During the last decade, the econometric study of investment behaviour has developed from the descriptive construction of empirical investment equations, as first performed by J. Tinbergen in his well-known 19- publication: 'A Method and its Application to Investment Activity', to the empirical testing of increasingly explicit theories of production behaviour. It was principally D. Jorgenson who gave a great push to this intensified interest in micro-economic investment, with his famous 1965-paper: 'Anticipation and Investment Behaviour', in which he formulated a pure neoclassical model of investment behaviour under the conditions of a (simple) homogeneous production technology and perfectly competi- tive markets. But, although the scope of the familiar flexible accelerator model was considerably extended by the introduction of relative factor prices, the resulting investment relationship generally remained one of the most ill-estimated equations in econometric models. The very rigid assumptions of pure neoclassical models might have caused these bad results. More- over, the required ex post measure for aggregate capital stock is very deficient for most economies. Hence, it should be interesting to formulate investment models subject to less rigid restrictions on production and market behaviour and, preferably, not containing any measure of aggre- gate capital stock.
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1. Some remarks on the concept of a production function.- 1.1 The Production Function.- 1.2 The Input (Level) Set.- 1.3 The Relationship between Production Functions and Input (Level) Sets.- 1.4 Transforms of Production Functions.- 1.5 Homogeneous and homothetic Production Functions.- 1.6 Substitutability in neoclassical Production Functions.- 1.7 Some particular neoclassical Production Functions.- 1.8 Technical Progress in neoclassical and vintage Production Functions.- 1.9 Conclusion.- 2. Investment behaviour based on micro production models.- 2.0 Introduction.- 2.1 A putty-putty ces-Investment Model.- 2.1.1 Case 1: An aggregate ces-production function exists.- 2.1.2 Case 2: An aggregate ces-production function need not exist.- 2.1.2.1 The putty-putty ces-investment model as a concave programming model.- 2.1.2.2 A state variable notation of the putty-putty ces-investment model.- 2.1.3 Further dynamics of the investment process.- 2.2 A putty-clay ces-Investment Model.- 2.2.1 Optimal economic growth in a putty-clay production model.- 2.2.2 Optimal investment behaviour in a putty-clay ces-model.- 2.2.3 The transition to a real-type investment function.- 2.3 A non-vintage ces-Investment Model.- 2.4 A putty-putty ves-Investment Model.- 2.5 A putty-clay ves-Investment Model.- 2.6 A non-vintage ves-Investment Model.- 2.7 An Adjustment Cost Model for the Investment Demand.- 2.8 Conclusion.- 3. The aggregation problem: the utilization of micro-relationships for macro-data.- 3.1 Functional Separability.- 3.2 Deterministic Aggregation.- 3.3 Bias in Deterministic Aggregation.- 3.3.1 The a priori linear aggregate regression model.- 3.3.2 Suitable choice of the slope coefficients for the aggregate data.- 3.4 Stochastic Aggregation.- 3.4.1 The random coefficient approach.- 3.4.2 The probability distribution approach.- 3.5 Conclusion.- 4. An application on postwar investment behaviour of six eec-countries.- 4.0 Introduction.- 4.0.1 Measuring the rate of capacity utilization and the fiscal depreciation allowance.- 4.0.2 Statistical estimation procedures and accompanying statistics.- 4.1 Belgium.- 4.2 France.- 4.3 Germany (Federal Republic).- 4.4 Great Britain (inclusive Northern Ireland).- 4.5 Italy.- 4.6 Netherlands.- 4.7 Conclusion.- A.1 Derivation of the Class of CES-Production Functions.- Appendix B. Necessary conditions for the supremum of the functional (II.1.6).- Appendix C. Optimal control problems with an infinite time horizon.- Appendix D. The investment relationships discussed.- List of Symbols.
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