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TOPIC:
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GEOMETRICALLY STOPPED MARKOVIAN RANDOM GROWTH PROCESSES AND PARETO TAILS
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ABSTRACT
Many empirical studies document power law behavior in size distributions of economic interest such as cities, firms, income, and wealth. One mechanism for generating such behavior combines independent and identically distributed Gaussian additive shocks to log-size with a geometric age distribution. We generalize this mechanism by allowing the shocks to be non-Gaussian (but light-tailed) and dependent upon a Markov state variable. Our main results provide sharp bounds on tail probabilities and simple formulas for Pareto exponents. We present two applications: (i) we show that the tails of the wealth distribution in a heterogeneous-agent dynamic general equilibrium model with idiosyncratic endowment risk decay exponentially, unlike models with investment risk where the tails may be Paretian, and (ii) we show that a random growth model for the population dynamics of Japanese prefectures is consistent with the observed Pareto exponent but only after allowing for Markovian dynamics.
Keywords: Exponential tails, Gibrat’s law, Pareto tails, Power law, Random growth, Tauberian theorem
JEL codes: C46, C65, D30, D52, D58, R12
Click here to view the paper.
Click here to view the CV.
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PRESENTER
Brendan Beare
University of California, San Diego
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RESEARCH FIELDS
Econometric Theory
Financial Econometrics
Time Series Econometrics
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DATE:
1 March 2019 (Friday)
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TIME:
2pm - 3.30pm
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VENUE:
Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903
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