The invention of PCAIDS (Proportionality Calibrated Almost Ideal Demand System) by Roy Epstein and Dan Rubinfeld stands at the forefront of current applications of merger simulation.
The only data requirements for PCAIDS are market shares and two price elasticities: the elasticity for a single brand in the market and the industry price elasticity. Scanner data and econometric estimation, while useful, are not necessary. These features make it possible to perform simulation in nearly any transaction at relatively low cost.
Nests are an important extension to PCAIDS to generalize the proportionality assumption. See the article by Dr. Epstein and Prof. Rubinfeld in Advances in Economic Analysis & Policy for a detailed explanation of empirical implementation of the nested model.
For questions about implementation generally and other technical issues that are not addressed here, please email pcaids@royepstein.com.
Related Articles by Roy J. Epstein
"Merger Simulation with Brand-Level Margin Data: Extending PCAIDS with Nests." With Daniel L. Rubinfeld. Advances in Economic Analysis & Policy: Vol. 4: No. 1, Article 2. (UC Berkeley Electronic Press).
"Merger Simulation: A Simplified Approach with New Applications." With Daniel L. Rubinfeld. Antitrust Law Journal 69 (3), 2002, pp. 883-919.
"Merger
Simulation and Unilateral Effects: A Primer for Antitrust Lawyers." American Bar Association, Section of Antitrust Law, Economics Committee Newsletter 2(2), Fall 2002, pp. 3-6.
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Italian Competition Authority uses PCAIDS. See discussion of merger simulation in the Fondiaria-SAI decision.
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New Zealand Commerce Commission uses PCAIDS. See discussion of merger simulation in the Cendant decision.
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Recent adopters of PCAIDS methods and software include: New Zealand Commerce Commission; Italian Competition Authority; UK Dept. of Trade and Industry; Cyprus National Competition Authority; Turkish Competition Authority; Massachusetts Institute of Technology Dept. of Economics; University of Otago (NZ).
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| PCAIDS TECHNICAL SUPPORT NOTES |
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Many readers of our PCAIDS article in the ALJ have asked about details of implementation. In terms of software, I have developed my version as an Excel add-in, which can handle all of the computations and is very convenient for many users. Others have told me they are using Mathlab, Mathematica, and even PERL. Any package with an optimization routine should be fine.
The most common issue so far is how to evaluate the FOC equation (A3). You must use post-merger values. The easiest way to do this is to update the shares using equation (1) to get s(new)=s(pre-merger)+ds. You can then generate new elasticity matrices E1...E* using equations (4) and (5). Remember that the E matrices are transposes in the appendix.
You should certainly explore the power of PCAIDS with nests. The Appendix discussion of nests has two minor typos--the last sentence in section 4.B. should read "the familiar si/sj." Also, the equation for bij at the top of page 918 should have a leading minus sign. There is also a minor typo in the derivation of (A5), the conclusion is correct but the line above it should replace the first two terms in the parentheses with the product bij/pj PQ/pi.
There was a slip in the example of entry on p. 909. The predicted price increase for B should be 4.5%. The corresponding value of alpha is .061, implying a threshold share for the entrant of 0.26%. I am indebted to Dave Schmidt at the FTC for pointing this out.
Table 3 (toilet paper shares) was scrambled in the ALJ version of the paper. Table 3 should read
| Brand |
Share
(%) |
| Scot Tissue |
16.7 |
| Cottonelle |
6.7 |
| Kleenex |
7.5 |
| Charmin |
30.9 |
| Northern |
12.4 |
| Angel |
8.8 |
| Private Label |
7.6 |
| Other |
9.4 |
| Total |
100.00 |
The discussion and simulation results use the correct data.
