Job Exit Behavior of Older Men
By J. Berkovec, Steven Stern. 1991
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- V. Aguirregabiria
- Zwi Eckstein
- P. Mira
- Gerard J. van den Berg
- Kenneth Train
- Simon C. Parker
- Jose Apesteguia
- Miguel A. Ballester
Citations in the corpus
- Panel Data Discrete Choice Models of Consumer Demand (2013, methodology): “A potential solution to this problem, proposed by Berkovec and Stern (1991) and Stern (1992), is to reformulate the model to ―add noise‖ and ―smooth out‖ the indicator functions in (19).”
- The retirement behaviour of the self-employed in Britain (2007, background): “E-mail: s.c.parker@durham.ac.uk 1 For studies on employee retirement, see Stock and Wise (1990a, b), Berkovec and Stern (1991), Lumsdaine et al. (1992), Blau (1994, 1998), Ruhm (1996), Rust and Phelan (1997), Samwick (1998), Friedberg (1999, 2003), Gustman and Steinmeier (2000a, b, 2001), Baker…”
- Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models (2002, background): “See Miller (1984), Pakes (1986), and Berkovec and Stern (1991) for early examples of applications that modeled important sources of unobserved heterogeneity.”
- Discrete Choice Methods with Simulation by Kenneth E. Train (2003, methodology): “The procedures for modeling these decisions were first developed for various applications by, for example, Wolpin (1984) on women’s fertility, Pakes (1986) on patent options, Wolpin (1987) on job search, Rust (1987) on engine replacement, Berkovec and Stern (1991) on retirement, and others.”
- Empirical labor search models: a survey (2003, background): “The papers by Eckstein and Wolpin (1989b), Berkovec and Stern (1991) and Keane and Wolpin (1997), and more recent papers, prefer the labor force participation framework for empirically analyzing dynamic labor supply.”
- Monotone Stochastic Choice Models: The Case of Risk and Time Preferences (2015, background): “In a random parameter model (RPM), the random error distorts the agent’s preference parameter.(8) Hence, the agent opts for the alternative that maximizes Uω+ , where the random error on the preference parameter follows a continuous cumulative distribution Ψ on Ω.”