For this assignment, I am trying to find the covariates of lumber and test the lead-lag relationships.
To test, I am thinking to do a linear regression and see if the two are correlated using either prices or returns (both would be ideal). You can use the close price ($). If expert feels something like an ANCOVA is necessary, that is also fine.
Also check to see if one leads/ lags the other.
Please run the regression for those against Lumber (IE you don’t need to run a linear regression on corn vs oil, but run it on corn vs. lumber and oil vs. lumber). Thank you!
CL=F is oil, ZC=F is Corn, HG=F is copper, LBS=f is Lumber, ETH is Ethereum (the cryptocurrency, GC=F is gold, and ALI=f is aluminum.
The last thing I want to test as a covariate is number of homes sold and the median price of those homes sold. I am unsure where to get that data – but maybe zillow, FRED, or census may have it. Let me know if expert is able to get this data. The test here would be to see if there is a correlation between lumber prices and number of homes sold, or lumber price and median price of the homes sold. I know the housing data only comes monthly - so I included LBS=Fmonthly as the monthly data. All the other data sets are daily. Thank you!
Please include interpretation for all of the results, as well as a conclusion of which is the best covariate, and whether there is a lead-lag relationship between any of them.