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  • Conducting a joint test on signs of estimated coefficients after regression

    Dear all:
    How do I conduct a joint test for testing the signs of two of the regression coefficients after running a regression?

    If my model is Y = β0 + β1 X + β2 Y + ...+ ε, I'd like to conduct the joint test (β1 > 0) & (β2 < 0).

    Unfortunately, the test command does not allow inequalities. I'd love to hear your suggestions on how to conduct such a test.


  • #2
    joint test the two are zero. look at the signs.

    I suppose you could use the one-tailed test probability (1/2 the probability given). (look at ttest as an example.)

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    • #3
      HTML Code:
      https://www.stata.com/support/faqs/statistics/one-sided-tests-for-coefficients/#:~:text=If%20you%20wish%20to%20test,coefficient%20is%20equal%20to%20zero.&text=The%20Wald%20test%20given%20here,71%20denominator%20degrees%20of%20freedom.

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      • #4
        Thanks, George, for your response.

        I'm not sure if I could run two t-tests, one for each coefficient, since the information I seek is about the joint distribution of b1 and b2, which are estimates of β1 and β2 respectively. Perhaps the picture below, which shows a confidence region for b1 and b2,will help?

        Click image for larger version

Name:	Joint distribution.png
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        Presumably, under the standard Gauss-Markov assumptions, the joint distribution of b1 and b2 is F-distributed. To do the computation myself, I need the conditional distribution of b1 given b2 (or the other way around) or the joint distribution itself. I was wondering if Stata has a built in command that yields the p-value for the joint "one-sided" test.

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        • #5
          Upon reflection, I think I might have answered my own question. Based on the figure below, if the one-sided p-value for b1 is p1 and the one-sided p-value for b2 is p2, then would the p-value for the joint test not simply be p1 +p2?
          Click image for larger version

Name:	Joint distribution2.png
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          If this reasoning is correct, then the p-value I seek is merely the arithmetic mean of the p-values of b1 and b2, as reported by the regression output.
          Last edited by Gautam Sethi; 21 Mar 2024, 16:22.

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