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Use the data in EZANDERS for this exercise. The data are on monthly unemployment claims in Anderson Township in Indiana, from January 1980 through November $1988 .$ In $1984,$ an enterprise zone (EZ) was located in Anderson (as well as other cities in Indiana). [See Papke $(1994)$ for details.](i) Regress log(uclms) on a linear time trend and 11 monthly dummy variables. What was the overall trend in unemployment claims over this period? (Interpret the coefficient on the time trend.) Is there evidence of seasonality in unemployment claims?(ii) Add $e z,$ a dummy variable equal to one in the months Anderson had an EZ, to the regression in part (i). Does having the enterprise zone seem to decrease unemployment claims? By how much? [You should use formula $(7.10)$ from Chapter $7 . ]$(iii) What assumptions do you need to make to attribute the effect in part (ii) to the creation of an EZ?

(i) significant trend and seasonality (ii) Yes (iii) Assume no other external factors influencing trend of unemployment claim

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Chapter 10

Basic Regression Analysis with Time Series Data

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part one. The coefficient on their attempt trend is minus point 0139 and it's standard error is 1390.12 So monthly unemployment claims is a dependent variable and it enters Theis equation in lock. So the change of this kind of variable should be interpreted as percent a percentage change. The size of the Tan Trinh implies that monthly unemployment claims falls 1.4% almost 1.4% per month on average. Yeah, and given the result for the coefficient of the temp, Trine, we can calculate the T statistic. The T strut is with a head divided by its standard error. And we have a very small number of send it Errol here. So we should have a fairly large T statistic. And given the value of the T statistic, you can conclude that the trend is significant. Okay, so that is the trend of unemployment claims about their seasonality. We will look at the estimation results for the monthly dummies. We will evaluate their individual significance and their joint significance. So you will look at the t the individual T statistic. You may find that six out of 11 monthly dummy. Dummy variables have a high T statistic. Yeah. Yeah. Okay. So I can write Six out of 11 dummies are highly significant for joint significance. You in run an F test and find a p value of the F statistic. You may get the P value of the F statistic as mhm going. Oh 01 So these monthly dummies are also jointly highly significant. You can conclude that there is a very strong seasonality in unemployment claims. Okay, in part to doing ad variable Easy to the regression. It's estimated coefficient is minus 0.508 The standard error is 0.146 We can interpret this result as unemployment claims are estimated to fall. Okay, because the Beta head has a minus sign, it is negative. We we need to convert. We we need to calculate very quickly. Thio, find the change in unemployment claims, so you will need to take one minus the exponential of beta hat minus 0.5 08 so e to the power of minus 0.58 and to get their percent change, you would multiply the whole bracket with 100 and what you get is unemployment claims are estimated to fall by almost 40% after Enterprises Zone designation. For part three, we must assume that around the time of enterprise zone designation, there were no other external factors that can cause a shift down in the trend of the lark of unemployment claims. Yeah, so no other factors influence unemployment claims, unemployment claims trend around the time of easy designation. Okay, we already controlled for a time, trend and seasonality, but this may not be enough.

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