Dickey Fuller for Multiple Regression Models

[sama]

Create a new varaible ddy=d(y,2) [or d(d(y))] which is the double difference. When performing the unit root test, choose the 2 difference option to gea test for I(4).
alternatively, if you want to actaully use the variable in its fourth difference form, then create it:
1-right click>new object> sereis> (name it "d4y" for example).
2-genr> d4y=d(y,4)

[econochoice]

Really grateful tcfoon.

Dear Econochoice,

For I(1) process, If L is price then the first difference of L = D(L) is interpreted as inflation. If Y is income, then first difference of income may interpreted as growth. The interpretation is that if inflation increase by one percent, on average the economic growth will increase/decrease by XXX percent and holding other factors is constant.

For I(2) process, DD(L) may be interpreted as growth of inflation (which is the growth of the growth), if growth of inflation increase by one percent, on average economic growth will increase/decrease by XXX percent.

Sometime the transformed variables is meaningless, but is to comply the statistical require only. From my point of view, the magnitude or the size of the parameter is not very important issue compared to the sign of the variables. If we aware of the fact that, when we interpret the coefficient we must mention the statement "holding other factor is constant" either in front or at the end of the sentence. The statement of "holding others factor is constant" or also known as Ceteris Paribus is irrational in the real world. Lutkepohl (1994) - Econometric Reviews pointed out that in the real world nothing is constant, thus direct interpretation of the coefficient may be incorrect. In this respect, obtain a correct sign should be focus on instead of the interpretation of the coefficients. For example, if the coefficient for computer price is -1.04, conventionally we may conclude that if the price of computer increase by one unit, then the demand for computer will reduce by 1.04 unit, holding other factor is constant. In practice, are you sure that the demand for computer will reduce by exactly 1.04 unit or 1.00 unit if the price of computer increase by one unit? Of course NO and the most useful indication for -1.04 is the negative relationship. One thing we affirm is that when the price of computer increase the demand for computer will reduce but we are not sure by how many unit computer will reduce.

If you want to make your research more valuable and interesting, you may consider cointegration, Granger causality, impulse response function and variance decomposition analyses.

Thank you,

Regards,
tcfoon

[Yvone]

Hello, I got some question regarding my asaignment. Quaterly data from 1995 until 2015. Using ADF unit root test. Hope someone can answer my question.
1) I have one variable which is my dependent variables is stationary at second differencing,what can I do for it?
2) I got five independenr variables are stationary after 1st differencing,but it is can stated that they are stationary at different level or same level due to my dependent variable is stationary after secons differencing?

[Jumysh]

to lardie2345


if all variables are I(1), you should use level values, if variables are different integrating order, you should equal them by integrating order.

[quote][/quote

This post by dealsfe to lardie2345 is interesting, but I need someone to quite a reference to justify or authenticate the statement. I am working on a multivariate data with all variables stationary at I(1). When I proceed to run regression with the I(1) values, all the results I get are contrary to a priori expectation. When I run the regression with level values, the results are according to a priori expectation. Could some one be of help please.

[Jumysh]

to lardie2345


if all variables are I(1), you should use level values, if variables are different integrating order, you should equal them by integrating order.

[quote][/quote

This post by dealsfe to lardie2345 is interesting, but I need someone to quote a reference to justify or authenticate the statement. I am working on a multivariate data with all variables stationary at I(1). When I proceed to run regression with the I(1) values, all the results I get are contrary to a priori expectation. When I run the regression with level values, the results are according to a priori expectation. Could some one be of help please. Could some one direct me to a book or an authentic source that l can use to justify why I should use level values to run regression if all variables are stationary at 1st difference please.