% Title Presentation % Jakob Schwerter % Whatever, Tilburg University
- Why do I not enjoy the sun, while sitting in the library?
- I mean, I have to work and cannot enjoy the sun.
- Maybe, because I have an aim I follow and there is not much which can actually distract me from that aim.
- Based on the fact that I have to work on the library, it is nicer to see the sun shining.
- Clear reason to get a spot at a window btw.
- More Chesse = More Holes
- More Holes = Less Cheese
- More Cheese = Less Cheese
just saying...
eststo: reg lnc lnp lny, cluster(state)
Results can be seen in Table 1
quietly tab year, gen(period)
eststo: xtreg lnc lnp lny period*,fe cluster(state)
eststo: reg d.lnc d.lnp d.lny d.period*, cluster(state)
xtserial lnc lnp lny
$$\begin{align*}
lnC_{it}= \alpha + \beta_{1}lnP_{it} + \beta_{2}lnY_{it} + \gamma_{2} D_{2i}+... + \ \gamma_{46} D_{46i}+ \delta_{1} T1_{t} + ...+ \delta_{29} T29_{t}+u_{it}\end{align*}$$
- Model of interest
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In order to account for time effects dummy variables (T1-T29) for each but one year are included.
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To include state effects there are several options.
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Firstly dummy variables (D2-D46) for each state can be added to the regression.
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Equivalently, a fixed effect within transformation can be applied to get rid of unobserved state-specific effects.
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If the state-specific effects can be argued to be uncorrelated with the included regressors, a random effects model can be estimated.
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In fact, the Hausmann test does no reject the RE model.
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Since the RE model yields very similar estimates we will stick with the FE model for comparative purposes.
- Given that smoking is an addiction it is plausible that there might be
serial correlation.
- confirmed by a test for serial correlation in the residuals obtained from the FD regression.
- Given substantial serial correlation, the FD regression should generally be preferred.
I guess it is okay to make the presentation a bit shorter than the normal file
Partial derivatives of the expenditure function with respect to prices
$$\begin{align*}
&h_{1}(p,u) = \frac{{\partial}e(p,w)}{{\partial}{p_{1}}} = \frac{u}{2} \
&h_{2}(p,u) = \frac{{\partial}e(p,w)}{{\partial}{p_{2}}} = u \
&\rightarrow h(p,u) = \left(\begin{array}{c} \frac{u}{2} \ u \end{array} \right)\end{align*}$$
Because figures are fun.
Checkout the different width's ;)
- we can also do a figure from the web
- But I actually prefer a funny meme
- Because I like them
- But I actually prefer a funny meme
- Cool, different types of points for nested bullets
- Nice
I hope we did not have to contruct a presentaiton which makes completely sense
I keep the following to know the codes for an easy copy and past:
- to get the html, type the following on one line:
pandoc -s --mathjax --slide-level 2 --toc --toc-depth=1 -t revealjs presentation.md -V theme=solarized -o index.html
- if you want a beamer/latex pdf, type:
pandoc --slide-level 2 --toc --toc-depth=1 -t beamer presentation.md -V theme:Montpellier -o presentation.pdf


