# Cook's Distance result: Does not make much sense

I am performing regression on the financial data

(dependent variable is the MSCI AC World Financials and independent variables are the MSCI North America Financials, MSCI Europe Financials, MSCI Pacific Financials, and MSCI Emerging Markets Asia Financials).

The data is here: see the excel file online

Then I use Python to calculate cook's distance

x_data = my_data[['MSCI N AMERICA FINANCIALS', 'MSCI EUROPE FINANCIALS', 'MSCI PACIFIC FINANCIALS', 'MSCI EM ASIA FINANCIALS']]
y_data = my_data['MSCI WORLD FINANCIALS']
model = sm.ols(endog = y_data, exog = x_data)
fitted = model.fit()
influence = fitted.get_influence()
(c,p)=influence.cooks_distance
plt.stem(np.arange(len(c)), c, markerfmt=",") I was expecting the 32nd data point (9/30/2002) to have a high Cook's distance because that's when the market lost its value most compared to other dates (the dependent variable was -0.14559 while the independent variables were -0.12523, -0.23832, -0.02796, and -0.16854). But it seems like, based on the picture, the most influential data point was the 7th one (8/31/2000), which does not make much sense to me.

I would greatly appreciate it if someone could explain the intuition behind this?

Thank you very much.

In this case, if you check the standard residual, the 32nd point is well within $\pm$1 standard deviation. It's well predicted. While the 7th point is -2 SDs away.