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STICERD Econometrics Seminar Series

Geometrically Stopped Markovian Random Growth Processes and Pareto Tails

Alexis Akira Toda (UC San Diego)

Tuesday 10 March 2020 12:30 - 14:00

NAB 2.08, 2nd Floor, New Academic Building, LSE, 54 Lincoln's Inn Fields, WC2A 3LJ

Many of our seminars and public events this year will continue as online seminars or as online and in person. Please check our website listings and Twitter feed @STICERD_LSE for updates.

Unless otherwise specified, current restrictions mean in-person seminars are only open to members of the LSE community (those with a valid LSE ID card).

Those unable to join the seminars in-person are welcome to participate via zoom.

About this event

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, a simple equation determining Pareto exponents, and comparative statics. We present two applications: we show that (i) the tails of the wealth distribution in a heterogeneous-agent dynamic general equilibrium model with idiosyncratic investment risk are Paretian, and (ii) a random growth model for the population dynamics of Japanese municipalities is consistent with the observed Pareto exponent but only after allowing for Markovian dynamics.

STICERD Econometrics seminars are held on Thursdays in term time at 14.00-15.30, ONLINE, unless specified otherwise.

Seminar organisers: Professor Tai Otsu and Dr. Vassilis Hajivassiliou.

For further information please contact Lubala Chibwe, either by email:

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This event will take place in NAB 2.08, 2nd Floor, New Academic Building, LSE, 54 Lincoln's Inn Fields, WC2A 3LJ. The building is labelled on the map.

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