1. Did you test the distribution of the “Financial investment engagement” variable for normality or examine the distribution of residuals from your linear regression models? The reported mean of 0.74 suggests a strong departure from normality, which is a core OLS assumption.
2. Were alternative modeling strategies for count data considered? For example:
– Poisson Regression: A standard starting point for count data.
– Negative Binomial Regression: Particularly appropriate if the data exhibits over-dispersion (variance > mean), which is common in such financial participation data where many households hold zero products.
– Ordered Logit/Probit: Given the limited range (0-4), the variable could also be treated as an ordinal outcome, similar to your treatment of the risk attitude variable.
3. How might the use of a linear model for this outcome variable impact the interpretation of your coefficients (β)? For instance, the coefficient for HPF possession (β = 0.091) is interpreted as a linear increase in the count of products. However, if the relationship is non-linear (e.g., the effect of HPF is stronger for moving from 0 to 1 product than from 3 to 4 products), OLS may misrepresent the true marginal effect.