- hlmmc In the late summer of 2012, I taught a graduate-level class in Hierarchical Linear Models (HLMs, sometimes called Multilevel Models) at the University of Tennessee (UT-Knoxville). The hlmmc package was developed for the course to facilitate Monte Carlo studies (computer-intensive methods for the numerical analysis of messy problems) and teaching HLMs. More information about hlmmc (also a public domain package) is available here. I am also serializing my course lectures (here). The lectures were essentially demonstrations of the hlmmc package.
- ws This package allows state-space time series models I have developed to be simulated, re-estimated or extended. I have used the ws package to develop models of the world system (WL20), the US economy (USL20), other countries in the world system and various forecasting models (see examples of the forecasting models here and the US Stock Market models here). More information about the ws package is available here.
I use another set of routines to develop the models made available with the ws package. These routines select the best model using the AIC criterion from a number of models starting with a Random Walk as the null hypothesis. I believe that the routines I am currently using could be simplified for general use, but there is still some testing that needs to be done to make sure the simplified functions generate similar "best" models. For 2013, I should be able to complete the rw package, depending on how the testing goes.
As I developed this software, I realized that the I needed another outlet to develop the theory and rationale underlying the programs. In 2012, I created a number of other blogs for this purpose. As time permits in 2013, I will post results to these blogs:
- Economic Bubble Machine The world-system models can be used not only to produce forecasts but also to do counterfactual analysis. In a counterfactual analysis, we change the model parameters or something about how the model works and then run a new "fictional" simulation to generate counterfactual history. In the Economic Bubble Machine blog I will explore the use of counterfactual history to identify economic bubbles such as the Great Depression or the Subprime Mortgage Crisis.
- World System Conjectures Since I am developing world-system models, I need a place to test the models against world-system theory. The World System Conjectures blog will be devoted to that effort in 2013. The project involves historical analysis with quantitative models.
- Causal Macrosystems Finally, the Subprime Mortgage Crisis and the IPCC Emission Scenarios have pointed out the weaknesses of current quantitative macrosocietal models. During the current financial crisis, large-scale econometric models did not seem to provide enough (or any?) short-term early warning about potential problems. The IPCC emission scenarios that attempt to link climate change to human activity challenge the other extreme, that is, the ability of quantitative models to predict decades into the future. The Causal Macrosystems blog will expand some early attempts (here and here) to evaluate state-space models against their competitors.
NOTE: My major area of interest involves state space models and the application of systems theory to statistics, computer simulation and societal development. I have been working in this area since the late 1970's (here). For many years I had used my own software package to do state-space analysis. It was a collection of Fortran subroutines (see John Nash's book Compact Numerical Methods for Computers plus other routines from other sources) driven by an interpreted, stack-based RPN (Reverse Polish Notation) language similar to Postscript and also written in Fortran (based on A. Colin Day's Book Fortran Techniques: With Special Reference to Non-numerical Applications). When R became available in the late 1990's, I started writing the routines I am currently using in the R language.
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