Identification and Estimation of Cost Functions Using Observed Bid Data: An Application to Electricity Markets

64 Pages Posted: 24 Mar 2001 Last revised: 20 Nov 2022

See all articles by Frank Wolak

Frank Wolak

National Bureau of Economic Research (NBER)

Date Written: March 2001

Abstract

This paper presents several techniques for recovering cost function estimates for electricity generation from a model of optimal bidding behavior in a competitive electricity market. Two techniques are developed based on different models of the price-setting process in a competitive electricity market. The first assumes that the firm is able to choose the price that maximizes its realized profits given the bids of its competitors and the realization of market demand. This procedure is straightforward to apply, but does not impose all of the market rules on the assumed price-setting process. The second procedure uses the assumption that the firm bids to maximize its expected profits. This procedure is considerably more complex, but can yield more insights about the nature of the firm's variable costs, because it allows the researcher to recover generation unit-level variable cost functions. These techniques are applied to bid, market outcomes and financial hedge contract data obtained from the first three months of operation of the National Electricity Market (NEM1) in Australia. The empirical analysis illustrates the usefulness of these techniques in measuring actual market power and the ability to exercise market power possessed by generation unit owners in competitive electricity markets.

Suggested Citation

Wolak, Frank A., Identification and Estimation of Cost Functions Using Observed Bid Data: An Application to Electricity Markets (March 2001). NBER Working Paper No. w8191, Available at SSRN: https://ssrn.com/abstract=264446

Frank A. Wolak (Contact Author)

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
137
Abstract Views
2,121
Rank
394,958
PlumX Metrics