Interaction Effects in Econometrics
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Citations
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References
Law and Finance
Africa's Growth Tragedy: Policies and Ethnic Divisions
Financial Dependence and Growth
Institutions and economic performance: cross‐country tests using alternative institutional measures
Investor Protection and Corporate Valuation
Related Papers (5)
Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations.
Initial conditions and moment restrictions in dynamic panel data models
Frequently Asked Questions (11)
Q2. What is the reason for the large change in the coefficient to the main term?
The large change in the coefficient to the main term is not due to misspecification but it reflects that the coefficient to X1 is to be interpreted as the marginal effect of X1 when X2 is zero.
Q3. What does the authors find to be the strongest result of negative interactions?
Including quadratic terms in the property rights measures seem to strengthen the authors’ main result of negative interactions (although the inclusion of a quadratic term in GDP weakens it).
Q4. What does the study show about the effects of the interaction terms?
The authors find that using Frisch-Waugh residuals strengthens the size and sig-nificance of the interactions; in fact, the interaction of external dependence and equity market capitalization and credit turns from insignificant to clearly significant at the 5- percent level with the expected sign.
Q5. What is the coefficient of the interaction term when estimating equation (1)?
If X21 is part of the correctly specified regression with coefficient δ, the estimated coefficient to the interaction term when estimating equation (1) will be α δ.
Q6. What does the author find to be the strongest evidence of the effect of interaction terms?
Clementi, and MacDonald (2004) hypothesize that strengthening of property rights, as measured by laws mandating “one share-one vote,” “anti-director rights” (which limit the power of directors to extract surplus), “creditor rights,” and “rule of law,” are beneficial for growth and more so when restrictions on capital transactions (capital flows) are weaker where the latter effect is captured by interaction terms.
Q7. What is the way to estimate the coefficient of a quadratic term?
If quadratic terms are not otherwise ruled out, the authors recommend also estimating the specification (4) in order to verify that a purported interaction term is not spuriously capturing left-out squared terms.
Q8. What is the partial derivative of Y with respect to X1?
In this regression, λ1 = ∂Y/∂X1 is the partial derivative of Y with respect to X1, implicitly evaluated at X2 = X2 (the mean value of X2).
Q9. What is the main message of the Castro, Clementi, and MacDonald (2004)?
the point estimates in the Castro, Clementi, and MacDonald (2004) study are not all robust, as one might conjecture from the size of the t-statistics, but the overall message of their regressions appear very robust to the kind of robustness checks that the authors recommend.
Q10. What is the way to determine if a regression with interactions really captures only interactions?
Case 2: if one wants to ascertain that the interaction of X1 and X2 captures no other regressors the safest strategy is to run the following regression model:Y = β0 + β1X1 + β2X2 + β3X ψ 1 X ψ 2 + , (9)where Xψ1 = M2X1 and X ψ 2 = M1X2, M1 = [I − Pβ0,X1 ] and M2 = [I − Pβ0,X2 ] (M1 is a residual maker; regressing X2 on a constant and X1 and M2 is the residual maker; regressing X1 on a constant and X2).
Q11. What is the way to explain the difference in the slope of the interaction term?
In the second column, the authors illustrate how the simple suggestion of subtracting the country-specific means from each variable prevents the interaction term from becoming spuriously significant due to country-varying slopes.