Long-Run Growth Forecasting

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 betwee...

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Principais autores: Bergheim, Stefan., SpringerLink (Online service)
Formato: Digital
Publicado em:
Assuntos:
Desenvolvimento econômico.
Macroeconomia.
Economia.
Endereço do item:http://dx.doi.org/10.1007/978-3-540-77680-2
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spelling oai:localhost:123456789-1300822023-07-17T15:09:13Z Long-Run Growth Forecasting Bergheim, Stefan. SpringerLink (Online service) Desenvolvimento econômico. Macroeconomia. Economia. 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. The author works for a commercial bank and has been the lead researcher in the bank's project called "Global Growth Centres 2020". 0 2022-10-06T07:58:39Z 2022-10-06T07:58:39Z 2008. Digital 338.1 B496l 9783540776802 198611 http://dx.doi.org/10.1007/978-3-540-77680-2 http://dx.doi.org/10.1007/978-3-540-77680-2
institution Acervo SISBI
collection SIGAA
topic Desenvolvimento econômico.
Macroeconomia.
Economia.
spellingShingle Desenvolvimento econômico.
Macroeconomia.
Economia.
Bergheim, Stefan.
SpringerLink (Online service)
Long-Run Growth Forecasting
description 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. The author works for a commercial bank and has been the lead researcher in the bank's project called "Global Growth Centres 2020".
format Digital
author Bergheim, Stefan.
SpringerLink (Online service)
author_facet Bergheim, Stefan.
SpringerLink (Online service)
author_sort Bergheim, Stefan.
title Long-Run Growth Forecasting
title_short Long-Run Growth Forecasting
title_full Long-Run Growth Forecasting
title_fullStr Long-Run Growth Forecasting
title_full_unstemmed Long-Run Growth Forecasting
title_sort long-run growth forecasting
publishDate 2022
url http://dx.doi.org/10.1007/978-3-540-77680-2
work_keys_str_mv AT bergheimstefan longrungrowthforecasting
AT springerlinkonlineservice longrungrowthforecasting
_version_ 1771687945869197312

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