I am a PhD student working with panel data and I am writing to ask because I am really confused whether I should use PCA to measure my CEO greed measure (which is an independent variable when looking at it's effect on firm performance or moderator when looking at its effect on the entrepreneurial orientation and firm performance relationship). In the paper, When More Is Not Enough: Executive Greed and Its Influence on Shareholder Wealth published in journal of management published by Haynes et al. 2014, that I am following which they also have panel data they measured CEO greed as a result of PCA of three proxies. I ran PCA using SAS and I got factors as well as eigenvalues for each of the three proxies, should I multiply this factor by each original standardised variable value and sum them up to get the final CEO greed measure to use in the regression (fixed-effect panel data regression)? Or do I multiply the eigenvalues by the original standardised variable to get the final CEO greed measure in the regression? I discussed this with my professor and I noticed in one of your replies that you noted the same issue my supervisor told me which is that by using an index as a result of PCA you lose the variations that might be seen by each proxy. However, what if the proxies are highly multi collinear after I run the correlation matrix then I cannot put them in the final fixed effect regression equation as separate variables? Also how can perform a PCA in panel data? Do I get separate PCA values for each firm in each year? Or a value to use for all firms in all years? Could you please help me. Below is the code I wrote. Thank you /*Principal component analysis for firm size*/ PROC Princomp DATA=year.mergedind simple METHOD=Prin PRIORS=one mineigen=1 ROTATE=varimax round SCREE CORR MSA RES; var firm_size1 firm_size2 firm_size3; by gvkey year; Run; /*Factor multiplication by each variable of the overall construct firm size*/ Data year.mergedind; Set year.mergedind; F1_size=sum(*firm_size1, *firm_size2, *firm_size3); Run;
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