I am working on an analysis for a highly competitive agriculture market. I'm looking at county elevators in Iowa where competitors set their price for tomorrow's corn "ask" the evening before, but after the market closes. The markets close around 4PM here and the elevators set their prices around 7PM. There are 5 competitors in this market. As an example, if the price increases between t & t-l (where t is today's closing price) is \$0.50 per bushel at a regional market, each elevator will raise their asking price for bushels by some percentage of the increase. That amount has an upper limit to prevent arbitrage, but for simplicity's sake, assume they can capture the full \$0.50, but often don't choose to because they want to sell the most corn possible and not price themselves out of the market. My goal is to understand how competitors have behaved historically. Have they tried to capture the full price increase or did they capture a smaller percentage of the increase?
I took the analysis over from a colleague who tried regular regression to model the data. As a starting point, I'm using two-stage least squares since there are some real endogeneity issues. However, are there any better tools which could be useful? The once which comes to mind is a random forest because of the relative importance tools, but is there a way to extract actual percentages? I'm careful to not use the word coefficients. Thanks in advance.