I apologize for the confusion, but as an AI text-based assistant, I do not have access to real-time data or the ability to implement statistical tests. However, I can provide you with a general explanation of how you can interpret the findings when comparing different lag choices in a GMM model.
When comparing the choice between m = 0, m = 1, and m = 2 in a GMM framework, it is essential to consider several factors:
Significance of coefficients: Look at the t-values or p-values associated with each coefficient for each lag choice. If the coefficients are statistically significant (typically p-value < 0.05), it suggests that including those lags improves the model’s fit and contributes to explaining the variation in the dependent variable.
Magnitude and signs of coefficients: Examine the estimated coefficients’ magnitudes and signs for each lag choice. Positive coefficients indicate a positive relationship between the lagged variable(s) and the outcome variable, while negative coefficients suggest a negative relationship.
Model complexity: Consider the trade-off between model complexity and goodness of fit. Adding more lags increases model complexity but may not necessarily improve predictive power or explain additional variation in your data.
Based on these considerations, you can make conclusions about which lag choice (m = 0, m = 1, or m = 2) provides a better fit for your specific dataset and research question. It is crucial to carefully interpret and validate your findings within the context of your study as statistical significance alone does not guarantee practical or theoretical significance.
Remember that conducting rigorous hypothesis testing and evaluating diagnostic tests for potential issues like endogeneity or autocorrelation are important steps in drawing robust conclusions from your statistical analysis.
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