Long-Run  Growth  Forecasting
 出版社                 Springer Berlin Heidelberg
DOI    10.1007/978-3-540-77680-2
版权       2008
ISBN     978-3-540-77679-6 (Print) 978-3-540-77680-2 (Online)
Subject Collection                商业和经济
 
SpringerLinkDate        2008年3月29日
 

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       . . 6
1.3.1 Choosing a sensible theoretical model   . . 6
1.3.2 Choosing the best econometric technique  . . . . . . 8
2 Assessment of growth theories     . 9
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)   . . . . . . 17
2.4.2 Modeling barriers to riches (Parente & 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    . 36X 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    . . 101Contents 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    . . . . . 146
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    . . . . 160XII Contents
14 Conclusion and outlook      . 163
List of figures        . 165
List of tables        . 167
References        . . . . 169

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