Autoeconometrics modeling method

• US 8,606,550 B2
• Filed: 02/11/2009
• Issued: 12/10/2013
• Est. Priority Date: 02/11/2009
• Status: Active Grant

• Current Assignee: Johnathan C. Mun
• Original Assignee: Johnathan C. Mun
• Inventors: Mun, Johnathan C.
• Primary Examiner(s): PATEL, SHAMBHAVI K
• Application Number:
  US12/378,168
• Publication Number:
  US 20100204967A1
• Time in Patent Office: 1,763 Days
• Field of Search: 703/2, 705/10, 705/14.52, 705/348
• US Class Current: 703/2
• CPC Class Codes: G06F 7/60 Methods or arrangements for performing computations using a digital non-denominational number representation, i.e. number representation without radix; Computing devices using combinations of denominational and non-denominational quantity representations , e.g. using difunction pulse trains, STEELE computers, phase computers conversion of digital data to or from non-denominational form H03M5/00, H03M7/00
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

A method and system allowing the ability to automatically and systematically run thousands and even millions of combinations and permutations of regression, forecasting and econometric trials to determine the best-fitting predictive model.

Claims

1. A non-transitory computer-readable medium that stores computer-executable instructions that are executable by a computer processor, the instructions when executed embodying a method for modeling various combinations of econometric regression analysis that comprises:

using a computer processor to store, in a non-transitory computer-readable medium, a plurality of customized and flexible financial options and a plurality of methods for autoeconometric modeling;

presenting to a user, via a software module stored on said tangible computer-readable medium, an interface for entering a dataset into a data grid;

receiving input from said user, wherein said input is comprised of an input dataset for insertion to said data grid, a threshold value and a set of one or more user selected options,

wherein said one or more user selected options are comprised of one or more of a linear model, a nonlinear model, a time-series lag and an autoregressive lag,

wherein said input dataset is comprised of a plurality of user provided variables;

enumerating a plurality of econometric variables from said plurality of user provided variables,

wherein said plurality of econometric variables is greater in total number of variables than said plurality of user provided variables;

assigning a fitness rating for said plurality of econometric variables via analysis routines;

associating a p-value to each variable in said plurality of econometric variables;

storing only said p-values in memory;

eliminating one or more variables from said plurality of econometric variables as insignificant, wherein said one or more variables have a p-value greater than said threshold value;

reanalyzing said plurality of econometric variables via econometric analysis routines;

generating one or more final model reports;

ranking said one or more final model reports by an R-Square coefficient; and

displaying said one or more final model reports in order of ranking to said user.

2. The system of claim 1, wherein said reanalyzing step utilizes a quick heuristic model.

3. The system of claim 1, wherein said R-Square is an adjusted R-Square coefficient.

4. The system non-transitory computer-readable medium of claim 1, wherein said econometric analysis include a bypass procedure.

5. The system non-transitory computer-readable medium of claim 1, wherein said econometric analysis include a linear and nonlinear routine.

6. The system non-transitory computer-readable medium of claim 1, wherein said econometric analysis include a time-series lag routine.

7. The system non-transitory computer-readable medium of claim 1, wherein said econometric analysis include an autoregressive lag routine.

8. The system non-transitory computer-readable medium of claim 1, wherein said econometric analysis include a linear &

nonlinear interlacing routine.

9. A computer implemented method for modeling various combinations of variables via econometric regression analysis:

using a computer processor to store, in a non-transitory computer-readable medium, a plurality of customized and flexible financial options and a plurality of methods for autoeconometric modeling;

presenting to a user, via a software module stored on said tangible computer-readable medium, an interface for entering a dataset into a data grid;

receiving input from said user, wherein said input is comprised of an input dataset for insertion to said data grid, a threshold value and a set of one or more user selected options,

wherein said one or more user selected options are comprised of one or more of a linear model, a nonlinear model, a time-series lag and an autoregressive lag,

wherein said input dataset is comprised of a plurality of user provided variables;

enumerating a plurality of econometric variables from said plurality of user provided variables,

wherein said plurality of econometric variables is greater in total number of variables than said plurality of user provided variables;

assigning a fitness rating for said plurality of econometric variables via econometric analysis;

associating a p-value to each variable in said plurality of econometric variables;

storing only said p-values in memory;

eliminating one or more variables from said plurality of econometric variables as insignificant, wherein said one or more variables have a p-value greater than said threshold value;

reanalyzing said plurality of econometric variables via econometric analysis;

generating one or more final model reports;

ranking said one or more final model reports by an R-Square coefficient; and

displaying said one or more final model reports in order of ranking to said user.

10. The method of claim 9, wherein said reanalyzing step utilizes a quick heuristic model.

11. The method of claim 9, wherein said R-Square is an adjusted R-Square coefficient.

12. The method of claim 9, wherein said econometric analysis include a bypass procedure.

13. The method of claim 9, wherein said econometric analysis include a linear and nonlinear routine.

14. The method of claim 9, wherein said econometric analysis include a time-series lag routine.

15. The method of claim 9, wherein said econometric analysis include an autoregressive lag routine.

16. The method of claim 9, wherein said econometric analysis include a linear &

nonlinear interlacing routine.