A Data Science Central Community

Over the last two months in particular, I have become interested in the integration of Low Dimensional Factor Modelling with Structural Modelling (nowadays referred as DSGE or Dynamic Stochastic General Equilibrium (models)). You can see many references to my growing interest in this topic in this blog and in my corporate blogs over on; http://www.asymptotix.eu/blogs

The recent publication of the Spring Forecasts by the Directorate General, Economic and Financial Affairs (DG ECFIN) of the European Commission gives me an idea on how to show how this might be put into practice to support stress testing in a European Financial institution (as it were on a micro-prudential basis), I have blogged this idea as a comment on our corporate news item about the Spring Forecasts here;

http://www.asymptotix.eu/content/european-commission-economic-and-f...

A comment which would be particularly relevant here, is that on testing this idea in my REvolution R cockpit last evening (with one ear on the activities at Stamford Bridge!) there develops a certain rule set around the kind of LDFM approach you need to estimate economic risk capital based upon the use of series from a model such as ECFIN's AMECO; your models need to be; non-linear, dynamic & over as long a time period as possible (observation frequency is less important, my current testing is telling me) but the challenge is how do you integrate a monetary aggregate into the essentially real-economy oriented DSGE variables, which monetary aggregate? Well for that you need to go back to the theory and pick the approach which best fits your view from all of the papers I have blogged here. Simple really. We will make this mainstream yet! [HINT: Term Structure & as aggregate an M-number as you can find] Oh yes and a Stock Exchange Index helps, I like the DAX; I like the DAX as a global indicator! Problem then is how low is low?

The recent publication of the Spring Forecasts by the Directorate General, Economic and Financial Affairs (DG ECFIN) of the European Commission gives me an idea on how to show how this might be put into practice to support stress testing in a European Financial institution (as it were on a micro-prudential basis), I have blogged this idea as a comment on our corporate news item about the Spring Forecasts here;

http://www.asymptotix.eu/content/european-commission-economic-and-f...

A comment which would be particularly relevant here, is that on testing this idea in my REvolution R cockpit last evening (with one ear on the activities at Stamford Bridge!) there develops a certain rule set around the kind of LDFM approach you need to estimate economic risk capital based upon the use of series from a model such as ECFIN's AMECO; your models need to be; non-linear, dynamic & over as long a time period as possible (observation frequency is less important, my current testing is telling me) but the challenge is how do you integrate a monetary aggregate into the essentially real-economy oriented DSGE variables, which monetary aggregate? Well for that you need to go back to the theory and pick the approach which best fits your view from all of the papers I have blogged here. Simple really. We will make this mainstream yet! [HINT: Term Structure & as aggregate an M-number as you can find] Oh yes and a Stock Exchange Index helps, I like the DAX; I like the DAX as a global indicator! Problem then is how low is low?

© 2019 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge