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Your question is not particularly clear - but let me try to give some elements that may help: Think about why do you want to convert prices into the same currency? Pure stock market performance is usually evaluated using Local currencies. You can compute daily, weekly or monthly returns (that is % price increases) for each stock index and then do whatever ...


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Yes, you can run diff-in-diff with aggreggate treatment in this case. Yes, standard errors should be clustered at the treatment (here=state) level. No, the number of clusters is not an issue, as the relevant number is the total number of clusters and 50 should reasonably avoid the small cluster problem. The way you can think about this is to forget states ...


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1 gives you the percentage point (p.p.) difference, 2 gives you the percentage difference. There is no rule that says that if you're dealing with shares, you have to use 1. The reason 1 is preferred is that, when the measurement unit is in percentage, things get confusing quickly (e.g. going from a 2% to a 1% interest rate is a 50 percent reduction in the ...


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Using clustered standard errors in Stata doesn't impose any additional restrictions on your coefficient estimates. The coefficient estimates are independent of the vce() option you choose. If you have reason to believe your standard errors should be clustered, it means you think that there is some association between the cluster variable and your outcome. If ...


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By including both regressors, you are effectively trying to find the partial effect of either the temperature level, or variance, on crop yield. That is, the effect of the one, after holding the other constant. Does that approach make sense? Do you need to know the effect of a rise in temperature, for a constant variance in temperature? Or, do you need to ...


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It's generally not good practice to go from categorical/dummy variables to quantitative variables, as csilvia noted, because categorical/dummy variables have less information than their quantitative analogs. None of the variables in your dataset are quantitative except for math, reading, writing, and total, which I'm assuming are outcomes of interest, not ...


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A common technique we use in Python through Pandas is the introduction of means, medians and modes (depending on the structure of data) in the stead of these missing values. Another great technique is introducing fitted values by a regression model, thus, predicting the missing values. In this case you'd want a command to drop your NaNs, estimate a model, ...


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