The Society for Computational Economics will sponsor two optional pre-conference workshops for conference attendees. Registration for workshops is available online with conference registration.  The fee is $25 for each full-day workshop. Morning and afternoon coffee breaks are included; attendees will be on their own for lunch. Space is limited and priority will be given to graduate students and recent PhDs (earned November 1, 2013 or later). Non-students may register but will be waitlisted until after April 1, 2014. Those who register and cannot be accommodated will be refunded in full. Both workshops will be held on Saturday, 21 June at BI Norwegian Business School. As of May 20, 2014 everyone who registered for the workshops is confirmed, and space is open for additional attendees in both workshops.

Scientific Computing in Python and Julia

Presented by John Stachurski, Sébastien Villemot and Pablo Winant

Making the right choice of programming language is central to the success of a scientific computing project. Despite their important past achievements, languages like Fortran, C or MATLAB suffer from limitations compared to newly introduced languages. The goal of this workshop is therefore to familiarize the audience with two recent languages, Python and Julia, that offer unique improvements in the trade-off between expressiveness and performance. The workshop will also highlight the use of these languages in economics and finance.

Thanks to its user-friendliness and flexibility, and with the maturation of a complete scientific stack of software, the Python language is becoming increasingly popular in the industry and in many academic fields. Recent development of compilation techniques have also made it a valid contender for writing high performance computing, without requiring a lot of low-level programming.

More recently, and with a more specific focus on scientific computing, a team of researcher created the Julia language that offers modern syntax and expressiveness, high performance (close to compiled languages) and an easy transition from MATLAB. Both languages are free and open-source software. They also have a lively scientific community behind them.

Attendees are expected to bring their laptop. Rudimentary knowledge of programming concepts would be useful but by no way required. The workshop will consist in three hands-on sessions:

- Morning session (John Stachuchski): Python in Economics and Finance
General introduction to Python (syntax, datatypes, ...), and its scientific libraries (scipy, matplotlib, statsmodels, pandas)
- Afternoon session 1 (Pablo Winant): High Performance Computing with Python
Profiling and accelerating Python code by interfacing C libraries (cffi) or by compiling Python code (Cython or numba)
- Afternoon session 2 (Sébastien Villemot): The Julia Language: a fresh approach to scientific computing
Introduction to Julia: syntax, performance, ecosystem, comparison with MATLAB, solving DSGE models

Numerical Methods for Large Scale Dynamic Economic Models

Presented by Lilia Maliar, Stanford University and Serguei Maliar, Santa Clara University

Lilia Maliar and Serguei Maliar will teach a one-day workshop about numerical methods for solving large scale dynamic economic models. Applications include large-scale models of international trade and a medium-scale new Keynesian model. MATLAB codes will be provided to the workshop participants.

Materials. Lilia Maliar and Serguei Maliar, (2013). “Numerical Methods for Large Scale Dynamic Economic Models” in: Schmedders, K. and K. Judd (Eds.), Handbook of Computational Economics, Volume 3, Amsterdam: Elsevier Science (forthcoming).

Abstract. We survey numerical methods that can accurately and reliably solve dynamic economic models with 10-100 state variables using a standard desktop computer and serial Matlab software. (Examples of such models are new Keynesian models, life-cycle models, heterogeneous agents models, asset pricing models, multisector models, multicountry models, and climate change models.) First, we show efficient nonproduct techniques for interpolating and approximating functions (Smolyak, stochastic simulation, and ε-distinguishable set grids), accurate low-cost monomial integration formulas, derivative-free solvers, and numerically stable regression methods. Second, we discuss endogenous grid and envelope condition methods that reduce the cost and increase accuracy of value function iteration. Third, we show precomputation techniques that construct solution manifolds for some models' variables outside the main iterative cycle. Fourth, we review techniques that increase the accuracy of perturbation methods: a change of variables and a hybrid of local and global solutions. Finally, we show examples of parallel computation using multiple CPUs and GPUs including applications on a supercomputer. We illustrate the performance of the surveyed methods using a multi-agent model and a new Keynesian model. Matlab codes will be made available to the workshop participants.

Contents:

1. Nonproduct approaches to representing, approximating and interpolating functions (Smolyak, stochastic simulation, and ε-distinguishable set grids).

2. Nonproduct approaches to approximation of integrals (monomial formulas, Monte Carlo and non-parametric methods).

3. Derivative-free optimization methods.

4. Dynamic programming methods for high-dimensional problems (endogenous grid method and envelope condition method).

5. Precomputation techniques (precomputation of integrals and precomputation of intratemporal choice functions).

6. Increasing accuracy of local (perturbation) methods (techniques of a change of variables and a hybrid of global and local solution methods).

7. Parallel computation on desktop and supercomputers.

8. Numerical examples: multi-agent model and a new Keynesian model.

Prof. Lilia Maliar and Serguei Maliar both obtained their PhD degrees in 1999 from Universitat Pompeu Fabra, Spain. They taught at Stanford University and University of Alicante; Serguei also taught at Leavey School of Business, Santa Clara University. Their joint research on numerical methods for dynamic economic models has appeared in numerous economic journals including Computational Economics, Journal of Business and Economic Statistics, Journal of Economic Dynamic and Control, Journal of Money Banking and Credit, Quantitative Economics, Review of Economic Dynamics; they contributed a chapter to Handbook of Computational Economics; and are currently advising Bank of Canada on the model for the optimal monetary choice.

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