Dynamic Random Utility

Econometrica ◽  
2019 ◽  
Vol 87 (6) ◽  
pp. 1941-2002 ◽  
Author(s):  
Mira Frick ◽  
Ryota Iijima ◽  
Tomasz Strzalecki
Keyword(s):  
Choice Behavior ◽  
Empirical Work ◽  
Parameter Estimates ◽  
Random Utility ◽  
Choice Data ◽  
Stochastic Choice ◽  
Axiomatic Analysis ◽  
Special Cases ◽  
Dynamic Stochastic ◽  
Bayesian Rationality

We provide an axiomatic analysis of dynamic random utility, characterizing the stochastic choice behavior of agents who solve dynamic decision problems by maximizing some stochastic process ( U t ) of utilities. We show first that even when ( U t ) is arbitrary, dynamic random utility imposes new testable across‐period restrictions on behavior, over and above period‐by‐period analogs of the static random utility axioms. An important feature of dynamic random utility is that behavior may appear history‐dependent, because period‐ t choices reveal information about U t , which may be serially correlated; however, our key new axioms highlight that the model entails specific limits on the form of history dependence that can arise. Second, we show that imposing natural Bayesian rationality axioms restricts the form of randomness that ( U t ) can display. By contrast, a specification of utility shocks that is widely used in empirical work violates these restrictions, leading to behavior that may display a negative option value and can produce biased parameter estimates. Finally, dynamic stochastic choice data allow us to characterize important special cases of random utility—in particular, learning and taste persistence—that on static domains are indistinguishable from the general model.

scholarly journals Neural random utility: Relating cardinal neural observables to stochastic choice behavior.

2019 ◽  
Vol 12 (1) ◽  
pp. 45-72 ◽  
Author(s):  
Ryan Webb ◽  
Ifat Levy ◽  
Stephanie C. Lazzaro ◽  
Robb B. Rutledge ◽  
Paul W. Glimcher
Keyword(s):  
Choice Behavior ◽  
Random Utility ◽  
Stochastic Choice

scholarly journals Investigation of the Stochastic Choice under Risk using Experimental Data

2020 ◽  
Vol 22 (2) ◽  
pp. 137
Author(s):  
Yudistira Permana ◽  
Giovanni Van Empel ◽  
Rimawan Pradiptyo
Keyword(s):  
Expected Utility ◽  
Goodness Of Fit ◽  
Random Utility ◽  
Utility Model ◽  
Stochastic Choice ◽  
Decisions Under Risk ◽  
Empirical Performance ◽  
Maximum Likelihood Technique ◽  
Better Than ◽  
The Eu

This paper extends the analysis of the data from the experiment undertaken by Pradiptyo et al. (2015), to help explain the subjects’ behaviour when making decisions under risk. This study specifically investigates the relative empirical performance of the two general models of the stochastic choice: the random utility model (RUM) and the random preference model (RPM) where this paper specifies these models using two preference functionals, expected utility (EU) and rank-dependent expected utility (RDEU). The parameters are estimated in each model using a maximum likelihood technique and run a horse-race using the goodness-of-fit between the models. The results show that the RUM better explains the subjects’ behaviour in the experiment. Additionally, the RDEU fits better than the EU for modelling the stochastic choice. 

R&D projects analyzed by semimartingale methods

1985 ◽  
Vol 22 (02) ◽  
pp. 288-299 ◽  
Author(s):  
Knut K. Aase
Keyword(s):  
Brownian Motion ◽  
Stochastic Equation ◽  
Compound Poisson Process ◽  
General Nature ◽  
Decision Variable ◽  
Non Linear Equation ◽  
Special Cases ◽  
Dynamic Stochastic ◽  
Present Setting

In this article we examine R&D projects where the project status changes according to a general dynamic stochastic equation. This allows for both continuous and jump behavior of the project status. The time parameter is continuous. The decision variable includes a non-stationary resource expenditure strategy and a stopping policy which determines when the project should be terminated. Characterization of stationary policies becomes straightforward in the present setting. A non-linear equation is determined for the expected discounted return from the project. This equation, which is of a very general nature, has been considered in certain special cases, where it becomes manageable. The examples include situations where the project status changes according to a compound Poisson process, a geometric Brownian motion, and a Brownian motion with drift. In those cases we demonstrate how the exact solution can be obtained and the optimal policy found.

Filters and parameter estimation for a partially observable system subject to random failure with continuous-range observations

2004 ◽  
Vol 36 (4) ◽  
pp. 1212-1230 ◽  
Author(s):  
Daming Lin ◽  
Viliam Makis
Keyword(s):  
Condition Monitoring ◽  
Continuous Time ◽  
The State ◽  
Parameter Estimates ◽  
State Process ◽  
Observation Process ◽  
Special Cases ◽  
Random Failure ◽  
Prediction Problems ◽  
Continuous Range

We consider a failure-prone system operating in continuous time. Condition monitoring is conducted at discrete time epochs. The state of the system is assumed to evolve as a continuous-time Markov process with a finite state space. The observation process with continuous-range values is stochastically related to the state process, which, except for the failure state, is unobservable. Combining the failure information and the condition monitoring information, we derive a general recursive filter, and, as special cases, we obtain recursive formulae for the state estimation and other quantities of interest. Updated parameter estimates are obtained using the expectation-maximization (EM) algorithm. Some practical prediction problems are discussed and finally an illustrative example is given using a real dataset.

Estimation of Dynamic Term Structure Models

2012 ◽  
Vol 02 (02) ◽  
pp. 1250008 ◽  
Author(s):  
Gregory R. Duffee ◽  
Richard H. Stanton
Keyword(s):  
Maximum Likelihood ◽  
Term Structure ◽  
Empirical Work ◽  
Interest Rate Risk ◽  
Small Sample ◽  
Accurate Estimation ◽  
Parameter Estimates ◽  
Finite Sample ◽  
Term Structure Models ◽  
Finite Sample Properties

We study the finite-sample properties of some of the standard techniques used to estimate modern term structure models. For sample sizes and models similar to those used in most empirical work, we reach three surprising conclusions. First, while maximum likelihood works well for simple models, it produces strongly biased parameter estimates when the model includes a flexible specification of the dynamics of interest rate risk. Second, despite having the same asymptotic efficiency as maximum likelihood, the small-sample performance of Efficient Method of Moments (a commonly used method for estimating complicated models) is unacceptable even in the simplest term structure settings. Third, the linearized Kalman filter is a tractable and reasonably accurate estimation technique, which we recommend in settings where maximum likelihood is impractical.

Stochastic Choice Behavior on Road Traffic Networks under Information Provision

2013 ◽  
Vol 361-363 ◽  
pp. 2173-2184 ◽  
Author(s):  
Jun Wang ◽  
Meng Liu ◽  
Hong Mei Zhou ◽  
Ying En Ge
Keyword(s):  
Individual Differences ◽  
Choice Behavior ◽  
Information Quality ◽  
Road Traffic ◽  
Information Provision ◽  
Traffic Information ◽  
Traffic Networks ◽  
Stochastic Choice ◽  
Information Interpretation ◽  
Road Traffic Networks

This paper attempts to model stochastic choice behavior in simultaneous trip route and departure time decision-making on road traffic networks, taking into account information quality and individual differences in information interpretation among the population of travelers. Different from the traditional stochastic model, the proposed choice behavior model assumes that road users simultaneously select the trip routes and departure times that have the largest probabilities of incurring the least generalized travel costs. This model is applicable in both static and dynamic settings and can be applied to both ordinary travelers as well as operators of emergent vehicles, e.g., the fire engine. The preliminary numerical experiments show that the proposed stochastic choice model can reflect the overreaction phenomena reported in studies of traffic information provision and the impacts of the types of traffic information on the effectiveness of information provision. This model opens a potential way to analyze network equilibrium behavior taking into account individual differences in the ability of information interpretation as well as information quality.

scholarly journals Deliberately Stochastic

2019 ◽  
Vol 109 (7) ◽  
pp. 2425-2445 ◽  
Author(s):  
Simone Cerreia-Vioglio ◽  
David Dillenberger ◽  
Pietro Ortoleva ◽  
Gil Riella
Keyword(s):  
Maximal Element ◽  
Choice Function ◽  
Random Utility ◽  
General Representation ◽  
Stochastic Choice

We study stochastic choice as the outcome of deliberate randomization. We derive a general representation of a stochastic choice function where stochasticity allows the agent to achieve from any set the maximal element according to her underlying preferences over lotteries. We show that in this model stochasticity in choice captures complementarity between elements in the set, and thus necessarily implies violations of Regularity/Monotonicity, one of the most common properties of stochastic choice. This feature separates our approach from other models, e.g., Random Utility. (JEL D80, D81)

scholarly journals On covariance estimation when nonrespondents are subsampled

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Wojciech Gamrot
Keyword(s):  
Finite Population ◽  
General Procedure ◽  
Covariance Estimation ◽  
Sample Survey ◽  
Double Sampling ◽  
Parameter Estimates ◽  
Initial Sample ◽  
Two Phase ◽  
Special Cases ◽  
Combined Data

The phenomenon of nonresponse in a sample survey reduces the precision of parameter estimates and introduces the bias. Several procedures have been developed to compensate for these effects. An important technique is the two-phase (or double) sampling scheme which relies on subsampling the nonrespondents and re-approaching them in order to obtain the missing data. This paper focuses on the application of double sampling to estimate the finite population covariance. Two covariance estimators using combined data from the initial sample and the subsample are considered. Their properties are derived. Two special cases of the general procedure are discussed.

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