EViews.com

Please tell me, what Eviews’ command I should use to operate the predicted values of ∆Yt. For example, in STATA, “deltaYhat” is used in the equation for the predicted value of ∆Yt, such as regress y deltaYhat x dif_x. For Eviews, what command should I substitute for deltaYhat. - ls y (??? instead of deltaYhat) x d(x). I would greatly appreciate it if anyone can tell me. Thanks!

I don't understand what you're asking.

I am trying to use the single equation error correction model. Since the long term effect is a ratio of two coefficients, the single equation ECM does not report the standard error and confidence level of the total long term effect of an IV on DV. Thus, I need to estimate the standard error and confidence level of the total long term effect of X on Y using the Bewley transformation regression. The following procedures will be adopted. First, I estimate ΔYt= α+ βY_(t-1)+ βX_t+βΔX_t+ ε_t. Second, I should take the predicted values of ΔYt from the previous procedure and estimate Yt= α+ δ_0 ΔY_t+ δ_1 X_t- δ_2 ΔX_t+μ_t. In STATA, I can use the following command to estimate the second equation: Regress y deltaYhat x dif_x. I wonder what commend I should use to estimate the second equation in Eviews. I hope this explanation is clear enough. Please let me know! Thanks!

Use equation.fit or equation.forecast to get the fitted values from first equation.

My first equation in Eviews’ words would be d(y) c d(x) y(-1) x(-1), and I am not sure how I can transcribe the second statistical algorism into Eviews’ words? Do you mean this? - y c equation.fit x d(x). I did but some error message appear about “equation.fit.”

Assuming you have an equation called "eq1", you would produce fitted values of Y by typing: See the equation section of the Object Reference for more details.

I found that the variables of interest are nonstationary and integrated of I(1), but they are cointegrated. Can I estimate the equation in levels using OLS? I read somewhere that the equation of cointegrate variables is less likely to produce spurious results. I just wonder whether I can run the analysis in levels if the variables are conitegrated without differencing the series or building error correction model. Thank you!