4
votes
Accepted
Heteroscedasticity and weighted least square estimator
Let $\hat{\beta}$ be the OLS estimator of $\beta$ in
$$y_t = \beta x_t + u_t$$
Let $\tilde{\beta}$ be the OLS estimator of $\beta$ in
$$\dfrac{y_t}{\sqrt{k_t}} = \beta \dfrac{x_t}{\sqrt{k_t}} + \dfrac{...
- 8,206
4
votes
possible Heteroskedasticity?
The question should be: Does there appear to be enough heteroskedasticity so that not taking it into account would lower the quality of inference? And this is because "taking heteroskedasticity into ...
- 33.4k
3
votes
Accepted
Annual Data and Heteroscedasticity (Engle's ARCH test)
Is there evidence for time varying second moments in annual economic data?
Yes, although not that much in finance in particular but in economics in general resounding yes. For example, the highly ...
- 54.4k
2
votes
Accepted
Heteroskedasticity assumption in fGLS into linear form?
I'm using the 4th edition, but the content should be the same. It sounds like you are asking how he got from
$$
Var(u|\boldsymbol{x}) = \sigma^2exp(\delta_0 +\delta_1x_1+\delta_2x_2+...+\delta_kx_k)
...
- 458
2
votes
Testing for heteroskedasticity in panel data vs time series?
You can regress residual squares (from RE or FE depending on your estimation) on $X_{it} \hat\beta$ and its square using the clustered standard errors (the ...
- 2,104
1
vote
Understanding General Least Squares
The Ω matrix is the matrix of the variance of the error term for each observation. Since we do not observe the true error term, we cannot find the true Ω, but we can try to estimate it.
There are ...
- 21
1
vote
Heteroskedasticity assumption in fGLS into linear form?
Let us write $\mathbf{x}\delta = \delta_0 + \delta_1 x_1 + \cdots + \delta_k x_k$ for notational brevity. If $u^2 = \sigma^2 \exp(\mathbf{x}\delta) v$, where $E(v|\mathbf{x})=1$, then it is indeed ...
- 2,104
Only top scored, non community-wiki answers of a minimum length are eligible