1
$\begingroup$

I'm working with some panel data, and I'm interested in estimating the parameters of the following process:

$$\Delta y_{t+1} = \alpha + \delta t+\beta_1 \ln y_t + \beta_2 \ln x_t+\epsilon_t$$

Where $y_t \sim I(1)$ (and $\Delta y_t \sim I(0)$). Hence, we have a cointegration relationship:

$$\alpha + \delta t+\beta_1 \ln y_t + \beta_2 \ln x_t \sim I(0)$$

Similar work approaches this estimation problem by using error correction models using the above relationship as the co-integration relationship. Why should I do this, as opposed to simply estimating the model directly?

My intuition says that the direct approach should be valid because both sides are $I(0)$ to begin with.

$\endgroup$
1
  • $\begingroup$ I think its done that way because most researchers in this are feel its the best way to do it. That does not mean alternate approaches won't work. So it comes down to whether you think the experts who prefer it are right. You can always simulate data and see which works best. $\endgroup$
    – user54285
    Commented Jul 23, 2020 at 21:38

1 Answer 1

2
$\begingroup$

What you have given is the same as the Johansen long-run VECM for a VAR(1) model. So you should be able to estimate your top linear model directly. (Do be careful, however, about violations of homoskedasticity.)

You may also find this question and answer helpful.

$\endgroup$
3
  • $\begingroup$ hi: could you explain why you have a cointegrating relationship ? I don't see it. thanks. Oh, I assume you're referring to $y_t$ and $ln(x_t)$. $\endgroup$
    – mlofton
    Commented Jul 24, 2020 at 3:16
  • $\begingroup$ The cointegrating relationship is assumed in the question. $\endgroup$
    – kurtosis
    Commented Jul 24, 2020 at 16:32
  • $\begingroup$ Thanks. I couldn't pick that up from what was written. and still can't so it's appreciated. $\endgroup$
    – mlofton
    Commented Jul 25, 2020 at 19:22

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.