New answers tagged econometrics
1
The interaction counts as an explanatory variable. If there is a factor involved in the interaction, all of the levels after the first count (so an interaction between two factors can count as many terms in this situation).
0
Basically, yes. Your DGP is a little weird. Normally, you would not both subscript the $Y_i^C$ and add and error. I'm going to take away the subscript on the $Y^C$. Observe:
\begin{align}
Y_i &= Y^C \cdot exp(T_i\beta) \cdot \epsilon_i\\
ln(Y_i) &= ln(Y^C) + T_i \beta + \epsilon_i
\end{align}
That's just a regression equation (and, it's true ...
0
I'm not sure if this is what you're looking for, but you can use the delta method to approximate the standard error of $\beta_{1}+\beta_{2}$:
$$
SE(\beta_{1}+\beta_{2})\approx \sqrt{Var(\beta_{1})+Var(\beta_{2})+2\cdot Cov(\beta_{1},\beta_{2})}
$$
After the regression command in R, you can type vcov(model) which gives you the variance-covariance-matrix of ...
3
To Expand: Econometrics includes Multivariate Analysis as a tool (a mathematical one). At the same time it may include many other things, such as economic "fundamental" models.
Econometrics is also a certain spin on (applied) statistics, just as biostatistics (one could say biometrics) or statistics in medicine, information theory or whatever field you can ...
2
Econometrics is a specialized branch of applied statistics. Multivariate analysis is a branch of mathematics that has a lot of applications to statistics. For a great econometrics intro (at the beginning PhD level), I recommend Mostly Harmless Econometrics.
It doesn't cover everything by any means, but if you're starting from scratch with no stats ...
1
Cross-sectional data, or a cross section of a study population, in statistics and econometrics is a type of one- dimensional data set. Cross-sectional data refers to data collected by
observing many subjects (such as
individuals, firms or countries/regions)
at the same point of time, or without
regard to differences in time. Analysis
of cross-sectional data ...
1
Maybe we can help eachother, I am working on a similar problem. It is my understanding that the impulse response is the response of one variable to a structural shock in another, the structural shock given by
$x_t=P*u_t$ . Why do you write that $$u_t=\begin{pmatrix} 1 \\ 0 \\ 0 \end{pmatrix}$$, and not the actual residuals?
Have you compared you findings ...
0
Detecting missing variables in full generality is impossible. However, something you can do is plot residuals. Specifically, you could try to plot $y_i - \hat{y}_i$ against each predictor $x_i$. If you observe any patterns (e.g. the residuals look U-shaped), then you are missing some predictors. If you do not observe any patterns, then you may or may not ...
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