ICE 2008 Student Presentations

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Student Presentations

Solving the Bewley Model with AMPL

Peter Karadi (New York University), Lorenz Küng (UC Berkeley), Moritz Kuhn (University of Mannheim), Matthias Lux (New York University), and Panos Stavrinides (University of Pennsylvania)

• Presentation: Solving the Bewley Model with AMPL
• Code
  • Two ways of calculating the policy function
    • IFP_kink.mod
    • borrow_end.mod
    • borrow_end.com
    • borrow_end.dat
  • Matlab code for calculating the general equilibrium
    • Bewley.m
    • TRP_AR.m

Bundling Software: An MPEC Approach to BLP

Guy Arie (Northwestern University), Oleg Baranov (University of Maryland), Benn Eifert (UC Berkeley), Hector Perez-Saiz (University of Chicago), and Ben Skrainka (University College London)

• Presentation: Bundling Software: An MPEC Approach to BLP
• Code
  • BBGHO.mod
  • BBGHO.cmd.noise
  • BBGHO.dat.noise

ML Estimation of a First-Price Auction Model Using the MPEC Approach

Pamela Cardozo (Queen’s University), Chris Conlon (Yale University), Brent Hickman (University of Iowa), Nicolas Roys (University College London)

• Presentation: ML Estimation of a First-Price Auction Model Using the MPEC Approach
• Code
  • Exponential_auction.md
    • bids24.dat
    • bids50.dat
    • bids150.dat
    • bids450.dat
      • EXPONENTIAL_AUCTION.MD estimates a first-price auction model with iid private values distributed exponentially. The three data sets are in BIDS24.DAT, BIDS50.DAT, BIDS150.DAT and BIDS450.DAT. Each data set contains a sample of equilibrium bids for private values generated from an exponential with parameter theta=2. To run the program, change the data set on line 8 as desired, and modify the solver information on line 66.
  • Fisher-Tippett_auction.md
    • JJ_scaled.dat
      • FISHER-TIPPETT_AUCTION.MD estimates a first-price auction model using the data of Campo, Perrigne and Vuong (2003) and under the assumption that private values are independent and distributed according to a Fisher-Tippett extreme value distribution. To run the program, change the data set on line 8 as desired, and modify the solver information on line 66.
  • ReadMe.txt

An Optimal Rule of Thumb for Pollution Permits Allocation

Evangelina Dardati (University of Texas) and Mar Reguant (MIT)

• Presentation: An Optimal Rule of Thumb for Pollution Permits Allocation
• Code: Set of codes to be used with AMPL with solvers PATH and KNITRO required
  • permit_path.mod
  • permit_knitro.mod
  • permit_ice.txt
  • planner_knitro.mod
  • planner_ice.txt
  • README.txt
• References: Please have a look at Sven Leyffer, Todd Munson and Karl Schmedder’s(1,2,3) tutorials. Model based on “Cournot Game with Learning and Investment” in K. Schmedders slides and “Incorporating oligopoly, CO2 emissions trading and green certificates into a power generation expansion model” by Pedro Linares et al, Automatica.

Portfolio Choice with Borrowing Constraints

Heng Chen (University of Zurich), Fabian Kindermann (University of Würzburg), Robert Sarama (Ohio State University), Daniel Shoag (Harvard University), and Xuan Tam (University of Virginia)

• Presentation: Portfolio Choice with Borrowing Constraints
• Code
  • projectwithrisk.mod
  • simulate.m
  • Data:
    • projectdata_1.dat
    • projectdata_2.dat
    • projectdata_3.dat
    • projectdata_4.dat
    • projectdata_5.dat

Note: To start the project, just execute simulate.m in MATLAB. Datafiles can only be changed directly in the projectwithrisk.modRisk, Diversification, and Growth

Marti Mestieri (MIT), Tarik Ocaktan (Paris School of Economics), Thom Sampson (Harvard University), and David Wiczer (University of Illinois)

• Presentation: Risk, Diversification, and Growth
• Code
  • README.txt
  • kmax.mod
  • sttom.mod
  • st.dat
  • stt.txt