Econometrics

• • Fedora Core is my favorite Linux distribution

  • R for simple data manipulation, summary statistics, simple regressions

  • List of all packages

    • Computational econometrics packages:linear regression models, microeconometrics, nonparametric methods, linear models for panel data, GMM, time series, etc.

  • Optimization and Mathematical Programming

  • Empirical Finance

    • Contributed documentation(manuals)

  • Java for everything else where speed is important: estimation of demand, dynamic games

    • • java.sun.com for Java foundation (compiler, libraries, executor)

      • Parallel Colt is a multithreaded version of Colt - a library for high performance scientific computing in Java. It contains efficient algorithms for data analysis, linear algebra, multi-dimensional arrays, Fourier transforms, and statistics.

      • JAMA for matrix library

      • Ports of Fortran optimization libraries (UNCMIN, MINPACK) at FPL Statistics Group

      • IPOPT -optimizer for large scale optimization problems (including constraint optimization problems)

      • The Apache Commons Mathematics Library - It contains classes for numerical analysis, statistics, optimization, graphics, etc.

  • MySQL for advanced data manipulation and large databases. It works very well with R and Java.

  • Stata for simple regressions when I do not have R

Why R?

• • Cost: Free!

  • Superior graphics

  • Object orientation

  • Reproducibility

  • Excellent programming support

Why Java?

• • Cost: Free!

  • Excellent documentation

  • Object-oriented

  • Automatic memory allocation

  • Dynamic optimization of execution paths

  • Designed from day one to be networked, multithreaded, write-once-run-anywhere cross-platform

Update January, 2018 (extra job)

I am working on a Java package (eio) for structural econometrics based Parallel Colt and The Apache Commons Mathematics Library.  Features:

• • Easy work with panel data in Java (summary statistics, data selection, merging data ) 

  • OLS, 2SLS, GMM estimation

  • Logit, multinomial logit estimators

  • B-Splines

  • Chebyshev interpolation (using vectors and matrices of points)

  • Classes for approximate dynamic programming (large state space)

  • Function interpolation

  • Demand estimation (Nested Logit)

  • Productivity estimation

  • Different classes that are useful when estimating dynamic games

  • Jacobian Matrix

  • Numerical Solvers (Broyden, Newton, Over-identified large system of equations)