"Big data" in macroeconomic forecasting: On the usefulness of knowledge discovery in databases

Bernd Brandl

"Big data" in macroeconomic forecasting: On the usefulness of knowledge discovery in databases

The York Management School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Traditional methods in econometrics have problems in analyzing efficiently "big data" or large combinatorial problems respectively. The forecasting profession in macroeconomics frequently deals with problems of that kind in applying various forms of factor analysis. Whereas, as recent literature shows, factor analysis reveals some problems in handling larger data sets. In this paper it is argued how useful the utilization of techniques and methods from the field of knowledge discovery in databases (KDD) can be. Whereas the term KDD emphasizes the aspect of extracting useful information out of "big data" or the solving of large combinatorial problems respectively. In this paper the usefulness of KDD is illustrated in by applying a genetic algorithm and artificial neural networks for the purpose of forecasting macroeconomic aggregates such as industrial production and inflation. Both methods can be categorized as methods from the field of artificial intelligence and are combined in a way that a GA serves as a tool for model selection and an ANN serves for the generation (estimation) of forecasts. It is shown that the utilization of KDD can be effective in forecasting macroeconomic time series.

Original language	English
Title of host publication	PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON MATHEMATICAL METHODS IN ECONOMICS 2004
Editors	L Bauer
Place of Publication	BRNO
Publisher	MASARYKOVA UNIV
Pages	30-35
Number of pages	6
ISBN (Print)	978-80-210-3496-9
Publication status	Published - 2004
Event	22nd International Conference on Mathematical Methods in Economics - Brno Duration: 15 Sept 2004 → 17 Sept 2004

Conference

Conference	22nd International Conference on Mathematical Methods in Economics
City	Brno
Period	15/09/04 → 17/09/04

Cite this

@inproceedings{84943e173de146a3889470f157662f71,

title = "{"}Big data{"} in macroeconomic forecasting: On the usefulness of knowledge discovery in databases",

abstract = "Traditional methods in econometrics have problems in analyzing efficiently {"}big data{"} or large combinatorial problems respectively. The forecasting profession in macroeconomics frequently deals with problems of that kind in applying various forms of factor analysis. Whereas, as recent literature shows, factor analysis reveals some problems in handling larger data sets. In this paper it is argued how useful the utilization of techniques and methods from the field of knowledge discovery in databases (KDD) can be. Whereas the term KDD emphasizes the aspect of extracting useful information out of {"}big data{"} or the solving of large combinatorial problems respectively. In this paper the usefulness of KDD is illustrated in by applying a genetic algorithm and artificial neural networks for the purpose of forecasting macroeconomic aggregates such as industrial production and inflation. Both methods can be categorized as methods from the field of artificial intelligence and are combined in a way that a GA serves as a tool for model selection and an ANN serves for the generation (estimation) of forecasts. It is shown that the utilization of KDD can be effective in forecasting macroeconomic time series.",

author = "Bernd Brandl",

year = "2004",

language = "English",

isbn = "978-80-210-3496-9",

pages = "30--35",

editor = "L Bauer",

booktitle = "PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON MATHEMATICAL METHODS IN ECONOMICS 2004",

publisher = "MASARYKOVA UNIV",

note = "22nd International Conference on Mathematical Methods in Economics ; Conference date: 15-09-2004 Through 17-09-2004",

}

TY - GEN

T1 - "Big data" in macroeconomic forecasting

T2 - 22nd International Conference on Mathematical Methods in Economics

AU - Brandl, Bernd

PY - 2004

Y1 - 2004

N2 - Traditional methods in econometrics have problems in analyzing efficiently "big data" or large combinatorial problems respectively. The forecasting profession in macroeconomics frequently deals with problems of that kind in applying various forms of factor analysis. Whereas, as recent literature shows, factor analysis reveals some problems in handling larger data sets. In this paper it is argued how useful the utilization of techniques and methods from the field of knowledge discovery in databases (KDD) can be. Whereas the term KDD emphasizes the aspect of extracting useful information out of "big data" or the solving of large combinatorial problems respectively. In this paper the usefulness of KDD is illustrated in by applying a genetic algorithm and artificial neural networks for the purpose of forecasting macroeconomic aggregates such as industrial production and inflation. Both methods can be categorized as methods from the field of artificial intelligence and are combined in a way that a GA serves as a tool for model selection and an ANN serves for the generation (estimation) of forecasts. It is shown that the utilization of KDD can be effective in forecasting macroeconomic time series.

AB - Traditional methods in econometrics have problems in analyzing efficiently "big data" or large combinatorial problems respectively. The forecasting profession in macroeconomics frequently deals with problems of that kind in applying various forms of factor analysis. Whereas, as recent literature shows, factor analysis reveals some problems in handling larger data sets. In this paper it is argued how useful the utilization of techniques and methods from the field of knowledge discovery in databases (KDD) can be. Whereas the term KDD emphasizes the aspect of extracting useful information out of "big data" or the solving of large combinatorial problems respectively. In this paper the usefulness of KDD is illustrated in by applying a genetic algorithm and artificial neural networks for the purpose of forecasting macroeconomic aggregates such as industrial production and inflation. Both methods can be categorized as methods from the field of artificial intelligence and are combined in a way that a GA serves as a tool for model selection and an ANN serves for the generation (estimation) of forecasts. It is shown that the utilization of KDD can be effective in forecasting macroeconomic time series.

M3 - Conference contribution

SN - 978-80-210-3496-9

SP - 30

EP - 35

BT - PROCEEDINGS OF THE 22ND INTERNATIONAL CONFERENCE ON MATHEMATICAL METHODS IN ECONOMICS 2004

A2 - Bauer, L

PB - MASARYKOVA UNIV

CY - BRNO

Y2 - 15 September 2004 through 17 September 2004

ER -