Nonstationary dynamic models with finite dependence

Semiparametric Bayesian estimation of dynamic discrete choice models

Discrete choice models have received widespread acceptance in transport research over the past three decades, being used in travel demand modelling and behavioural analysis; however, their applications have been mainly developed in a static context. There have been several dynamic models in transportation; but these formulations are not based on dynamic optimization principles and do not allow for changes in external factors. With the continuous and rapid changes in modern societies (i.e. introduction of advanced technologies, aggressive marketing strategies and innovative policies) it is more and more recognized by researchers in various disciplines from economics to social science that choice situations take place in a dynamic environment and that strong interdependencies exist among decisions made at different points in time. Dynamic discrete choice models (DDCMs) describe the behaviour of a forward-looking economic agent who chooses between several alternatives repeatedly over time. DDCMs are usually specified as an optimal stopping problem, where agents decide when to make a change in ownership of durable goods or in their behaviour. In this paper, we present the application of the dynamic formulation to short- to medium-term vehicle-holding decisions.

The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service

Summary We consider dynamic discrete choice models with heterogeneity in both the levels parameter and the state dependence parameter. We first present an empirical analysis that motivates the theoretical analysis which follows. The theoretical analysis considers a simple two-state, first-order Markov chain model without covariates in which both transition probabilities are heterogeneous. Using such a model we are able to derive exact small sample results for bias and mean squared error (MSE). We discuss the maximum likelihood approach and derive two novel estimators. The first is a bias corrected version of the Maximum Likelihood Estimator (MLE) although the second, which we term MIMSE, minimizes the integrated mean square error. The MIMSE estimator is always well defined, has a closed-form expression and inherits the desirable large sample properties of the MLE. Our main finding is that in almost all short panel contexts the MIMSE significantly outperforms the other two estimators in terms of MSE. A final section extends the MIMSE estimator to allow for exogenous covariates.

Chapter 56 Computationally intensive methods for integration in econometrics

Joint Analysis of the Discount Factor and Payoff Parameters in Dynamic Discrete Choice Models

Identifying the Discount Factor in Dynamic Discrete Choice Models

Semiparametric estimation of dynamic discrete choice models

Structural Estimation Using Parametric Mathematical Programming with Equilibrium Constraints and Homotopy Path Continuation

Two key issues in the literature on female labor supply are (i) whether persistence in employment status is due to unobserved heterogeneity or state dependence, and (ii) whether fertility is exogenous to labor supply. Until recently, the consensus was that unobserved heterogeneity is very important and fertility is endogenous. Hyslop (1999) challenged this. Using a dynamic panel probit model of female labor supply including heterogeneity and state dependence, he found that adding autoregressive errors led to a substantial diminution in the importance of heterogeneity. This, in turn, meant he could not reject that fertility is exogenous. Here, we extend Hyslop (1999) to allow classification error in employment status, using an estimation procedure developed by Keane and Wolpin (2001) and Keane and Sauer (2005). We find that a fairly small amount of classification error is enough to overturn Hyslop's conclusions, leading to overwhelming rejection of the hypothesis of exogenous fertility. © 2009 The Econometric Society.

We seek evidence for economic and social mechanisms that aim to explain the relationship between employment and crime. We use the distinctive features of social welfare for identification. We consider a sample of disadvantaged males from The Netherlands who are observed between ages 18 and 32 on a monthly time scale. We simultaneously model the offending, employment and social welfare variables using a dynamic discrete choice model, where we allow for state dependence, reciprocal effects and time-varying unobserved heterogeneity. We find significant negative bi-directional structural effects between employment and property crime. Robustness checks show that only regular employment is able to significantly reduce the offending probability. Further, a significant uni-directional effect is found for the public assistance category of social welfare on property offending. The results highlight the importance of economic incentives for explaining the relationship between employment and crime for disadvantaged individuals. For these individuals the crime reducing effects from the public assistance category of social welfare are statistically equivalent to those from employment, which suggests the importance of financial gains. Further, the results suggest that stigmatizing effects from offending severely reduce future employment probabilities.

We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variables spans nonparametric neighborhoods of a given parametric specification while other “structural” features of the model are maintained. Our approach recasts the infinite‐dimensional problem of optimizing the counterfactual with respect to the distribution of latent variables (subject to model constraints) as a finite‐dimensional convex program. We also develop an MPEC version of our method to further simplify computation in models with endogenous parameters (e.g., value functions) defined by equilibrium constraints. We propose plug‐in estimators of the bounds and two methods for inference. We also show that our bounds converge to the sharp nonparametric bounds on counterfactuals as the neighborhood size becomes large. To illustrate the broad applicability of our procedure, we present empirical applications to matching models with transferable utility and dynamic discrete choice models.

PurposeThis paper investigates the employment dynamics of Greek married women.Design/methodology/approachLongitudinal/panel data for the period 1995‐2001 and dynamic discrete choice models are used for estimation purposes.FindingsIt is found that the probability of being employed is influenced by observed individual characteristics (e.g. human capital, fertility and unearned income), while genuine state dependence and unobserved heterogeneity constitute major sources of observed serial persistence. The results show that lagged employment affects current employment decisions in a systematic way, and that the non‐contemporaneous effects of unearned income and fertility correlate with unobserved heterogeneity. The estimated average partial effects reveal that an employed woman in t−1 has a probability of being employed in t that is almost 50 percentage points higher than for a non‐employed woman.Practical implicationsThe presence of state dependence and unobserved heterogeneity imply that the bridging of the female employment gap between Greece and its EU partners is expected to follow a slow, long‐term course.Originality/valueGreek female labour force participation has been studied only under a static analytical framework. This is the first study to investigate employment decisions of Greek married women in an inter‐temporal setting.

Leaving Your Mamma: Why so Late in Italy?

In this paper, we present a systematic approach based on dynamic discrete choice models (DDCM) to investigate individuals’ forward-looking mode choice behaviours in daily travel tours with multiple destinations. We propose a novel network transformation model that encompasses the entire decision space of all feasible mode combinations for every observed trip/tour in the dataset. By applying the well-established Recursive Logit model structure commonly used in path choice modelling, we address the tour mode choice problem effectively and quantify forward looking considerations in the mode choice process. The proposed model captures the complex considerations individuals take into account when making mode choices. The network transformation incorporates downstream mode limitations into the preceding mode choice options, enabling us to model individuals’ forward-looking behaviour and gain insights into how considerations of future trips such as a shopping in the evening, or school pick-up trip influence previous mode choice decisions earlier in the day. Uncovering and quantifying these hidden forward-looking factors can help modellers better explain the private car usage usually observed for the entire sequences of daily trips, even in presence of competitive alternative modes. The proposed network transformation also enables us to measure the effect of the requirement/preference to return private vehicles (car, motorcycle, and bicycle) home on mode choices in home-bound trips, and subsequently, on the entire daily mode choice decisions. To validate the proposed model, we utilise the VISTA household travel survey data from the Melbourne Metropolitan area in Australia. The model is compared against baseline models, demonstrating its statistical advantages. Additionally, intuitive illustrations using the data showcase the model’s efficacy. From transport planning and policy perspective, tour-based mode choice modelling provides a more comprehensive and precise understanding of travel behaviour by considering the sequence of trips made by an individual. This can help capture the interactions and dependencies between different trips, which trip-based models may overlook. The proposed model is more suitable for analysing the effects of policy interventions like congestion pricing, public transport investments, or new mobility initiatives, as they can better represent the cascading effects of such policies across multiple trips.