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    A question regarding simultaneous relationship and its implications on predictions




    Hey guys,
    I have a question regarding The simultaneous relationships and the Implications it has on regarding whether the said variable will be able to make predictions.

    Me and my partner ran a standard linear regression model (ols) on two variables, we've received significance regarding the said variable sig<0.01. Now, we've got a weak correlation (about 0.1) between the independent and the dependent variables. And we got an r2 of 0.016. Let me be clear that we did not expect a strong correlation nor a high r2.

    Now, we checked a simultaneous connection between the two variables.
    I conclude that because of the significant results, low r2 and the very very weak correlation there is no reason to believe that this independent variable if lagged, will be able to predict the dependent variable (not lagged) because of the conditions said. Also, I believe we will not get higher results than we did with the unlagged independent variable. Because if the independent variable cannot explain and it doesn't have any strong relation and the results are significant when they're measured simultaneously then the chance that it will be able to predict the dependent variable when they're not measured simultaneously are even slimmer.


    My partner believes that we cannot reach such a conclusion regarding the independent variable's power (if lagged) to predict the dependent variable because the independent variable and the lagged independent variable are different variables.

    (We do not want to perform another manipulation other than a lag, meaning, we are wondering whether we can make a general conclusion)
    The research and variables are related to financial measures and the stock market]

    Can we reach the conclusion that I've reached from the data or is my partner correct?
    Last edited by orenz10; 04-14-2017 at 08:28 AM.

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