Long-Run Growth Forecasting

Stefan Bergheim

ISBN 10: 3540776796 / ISBN 13: 9783540776796

Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2008

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book explores how to set up an empirical model that helps with forecasting long-term economic growth in a large number of countries. It offers a systematic approach to models of potential GDP that can also be used for forecasts of more than a decade. It is an attempt to fill the wide gap between the high demand for such models by commercial banks, international organizations, central banks and governments on the one hand and the limited supply on the other hand. Frequent forecast failures in the past (e.g. Japan 1990, Asia 1997) and the heavy economic losses they produced motivated the work. The book assesses the large number of different theories of economic growth, the drivers of economic growth, the available datasets and the empirical methods on offer. A preference is shown for evolutionary models and an augmented Kaldor model. The book uses non-stationary panel techniques to find pair-wise cointegration among GDP per capita and its main correlates such as physical capital, human capital and openness. GDP forecasts for the years 2006 to 2020 for 40 countries are derived in a transparent way.

Seller Inventory # 9783540776796

Bibliographic Details

• Title: Long-Run Growth Forecasting
• Publisher: Springer Berlin Heidelberg, Springer Berlin Heidelberg
• Publication Date: 2008
• Language: English
• ISBN 10: 3540776796
• ISBN 13: 9783540776796
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 16D millimeters 241 millimeters width by 160 millimeters height by 16 millimeters depth
• Weight: 483 grams

About this title

Synopsis

This book explores how to set up an empirical model that helps with forecasting long-term economic growth in a large number of countries. It offers a systematic approach to models of potential GDP that can also be used for forecasts of more than a decade. It is an attempt to fill the wide gap between the high demand for such models by commercial banks, international organizations, central banks and governments on the one hand and the limited supply on the other hand. Frequent forecast failures in the past (e.g. Japan 1990, Asia 1997) and the heavy economic losses they produced motivated the work. The book assesses the large number of different theories of economic growth, the drivers of economic growth, the available datasets and the empirical methods on offer. A preference is shown for evolutionary models and an augmented Kaldor model. The book uses non-stationary panel techniques to find pair-wise cointegration among GDP per capita and its main correlates such as physical capital, human capital and openness. GDP forecasts for the years 2006 to 2020 for 40 countries are derived in a transparent way.

About the Author

The author works for a commercial bank and has been the lead researcher in the bank's project called "Global Growth Centres 2020".