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Modelling Tails of Aggregated Economic Processes in a Stochastic Growth Model

Author

Listed:
  • Stéphane Auray

    (ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - GENES - Groupe des Écoles Nationales d'Économie et Statistique)

  • Aurélien Eyquem

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - Université de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Frédéric Jouneau-Sion

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - Université de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

Abstract

An annual sequence of wages in England starting in 1245 is used. It is shown that a standard AK-type growth model with capital externality and stochastic productivity shocks is unable to explain important features of the data. Random returns to scale are then considered. Moderate episodes of increasing returns to scale and growth are shown to be compatible with convergence of wage’s process towards a unique stationary distribution. This holds true for other relevant values such as GDP and/or capital stock. Furthermore, random returns to scale generate heteroskedasticity, a feature common to macroeconomic time series. Finally, the limit distribution of real wages displays fat tails if returns to scale are episodically increasing. Several inference results supporting randomness of returns to scale are provided.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Stéphane Auray & Aurélien Eyquem & Frédéric Jouneau-Sion, 2014. "Modelling Tails of Aggregated Economic Processes in a Stochastic Growth Model," Post-Print halshs-00995703, HAL.
    • Handle: RePEc:hal:journl:halshs-00995703
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    Cited by:

    1. Isoré, Marlène & Szczerbowicz, Urszula, 2017. "Disaster risk and preference shifts in a New Keynesian model," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 97-125.

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    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • N13 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - Europe: Pre-1913
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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