The endogenous grid method for discrete-continuous dynamic choice models with (or without) taste shocks

TY - JOUR

T1 - The endogenous grid method for discrete-continuous dynamic choice models with (or without) taste shocks

AU - Iskhakov, Fedor

AU - Jørgensen, Thomas H.

AU - Rust, John

AU - Schjerning, Bertel

PY - 2017/7/1

Y1 - 2017/7/1

N2 - We present a fast and accurate computational method for solving and estimating a class of dynamic programming models with discrete and continuous choice variables. The solution method we develop for structural estimation extends the endogenous grid-point method (EGM) to discrete-continuous (DC) problems. Discrete choices can lead to kinks in the value functions and discontinuities in the optimal policy rules, greatly complicating the solution of the model. We show how these problems are ameliorated in the presence of additive choice-specific independent and identically distributed extreme value taste shocks that are typically interpreted as “unobserved state variables” in structural econometric applications, or serve as “random noise” to smooth out kinks in the value functions in numerical applications. We present Monte Carlo experiments that demonstrate the reliability and efficiency of the DC-EGM algorithm and the associated maximum likelihood estimator for structural estimation of a life-cycle model of consumption with discrete retirement decisions.

AB - We present a fast and accurate computational method for solving and estimating a class of dynamic programming models with discrete and continuous choice variables. The solution method we develop for structural estimation extends the endogenous grid-point method (EGM) to discrete-continuous (DC) problems. Discrete choices can lead to kinks in the value functions and discontinuities in the optimal policy rules, greatly complicating the solution of the model. We show how these problems are ameliorated in the presence of additive choice-specific independent and identically distributed extreme value taste shocks that are typically interpreted as “unobserved state variables” in structural econometric applications, or serve as “random noise” to smooth out kinks in the value functions in numerical applications. We present Monte Carlo experiments that demonstrate the reliability and efficiency of the DC-EGM algorithm and the associated maximum likelihood estimator for structural estimation of a life-cycle model of consumption with discrete retirement decisions.

KW - Bellman equation

KW - discrete and continuous choice

KW - endogenous grid-point method

KW - Euler equation

KW - extreme value taste shocks

KW - Life-cycle model

KW - nested fixed point algorithm

KW - retirement choice

KW - smoothed max function

KW - structural estimation

U2 - 10.3982/QE643

DO - 10.3982/QE643

M3 - Journal article

AN - SCOPUS:85026308937

SN - 1759-7323

VL - 8

SP - 317

EP - 365

JO - Quantitative Economics

JF - Quantitative Economics

IS - 2

ER -