We use data from the Portland Fish Exchange of Portland, Maine, to illustrate how to estimate a…

We use data from the Portland Fish Exchange of Portland,
Maine, to illustrate how to estimate a demand function using regression
analysis. At the Exchange, fishing boats unload thousands of pounds of cod and
many other types of fish on a daily basis. The fish are weighed and put on
display in the Exchange’s 22,000-square-foot refrigerated warehouse. Buyers
inspect the quality of the day’s catch. At noon, an auction takes place in a
room overlooking the fish in the warehouse. Fleece-clad buyers representing
fish dealers and restaurants sit facing a screen, an auctioneer, and
representatives
»

We use data from the Portland Fish Exchange of Portland,
Maine, to illustrate how to estimate a demand function using regression
analysis. At the Exchange, fishing boats unload thousands of pounds of cod and
many other types of fish on a daily basis. The fish are weighed and put on
display in the Exchange’s 22,000-square-foot refrigerated warehouse. Buyers
inspect the quality of the day’s catch. At noon, an auction takes place in a
room overlooking the fish in the warehouse. Fleece-clad buyers representing
fish dealers and restaurants sit facing a screen, an auctioneer, and
representatives of the sellers. A screen at the front of the room shows bid
information. Thousands of pounds of fish sell rapidly. The quantity of fish
delivered each day, Q, varies due to fluctuations in weather and other factors.
Because fish spoils rapidly, all the fish must sell immediately. Therefore, the
daily auction price for cod adjusts to induce buyers to demand the amount of
cod available on that particular day. The price-quantity combination for cod on
any particular day represents a point on the cod demand curve. For each day it
operates, the Portland Fish Exchange reports the quantity (in pounds) of each
particular type of fish sold and the price for that type of fish. For example,
on June 8, 2015, 2,946 lb of large white hake sold for $0.97 per lb. A
collection of such observations provides a data set that can be used for demand
analysis using regression. Here, quantity is the explanatory variable. Whatever
quantity comes to market sells that day. Price adjusts to ensure that the
amount of fish available meets the buyers’ demand. As the quantity brought to
market “explains” the price, we estimate an inverse demand curve.

»