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on Discrete Choice Models |
| By: | Hartleb, J.; Schmidt, M.E. |
| Abstract: | Timetabling for railway services often aims at optimizing travel times for passengers. At the same time, restricting assumptions on passenger behavior and passenger modeling are made. While research has shown that passenger distribution on routes can be modeled with a discrete choice model, this has not been considered in timetabling yet. We investigate how a passenger distribution can be integrated into an optimization framework for timetabling and present two mixed-integer linear programs for this problem. Both approaches design timetables and simultaneously find a corresponding passenger distribution on available routes. One model uses a linear distribution model to estimate passenger route choices, the other model uses an integrated simulation framework to approximate a passenger distribution according to the logit model, a commonly used route choice model. We compare both new approaches with three state-of-the-art timetabling methods and a heuristic approach on a set of artificial instances and a partial network of Netherlands Railways (NS). |
| Keywords: | transportation, timetabling, public transport, route choice, discrete choice model, passenger distribution |
| Date: | 2019–12–17 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:122487&r=all |
| By: | Chie Aoyagi; Alistair Munro |
| Abstract: | The quantification of how aspects of a job are valued by employees sheds light on the potential for labor market reform in Japan. Using a nationwide sample of 1,046 working-age adults, we conduct a choice experiment that examines individuals’ willingness to trade wages against job characteristics such as the extent of overtime, job security, the possibility of work transfer and relocation. Our results suggest that: i) workers have high WTP (willingness to pay) to avoid extreme overtime and work transfer, ii) women have higher WTP than men, and iii) higher WTP for women are driven in part by feelings of guilt. |
| Date: | 2019–11–27 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:19/261&r=all |
| By: | Maria Kyriacou (University of Southampton); Peter C.B. Phillips (Cowles Foundation, Yale University); Francesca Rossi (University of Verona) |
| Abstract: | Spatial units typically vary over many of their characteristics, introducing potential unobserved heterogeneity which invalidates commonly used homoskedasticity conditions. In the presence of unobserved heteroskedasticity, standard methods based on the (quasi-)likelihood function generally produce inconsistent estimates of both the spatial parameter and the coefficients of the exogenous regressors. A robust generalized method of moments estimator as well as a modiï¬ ed likelihood method have been proposed in the literature to address this issue. The present paper constructs an alternative indirect inference approach which relies on a simple ordinary least squares procedure as its starting point. Heteroskedasticity is accommodated by utilizing a new version of continuous updating that is applied within the indirect inference procedure to take account of the parametrization of the variance-covariance matrix of the disturbances. Finite sample performance of the new estimator is assessed in a Monte Carlo study and found to offer advantages over existing methods. The approach is implemented in an empirical application to house price data in the Boston area, where it is found that spatial effects in house price determination are much more signiï¬ cant under robustiï¬ cation to heterogeneity in the equation errors. |
| Keywords: | Spatial autoregression, Unknown heteroskedasticity, Indirect inference, Robust methods, Weights matrix |
| JEL: | C13 C15 C21 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:cwl:cwldpp:2208&r=all |
| By: | Jianhua Duan; Kuntal K. Das (University of Canterbury); Laura Meriluoto (University of Canterbury); W. Robert Reed (University of Canterbury) |
| Abstract: | This study uses meta-analysis to analyze the empirical literature on spillovers and exports. It collects 3,291 estimated spillover effects from 99 studies. The estimated spillover effects in the literature span a large number of types and measures of both exports and spillovers. As a result, we transform estimates to partial correlation coefficients. We analyze these transformed effects using four different versions of Weighted Least Squares (WLS) estimators, incorporating both meta-analytic “Fixed Effects” and “Random Effects”. Our analysis produces three main findings. First, while we estimate a overall mean effect of spillovers on exports that is statistically significant, the size of the effect is economically negligible. Second, we find modest evidence for the existence of publication bias in the empirical literature. Publication bias can arise when researchers and journals have a preference to publish articles that find positive and significant results. While some of our tests indicate the presence of publication bias, in every case the size of the effect is small. Third, using both Bayesian Model Averaging and frequentist meta-regression analysis, we find that some data, estimation, and study characteristics are significant in some regressions. However, only a few of the characteristics are robust, and none are large in size. |
| Keywords: | Spillovers, Exports, Meta-analysis; Meta-Regression Analysis; Bayesian Model Averaging, Partial Correlation Coefficient |
| JEL: | D62 F10 F20 O30 C80 |
| Date: | 2019–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:cbt:econwp:19/19&r=all |
| By: | Thomas Crossley (Institute for Fiscal Studies and Institute for Fiscal Studies, University of Essex); Peter Levell (Institute for Fiscal Studies and Institute for Fiscal Studies); Stavros Poupakis (Institute for Fiscal Studies and University of Essex) |
| Abstract: | Researchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching-based imputation procedures lead to inconsistent estimates. We o?er an easily-implemented correction and correct asymptotic standard errors. We illustrate these with Monte Carlo experiments and empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID). |
| Date: | 2019–06–24 |
| URL: | http://d.repec.org/n?u=RePEc:ifs:ifsewp:19/16&r=all |