Long-run growth forecasting:
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| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Berlin [u.a.]
Springer
2008
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Auch als Internetausgabe |
| Physical Description: | XIV, 189 S. graph. Darst. 235 mm x 155 mm |
| ISBN: | 3540776796 9783540776796 |
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MARC
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| 020 | |a 9783540776796 |c Gb. : ca. EUR 85.55 (freier Pr.), ca. sfr 139.50 (freier Pr.) |9 978-3-540-77679-6 | ||
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| 100 | 1 | |a Bergheim, Stefan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Long-run growth forecasting |c Stefan Bergheim |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2008 | |
| 300 | |a XIV, 189 S. |b graph. Darst. |c 235 mm x 155 mm | ||
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| 650 | 4 | |a Prévision économique - Modèles mathématiques | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Economic forecasting |x Mathematical models | |
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Record in the Search Index
| _version_ | 1804137382284361728 |
|---|---|
| adam_text | Contents
1
The importance of long-run growth analysis
............... 1
1.1
Frequent forecast failures
................................. 1
1.2
Strong demand
-
but little supply
......................... 3
1.3
Plan of work
............................................
(¡
1.3.1
Choosing a sensible theoretical model
................
f j
1.3.2
Choosing the best econometric technique
............. 8
2
Assessment of growth theories
.............................
f)
2.1
The search for a dynamic model
........................... 9
2.2
The basic neoclassical model
.............................. 10
2.2.1
Application in cross-country analysis
................. 12
2.3
Focus on convergence
.................................... 13
2.3.1
Tests for conditional convergence
.................... 15
2.4
Models with deeper insights
............................... 16
2.4.1
Including human capital (Lucas)
....................
IT
2.4.2
Modeling barriers to riches
(Parente
<fe Prescott)
...... 18
2.5
Opening the theories further
.............................. 19
2.5.1
Models with scale effects
........................... 20
2.5.2
Evolutionary models of growth
...................... 21
2.5.3
Open-system models
............................... 24
2.6
General critique of the standard approach
.................. 25
2.6.1
Production function cannot be estimated
............. 25
2.6.2
Aggregate production function does not exist
......... 28
2.6.3
The concept of TFP is not helpful
................... 29
2.6.4
Beyond neoclassical economics
...................... 29
2.7
The augmented Kaldor model
............................. 30
3
The dependent variable: GDP growth
..................... 35
3.1
Choosing the appropriate data source
...................... 36
X
Contents
4
Labor input
............................................... 43
4.1
Population growth is endogenous
.......................... 43
4.2
Hours worked per capita are important
..................... 46
4.3
Age structure of the population
........................... 48
5
Physical capital
............................................ 51
5.1
Measuring capital accumulation
........................... 52
5.1.1
Investment and changes in capital stocks
............. 52
5.1.2
Different databases
-
different investment ratios
....... 53
5.1.3
Capital stocks from perpetual inventory
.............. 54
5.2
Main insights on capital accumulation
...................... 56
5.2.1
Investment ratios are not constant
................... 56
5.2.2
Investment ratios do not differ much across countries
. . 56
5.2.3
Investment ratios are not proportional to changes in
the capital stock
.................................. 60
5.2.4
Investment ratios are not proportional to levels of the
capital stock
...................................... 61
5.2.5
Capital productivity does not correlate with income
... 61
5.2.6
Capital accumulation is not exogenous
............... 63
5.3
Proper modeling of capital accumulation
................... 63
6
Human capital
............................................. 67
6.1
Micro- and macroeconomic theory
......................... 69
6.1.1
Microeconomic analysis: labor economics
............. 70
6.1.2
Macroeconomic models with different conclusions
...... 71
6.2
Measures and empirical analysis
........................... 74
6.2.1
Best measure: years of education
................... 75
7
Openness
.................................................. 81
7.1
Theory: higher efficiency
................................. 83
7.1.1
Extent of the market and specialization
.............. 84
7.1.2
Good macro polices and more competition
............ 84
7.1.3
Additional influences of trade on income
............. 86
7.2
Measuring openness
...................................... 86
7.2.1
Black market premium and tariffs
................... 87
7.2.2
The openness dummy
.............................. 87
7.2.3
Best measure: adjusted trade share
.................. 88
7.3
Empirical debate: levels versus growth
..................... 90
8
Spatial linkages
............................................ 95
8.1
Spatial economics
-
location matters
....................... 97
8.1.1
Absolute location: latitude and climate
............... 97
8.1.2
Relative location: rich neighbors
..................... 97
8.2
Constructing spatial GDP
................................ 98
8.3
Sum: Spatial linkages not much help
....................... 101
Contents
XI
9
Other determinants of GDP
...............................103
10
The theory of forecasting
..................................105
10.1
The benefits of forecast experiments
.......................106
10.2
The characteristics of good forecasts
.......................106
10.3
Intercept correction and forecast combination
...............109
11
The evolution of growth empirics
..........................113
11.1
Still widely used: cross-section
............................114
11.2
Weaknesses of cross-section regressions
.....................116
11.2.1
Same production function assumed
..................117
11.2.2
Long-run growth path assumed to be constant and
the same across countries
...........................117
11.2.3
Same pace of conditional convergence assumed
........118
11.2.4
Errors are assumed uncorrelated with the
explanatory variables
..............................118
11.2.5
Right-hand side variables assumed exogenous
.........118
11.2.6
In sum: many assumptions are violated
...............119
11.3
The climax of cross-section
...............................119
11.4
Advantages of panel techniques
............................121
11.4.1
Initial technology can differ across countries
..........123
11.4.2
Dealing with endogeneity bias
.......................123
11.4.3
Addressing lagged dependent bias
...................124
11.4.4
Modeling heterogeneous technological progress
........125
11.4.5
Summary of results from panel regressions
............125
11.5
Non-stationary panel techniques
...........................126
11.5.1
Pooled mean group technique
.......................126
11.5.2
Testing unit roots and
cointegration in
panels
.........128
11.5.3
Panel unit root tests
...............................129
11.5.4
Panel
cointegration
tests
...........................131
11.6
A two-stage estimation method
............................134
12
Estimation results
.........................................137
12.1
Correlation analysis
......................................137
12.2
Panel unit root tests
.....................................139
12.3
Panel cointegration test
..................................142
12.4
The short-run forecasting models
..........................140
13
Forecast competitions and
2006-2020
forecasts
.............151
13.1
Forecast competition
2001-2005...........................151
13.2
Forecast combination
....................................154
13.3
Forecast competition
1996-2005 ...........................155
13.4
Forecasts for
2006-2020...................................155
13.5
Other long-run forecasting models
.........................160
XII Contents
14
Conclusion and outlook
....................................163
List of figures
..................................................165
List of tables
..................................................167
References
.....................................................169
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
|
| adam_txt |
Contents
1
The importance of long-run growth analysis
. 1
1.1
Frequent forecast failures
. 1
1.2
Strong demand
-
but little supply
. 3
1.3
Plan of work
.
(¡
1.3.1
Choosing a sensible theoretical model
.
f j
1.3.2
Choosing the best econometric technique
. 8
2
Assessment of growth theories
.
f)
2.1
The search for a dynamic model
. 9
2.2
The basic neoclassical model
. 10
2.2.1
Application in cross-country analysis
. 12
2.3
Focus on convergence
. 13
2.3.1
Tests for conditional convergence
. 15
2.4
Models with deeper insights
. 16
2.4.1
Including human capital (Lucas)
.
IT
2.4.2
Modeling barriers to riches
(Parente
<fe Prescott)
. 18
2.5
Opening the theories further
. 19
2.5.1
Models with scale effects
. 20
2.5.2
Evolutionary models of growth
. 21
2.5.3
Open-system models
. 24
2.6
General critique of the standard approach
. 25
2.6.1
Production function cannot be estimated
. 25
2.6.2
Aggregate production function does not exist
. 28
2.6.3
The concept of TFP is not helpful
. 29
2.6.4
Beyond neoclassical economics
. 29
2.7
The augmented Kaldor model
. 30
3
The dependent variable: GDP growth
. 35
3.1
Choosing the appropriate data source
. 36
X
Contents
4
Labor input
. 43
4.1
Population growth is endogenous
. 43
4.2
Hours worked per capita are important
. 46
4.3
Age structure of the population
. 48
5
Physical capital
. 51
5.1
Measuring capital accumulation
. 52
5.1.1
Investment and changes in capital stocks
. 52
5.1.2
Different databases
-
different investment ratios
. 53
5.1.3
Capital stocks from perpetual inventory
. 54
5.2
Main insights on capital accumulation
. 56
5.2.1
Investment ratios are not constant
. 56
5.2.2
Investment ratios do not differ much across countries
. . 56
5.2.3
Investment ratios are not proportional to changes in
the capital stock
. 60
5.2.4
Investment ratios are not proportional to levels of the
capital stock
. 61
5.2.5
Capital productivity does not correlate with income
. 61
5.2.6
Capital accumulation is not exogenous
. 63
5.3
Proper modeling of capital accumulation
. 63
6
Human capital
. 67
6.1
Micro- and macroeconomic theory
. 69
6.1.1
Microeconomic analysis: labor economics
. 70
6.1.2
Macroeconomic models with different conclusions
. 71
6.2
Measures and empirical analysis
. 74
6.2.1
Best measure: years of education
. 75
7
Openness
. 81
7.1
Theory: higher efficiency
. 83
7.1.1
Extent of the market and specialization
. 84
7.1.2
Good macro polices and more competition
. 84
7.1.3
Additional influences of trade on income
. 86
7.2
Measuring openness
. 86
7.2.1
Black market premium and tariffs
. 87
7.2.2
The openness dummy
. 87
7.2.3
Best measure: adjusted trade share
. 88
7.3
Empirical debate: levels versus growth
. 90
8
Spatial linkages
. 95
8.1
Spatial economics
-
location matters
. 97
8.1.1
Absolute location: latitude and climate
. 97
8.1.2
Relative location: rich neighbors
. 97
8.2
Constructing spatial GDP
. 98
8.3
Sum: Spatial linkages not much help
. 101
Contents
XI
9
Other determinants of GDP
.103
10
The theory of forecasting
.105
10.1
The benefits of forecast experiments
.106
10.2
The characteristics of good forecasts
.106
10.3
Intercept correction and forecast combination
.109
11
The evolution of growth empirics
.113
11.1
Still widely used: cross-section
.114
11.2
Weaknesses of cross-section regressions
.116
11.2.1
Same production function assumed
.117
11.2.2
Long-run growth path assumed to be constant and
the same across countries
.117
11.2.3
Same pace of conditional convergence assumed
.118
11.2.4
Errors are assumed uncorrelated with the
explanatory variables
.118
11.2.5
Right-hand side variables assumed exogenous
.118
11.2.6
In sum: many assumptions are violated
.119
11.3
The climax of cross-section
.119
11.4
Advantages of panel techniques
.121
11.4.1
Initial technology can differ across countries
.123
11.4.2
Dealing with endogeneity bias
.123
11.4.3
Addressing lagged dependent bias
.124
11.4.4
Modeling heterogeneous technological progress
.125
11.4.5
Summary of results from panel regressions
.125
11.5
Non-stationary panel techniques
.126
11.5.1
Pooled mean group technique
.126
11.5.2
Testing unit roots and
cointegration in
panels
.128
11.5.3
Panel unit root tests
.129
11.5.4
Panel
cointegration
tests
.131
11.6
A two-stage estimation method
.134
12
Estimation results
.137
12.1
Correlation analysis
.137
12.2
Panel unit root tests
.139
12.3
Panel cointegration test
.142
12.4
The short-run forecasting models
.140
13
Forecast competitions and
2006-2020
forecasts
.151
13.1
Forecast competition
2001-2005.151
13.2
Forecast combination
.154
13.3
Forecast competition
1996-2005 .155
13.4
Forecasts for
2006-2020.155
13.5
Other long-run forecasting models
.160
XII Contents
14
Conclusion and outlook
.163
List of figures
.165
List of tables
.167
References
.169
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" |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Bergheim, Stefan |
| author_facet | Bergheim, Stefan |
| author_role | aut |
| author_sort | Bergheim, Stefan |
| author_variant | s b sb |
| building | Verbundindex |
| bvnumber | BV023116464 |
| callnumber-first | H - Social Science |
| callnumber-label | HB3730 |
| callnumber-raw | HB3730 |
| callnumber-search | HB3730 |
| callnumber-sort | HB 43730 |
| callnumber-subject | HB - Economic Theory and Demography |
| classification_rvk | QC 340 |
| ctrlnum | (OCoLC)212327877 (DE-599)DNB986860743 |
| dewey-full | 338.900112 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 338 - Production |
| dewey-raw | 338.900112 |
| dewey-search | 338.900112 |
| dewey-sort | 3338.900112 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV023116464 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:50:12Z |
| indexdate | 2024-07-09T21:11:24Z |
| institution | BVB |
| isbn | 3540776796 9783540776796 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016318983 |
| oclc_num | 212327877 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-12 DE-384 DE-N2 DE-634 |
| owner_facet | DE-355 DE-BY-UBR DE-19 DE-BY-UBM DE-12 DE-384 DE-N2 DE-634 |
| physical | XIV, 189 S. graph. Darst. 235 mm x 155 mm |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| spelling | Bergheim, Stefan Verfasser aut Long-run growth forecasting Stefan Bergheim Berlin [u.a.] Springer 2008 XIV, 189 S. graph. Darst. 235 mm x 155 mm txt rdacontent n rdamedia nc rdacarrier Auch als Internetausgabe Dissertation Hochschule für Unternehmensführung Prévision économique - Modèles mathématiques Mathematisches Modell Economic forecasting Mathematical models Wirtschaftswachstum (DE-588)4066527-6 gnd rswk-swf Langfristige Prognose (DE-588)4166701-3 gnd rswk-swf Nichtstationäre Zeitreihenanalyse (DE-588)4300599-8 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Wirtschaftswachstum (DE-588)4066527-6 s Langfristige Prognose (DE-588)4166701-3 s Nichtstationäre Zeitreihenanalyse (DE-588)4300599-8 s DE-604 Erscheint auch als Online-Ausgabe 978-3-540-77680-2 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016318983&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016318983&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Bergheim, Stefan Long-run growth forecasting Prévision économique - Modèles mathématiques Mathematisches Modell Economic forecasting Mathematical models Wirtschaftswachstum (DE-588)4066527-6 gnd Langfristige Prognose (DE-588)4166701-3 gnd Nichtstationäre Zeitreihenanalyse (DE-588)4300599-8 gnd |
| subject_GND | (DE-588)4066527-6 (DE-588)4166701-3 (DE-588)4300599-8 (DE-588)4113937-9 |
| title | Long-run growth forecasting |
| title_auth | Long-run growth forecasting |
| title_exact_search | Long-run growth forecasting |
| title_exact_search_txtP | Long-run growth forecasting |
| title_full | Long-run growth forecasting Stefan Bergheim |
| title_fullStr | Long-run growth forecasting Stefan Bergheim |
| title_full_unstemmed | Long-run growth forecasting Stefan Bergheim |
| title_short | Long-run growth forecasting |
| title_sort | long run growth forecasting |
| topic | Prévision économique - Modèles mathématiques Mathematisches Modell Economic forecasting Mathematical models Wirtschaftswachstum (DE-588)4066527-6 gnd Langfristige Prognose (DE-588)4166701-3 gnd Nichtstationäre Zeitreihenanalyse (DE-588)4300599-8 gnd |
| topic_facet | Prévision économique - Modèles mathématiques Mathematisches Modell Economic forecasting Mathematical models Wirtschaftswachstum Langfristige Prognose Nichtstationäre Zeitreihenanalyse Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016318983&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016318983&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bergheimstefan longrungrowthforecasting |