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Deposing an Econometrics Expert

Deposing an Econometrics Expert. Presentation to Boston Bar Association Business Litigation Committee by Roy J. Epstein, PhD Expert economic analysis for complex litigation Adjunct Professor of Finance, Boston College April 9, 2008. What is Econometrics?.

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Deposing an Econometrics Expert

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  1. Deposing an Econometrics Expert Presentation to Boston Bar Association Business Litigation Committee by Roy J. Epstein, PhD Expert economic analysis for complex litigation Adjunct Professor of Finance, Boston College April 9, 2008

  2. What is Econometrics? • Combines economic theory, data, and statistical methods • Mainstream tool in legal proceedings • Generates formulas to show causation (liability) and to estimate damages • E.g., did release of a pollutant lower property values and, if so, by how much

  3. Most Common Econometric Model—Linear Regression • Predicts “dependent” variable in terms of one or more “explanatory” variables, e.g.: Crop Yield = 5*Rain + 2*Fertilizer • Coefficients of 5 and 2 “best fit” the rain and fertilizer data to crop yield • Sorts out individual effects of multiple causal factors, e.g.,: • 5 bushels per additional inch of rain • 2 bushels per additional ton of fertilizer

  4. Principal Outputs from Linear Regression • Estimated value of each coefficient in the regression equation • Test of “statistical significance” of each estimated coefficient • Not significant means a coefficient is statistically indistinguishable from zero, regardless of value actually obtained

  5. Clash of Models • For same alleged conduct and facts: • Expert for one side typically finds large and statistically significant coefficients • Expert for other side typically finds small and/or statistically insignificant effects

  6. How Econometric Experts Reach Opposite Conclusions • Different results usually due to combination of: • Using different explanatory variables • Using different data • Using different statistical procedures • Deposition must explore each area

  7. If You Could Ask Only a Single Question at the Deposition • “What did you do to establish the reliability of your results?”

  8. Deposition Step 1—Discovery • Opposing expert’s backup materials • Raw data and/or identification of exact sources • Details of all data manipulations • All regression runs, graphs, and other data analyses considered • Allow adequate time for your expert to replicate/review

  9. Deposition Step 2—Planning Your Questions • Opposing expert’s results usually sensitive to assumptions involving choice of variables, data, and estimation procedures • Work with your expert in advance • Identify key assumptions • Know effect of adopting alternative assumptions • Questions should probe basis for opposing expert’s choices

  10. Deposition Step 3—General Topics to Cover

  11. Estimated Coefficients • Algebraic sign • Effect of explanatory variable in “right” direction? • Magnitude • Implausibly large or small? • Statistical significance • Did expert use 95% confidence interval?

  12. Variables • Selection of explanatory variables • How many different models were estimated? How were they different? Did any yield contrary results? • What did expert do to establish chosen model was more reliable than alternatives considered?

  13. Data • Reliability of data sources • Procedures used to construct data • Rationale for grouping of transactions (transaction, plaintiff, all customers, product, industry) • Rationale for time period chosen • Checks/controls for outliers (atypical data points)

  14. Estimation Procedures • Ordinary Least Squares (“OLS”) most widely used procedure but inappropriate in certain situations • Adjustments may be needed for reliable coefficient estimates • Tests exist to assess whether alternative procedures should be used • Did the expert use them?

  15. Case Studies

  16. 1) General Use of Regression: Ivy League Financial Aid Antitrust Litigation

  17. Assessing Market Impact of Alleged Conduct • DOJ sued MIT and Ivy League schools for colluding on financial aid awards • Key issue: did challenged practices have anticompetitive effect? • MIT used econometric model to analyze prices charged by national sample of schools • No evidence that alleged conduct raised prices

  18. The Model • Dependent variable: average price (tuition + room and board) by school • 14 explanatory variables to account for different school characteristics • No price effect of alleged collusion: • Controlling for other factors, MIT and Ivys charged $322 less than other schools • But effect not statistically significant, therefore indistinguishable from zero

  19. 2) Assumptions about Explanatory Variables: Estimating Profits in a Damages Claim [a case last year in which Dr. Epstein was involved]

  20. Different Models for Profit Analysis • Defendant produced two products, A and B • Defendant: overhead expenses caused by total sales (1 explanatory variable) • Plaintiff: separate effects on overhead from products A and B (2 explanatory variables)

  21. Importance of Choice of Explanatory Variables • Defendant: each $1 increase in total sales adds $0.40 in overhead (and statistically significant) • Plaintiff: sales of B have no statistically significant effect on overhead • Profitability of product B: • Zero under defendant theory • Substantial under plaintiff theory

  22. 3) Data Reliability (or Lack Thereof): the Conwood Case

  23. Conwood v. US Tobacco • Plaintiff analysis relies on extreme data outlier • $1 billion claimed damages, after trebling • Sustained after review by Supreme Court

  24. Data Outlier Skews Regression Result Washington, DC

  25. Informative Legal Decisions

  26. Selected Cases that Discuss Quality of Econometric Evidence • Freeland v. AT&T Corp., 238 F.R.D. 130 (S.D.N.Y. 2006) • Issues: omitted explanatory variables, misuse of average prices • In Re Methionine Antitrust Litigation (West Bend Elevator, Inc. v. Rhone-Poulenc), 2003 U.S. Dist. LEXIS 14828 (N.D. Cal., August 26, 2003) • Issues: omitted explanatory variables, irrelevant data, improper/insufficient time period, improper estimation procedure • Johnson Electric v. Mabuchi Motor America, 103 F. Supp. 2d 268 (S.D.N.Y 2000) • Issues: unreliable data, implausible magnitudes of coefficients

  27. Summary • Most econometric models sensitive to one or more assumptions regarding: • Choice of explanatory variables • Appropriate data • Estimation procedure • Regression results not reliable until sensitivities identified and explained • Deposition must address basis for opposing expert’s assumptions

  28. For Further Information… Roy J. Epstein, PhD Expert economic analysis for complex litigation 1280 Massachusetts Ave., 2nd Fl. Cambridge, MA 02138 rje@royepstein.com (617) 489-3818 Adjunct Professor of Finance, Boston College

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