Test if economic indicators have no explanatory power

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# test if economic indicators have no explanatory power linearHypothesis (fatalities_mod4, test = "F" , c ( "log(income)" , "unemp" ), vcov. = vcovHC, type = "HC1" ) #> Linear hypothesis test
10.6. DRUNK DRIVING LAWS AND TRAFFIC DEATHS 301 #> #> Hypothesis: #> log(income) = 0 #> unemp = 0 #> #> Model 1: restricted model #> Model 2: fatal_rate ~ beertax + state + year + drinkagec + punish + miles + #> unemp + log(income) #> #> Note: Coefficient covariance matrix supplied. #> #> Res.Df Df F Pr(>F) #> 1 275 #> 2 273 2 31.577 4.609e-13 *** #> --- #> Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Model (5) omits the economic factors. The result supports the notion that economic indicators should remain in the model as the coefficient on beer tax is sensitive to the inclusion of the latter. Results for model (6) demonstrate that the legal drinking age has little explana- tory power and that the coefficient of interest is not sensitive to changes in the functional form of the relation between drinking age and traffic fatalities. Specification (7) reveals that reducing the amount of available information (we only use 95 observations for the period 1982 to 1988 here) inflates standard errors but does not lead to drastic changes in coefficient estimates. Summary We have not found evidence that severe punishments and increasing the min- imum drinking age reduce traffic fatalities due to drunk driving. Nonetheless, there seems to be a negative effect of alcohol taxes on traffic fatalities which, however, is estimated imprecisely and cannot be interpreted as the causal effect of interest as there still may be a bias. The issue is that there may be omitted variables that differ across states and change over time and this bias remains even though we use a panel approach that controls for entity specific and time invariant unobservables. A powerful method that can be used if common panel regression approaches fail is instrumental variables regression. We will return to this concept in Chapter 12.
302 CHAPTER 10. REGRESSION WITH PANEL DATA 10.7 Exercises This interactive part of the book is only available in the HTML version.
Chapter 11 Regression with a Binary Dependent Variable This chapter, we discusses a special class of regression models that aim to ex- plain a limited dependent variable. In particular, we consider models where the dependent variable is binary. We will see that in such models, the regression function can be interpreted as a conditional probability function of the binary dependent variable. We review the following concepts: the linear probability model the Probit model the Logit model maximum likelihood estimation of nonlinear regression models Of course, we will also see how to estimate above models using R and discuss an application where we examine the question whether there is racial discrimination in the U.S. mortgage market. The following packages and their dependencies are needed for reproduction of the code chunks presented throughout this chapter on your computer: AER (Kleiber and Zeileis, 2020) stargazer (Hlavac, 2018) Check whether the following code chunk runs without any errors.

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