Tail of the stationary solution of the stochastic equation Yn+1=anYn+bn with Markovian coefficients

65 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Skewed Wealth Distributions: Theory and Empirics (2018, methodology): “…build on the entrepreneurial model of Quadrini (2000), and also calibrate their model to SCF data.66 Benhabib, Bisin, and Zhu (2011), using the methods of Kesten (1973), de Saporta (2005), and Roitershtein (2007), formally obtain a thick-tailed wealth distributions in OLG models with finite lives.”
The distribution of wealth and …scal policy in economies with …nitely lived agents (2008, unknown): “In terms of ( n ; n ) n these assumptions guarantee that ( n ; n ) n > 0; that E ( n j n1 ) < 1 for any n1 , and …nally that n > 1 with positive probability; see Lemma 3 in Appendix B. 27 Under these assumptions we can prove the following theorem, based on a theorem in Saporta (2005).”
One-dimensional linear recursions with Markov-dependent coefficients (2004, background): “(1.3) This extends both the one-dimensional version of a result of Kesten which is valid for i.i.d. variables (ξn, ρn)n∈Z (cf. [14], Theorem 5, see also an alternative approach developed by Goldie in [9]) as well as the recent result of de Saporta [7] where it is assumed that (ρn)n∈Z is a finite irr”