chan1142
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The meaning of the words first Some people use the word "IV estimator" to refer to any estimator that uses instrumental variables. To them, IV estimators contain 2SLS, LIML, k-class estimators, and ...

The Stata outputs say that you did not omit the intercept. No worries about the intercept. The _cons row is for the intercept. As you know, Stat's xtreg ..., fe will give you identical coefficient ...

Let us consider Situation 1. Let us assume that $\rho$ is observed. If it does not work when $\rho$ is observed, there is no reason why it (using a proxy of $\rho$ as instrument) should work when $\... View answer 6 votes Weak instruments combined with slight instrumental endogeneity can lead to a larger bias than OLS. As Nox's answer shows, the probability limit of the IV estimator is$\beta_1 + cov(z,u)/cov(z,x)$. ... View answer Accepted answer 5 votes Nerlove and Wallis (1966) result Nerlove and Wallis (1966) have discussed this issue. Their Equation (3) derives the probability limit of the Durbin-Watson statistic as:$$\mathrm{plim}\, d^* = 2 \... View answer Accepted answer 5 votes Intuitive explanation might help. (i)$E[u]=0$vs$E[u|x]=0$: Imagine partitioning the population by the value of$x$so that each slice of population has the same value of$x$in it. You can then ... View answer 5 votes R-squareds and p-values give different information. For example, suppose that you regress birth weight on mother's smoking. The p-value tells us whether the association is indeed nonzero (in the ... View answer Accepted answer 4 votes You can also statistically test heteroskedasticity (e.g., White's test: regress squared residuals from OLS on$\hat{y}$and$\hat{y}^2$, or if you have a simple model, regress$\hat{u}^2$on$x$and$...

Let $\hat{\pi}_0$ and $\hat{\pi}_1$ be the first stage coefficients. That is, $\hat{D} = \hat\pi_0 + \hat\pi_1 Z$. When $Z_i D_i = Z_i$ (if $Z_i=1$ then $D_i=1$), we can show that $\hat\pi_0 + \hat\... View answer 3 votes Your model has$\beta_3 * t$, which is a linear time trend, not time dummies. If that's correct, you are controlling for only a linear trend. Because oil prices do not have a perfect linear trend, you ... View answer Accepted answer 3 votes The constant term in your final FE model has no specific meaning without further restrictions. For Stata, it is only chosen such that the (sample) mean of the estimated individual effects add up to 0. ... View answer Accepted answer 3 votes FE logit requires the idiosyncratic errors to be IID across$i$and$t$, quite a strong assumption. Also the regressors should be strictly exogenous, but it's the same for linear FE models. In your ... View answer Accepted answer 3 votes There are cases error serial correlation is a disaster. For example, if your model is$y_t = \beta_0 + \beta_1 y_{t-1} + u_t$, then serial correlation in$u_t$means correlation of$y_{t-1}$and$u_t$... View answer Accepted answer 3 votes In Alecos Papadopoulos's answer, both the conditional mean and the conditional median are linear in$X$. In the following example, the conditional mean is linear in$X$while the conditional median is ... View answer Accepted answer 3 votes If I understood you correctly,$\Lambda(x_i'\hat\beta)$, where$\Lambda(x) = e^x / (1+e^x)$. You can use predict in both R and Stata. Try in Stata: clear all *** Generate data set obs 10 set seed 1 ... View answer Accepted answer 3 votes In general, if$c_1$is endogenous, you need instruments for$c_1$as well. Example: Even when$x_1$is exogenous, if$c_1$is endogenous and$x_1$and$c_1$are correlated, then OLS (which is the IV ... View answer Accepted answer 3 votes As jmbejara says, better to be more specific, but let me guess. You have a linear model$y = \beta_0 + \beta_1 x_1 + \cdots + \beta_{10} x_{10} + u$, where$x_1, x_2, \ldots, x_{10}$are endogenous. ... View answer 2 votes The issue OP raises has always bothered me. Now I think it all depends on the purpose of the study and whether we want to control for$y_{it-1}$or not. There are examples against dynamic models. As ... View answer 2 votes Welcome. Do you mean: if$x^*_{k,it}$are the panel variables, then$x_{1,i} = \min_t x^*_{1,it}$,$x_{2,i} = \max_t x^*_{2,it}$, etc.? Usually people use averages over time or values at certain ... View answer 2 votes This is a very thoughtful question. I think it is related with (i) the purpose and (ii) the sample size. Econometrics is very often concerned with causality (rather than prediction or forecast). For ... View answer Accepted answer 2 votes Short answer: No. Your model is$Y=\alpha + \beta X + \varepsilon$. Even when$X$is exogenous, if you regress$Y$on$X$,$W_1$and$W_2$, then the OLS estimator is inconsistent (for$\beta$) unless$...

Yes, $IM$ increases in the equilibrium, but, no, $IM$ cannot increase more than $X$ does. Logical explanation: "$\Delta X < \Delta IM$ in the equilibrium" means that $A$'s GNP decreases. When GNP ...

No, you're right, $Cov[Z_i,u_i]=0$ does not imply $E[u_i|Z_i]=0$ in general. If the author defined "instrument exogeneity" as $Cov(Z_i,u_i)=0$ previously, he/she is being careless here. "Instrument ...

General remarks: The BG test under homoskedasticity can be done using the bgtest command in the lmtest package of R. The $(n-p)R_{aux}^2$ version mentioned in link works only under homoskedasticity. ...

That is natural and you are not missing anything. Let $y=\alpha + \beta z + u$. Your prediction of $y$ given $z=1$ is $\hat{y} = a + b$, where $a$ and $b$ are the OLS estimates. The prediction error (...

That can happen and is not odd. For your model the partial effect of a change in $x_1$ on the average price equals $\beta_1 + 2\beta_2 x_1$ (use calculus for simple derivation). Thus, $\beta_1$ ...

Point prediction and CI are different. For point prediction, we are better off by correcting the bias as much as possible. For CI, what is required from the beginning is that the probability equals $... View answer 2 votes It is conventional to report both the total number of observations (1000 in your case) and the number of groups (500 in your case). If you want or are allowed to report only one number as the number ... View answer 2 votes You have done two different things. Your fixed-effects model captures the within-group over-time functional relationship between$debt_{it}$and$y_{it}$(that is, how much average difference in$y_{...