Economics and quantitative methods

Computational tools are an inescapable component of modern economic research. I have contributed to a number of open-source software projects to improve freely available time series econometrics software. These contributions include:

Kalman Filter SARIMAX Unobserved Components Diagnostics VAR, Dynamic Factors Metropolis-Hastings
| May, 2014 | August, 2014 | April, 2015 | June, 2015 | July, 2015 | August, 2015

Statsmodels: State space models and the Kalman filter

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Summary: I contributed a module to the Statsmodels project which allows (1) specification of state space models, (2) fast Kalman filtering of those models, and (3) easy estimation of parameters via maximum likelihood estimation. See below for details.

For a longer description of these types of models, a discussion of the implementation in Statsmodels, and example code, see the following link (note: the content at this link used to be on this page): Implementing state space models for Statsmodels

For more information about state space models in Python::

Statsmodels: Markov switching dynamic regression and autoregression

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Summary: I contributed MarkovRegression and MarkovAutoregression classes to the Statsmodels project allowing maximum likelihood estimation of these classes of models.

For more information about these Markov switching models:

Scipy: Wishart random variables and sampling

View Scipy documentation

Summary: I contributed wishart and invwishart classes to the Scipy project allowing evaluation of properties of these random variables (PDF, entropy, etc.) as well as the drawing of random samples from these distributions. These can be useful in a variety of settings, including Gibbs sampling approach to estimating covariance matrices in state space models.