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3

Normally an exam question like that can be solved by simply calculating the $t$-statistics which can be simply done as: $$t_{\hat{\beta}} = \frac{\hat{\beta}-\beta_0}{se(\hat{\beta})}$$ where $\hat{\beta}$ is the estimate, $\beta_0$ is the assumed value of beta under null (usually 0) and $se(\hat{\beta})$ standard error of the coefficient and then compare it ...

0

You have to add them even though they are in $\ln$ the model is still linear in the parameters. To see this more clearly you can rewrite your equation as (I use $D$ for demand, $c$ for constant, $F$ for fare and $M$ and $T$ for Monday and Tuesday): $$\ln(D) = c + \left(- 0.45 - 0.15(T) + 0.05(M) \right)\ln(F) + 10(M) + 15(T)$$ Hence when both $T=0$ and \$M=...

1

Assuming that from the beginning you made the regression with prarrest with transformation *100 (say you have for example [0.012,0.093] and you transform it into -> [1.2,9.3], I remark this cause its critical to the interpretation). Ceteris paribus, one percentage point increase in prarrest causes the number of crimes committed per 1000 county residents ...

0

After some of my own back-and-forth, I'm going to try and answer my own question. I hope I might get some comments on whether or not I'm correct. If I'm understanding 1muflon1's response correctly, they provided a very useful explanation of why it's important to get the number of lags correct in an ARDL, but as Michael pointed out, that wasn't my question (...

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