so that's it. Thank you very much. Have a good day

View More Answers From This Book

Find Another Textbook