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on Discrete Choice Models |
| By: | Mogens Fosgerau (University of Copenhagen); Emerson Melo (Indiana University); Matthew Shum (California Institute of Technology); Jesper R.-V. Sørensen (University of Copenhagen) |
| Abstract: | This note provides several remarks relating to the conditional-choice probability (CCP) based estimation approaches for dynamic discrete-choice models. Specifically, the Arcidiacono and Miller [2011] estimation procedure relies on the “inverse-CCP” mapping (p) from CCP’s to choice-specific value functions. Exploiting the convex-analytic structure of discrete choice models, we discuss two approaches for computing this, using either linear or convex programming, for models where the utility shocks can follow arbitrary parametric distributions. Furthermore, the function is generally distinct from the “selection adjustment” term (i.e. the expectation of the utility shock for the chosen alternative), so that computational approaches for computing the latter may not be appropriate for computing . |
| Keywords: | dynamic discrete choice, random utility, linear programming, convex analysis, convex optimization |
| JEL: | C35 C61 D90 |
| Date: | 2021–02–21 |
| URL: | http://d.repec.org/n?u=RePEc:kud:kuiedp:2103&r=all |
| By: | Bonakdar, Said Benjamin; Roos, Michael W. M. |
| Abstract: | Residential choice does not only depend on properties of the dwelling, neighborhood amenities and affordability, but is also affected by the population composition within a neighbourhood. All these attributes are capitalised in the house price. Empirically, it is not easy to disentangle the effect of the neighbourhood on house prices from the effects of the dwelling attributes. We implement an agentbased model of an urban housing market that allows us to analyse the interaction between residential choice, population composition in a neighbourhood and house prices. Agents differ in terms of education, income and group affiliation (majority vs. minority). The results show that the "wrong" neighbourhood can lead to an average house price depreciation of up to 13,500 monetary units or 7.1 percent. Whereas rich agents can afford to move to preferred places, roughly 13.01% of poor minorities and 8.02% of poor majority agents are locked in their current neighbourhood. By introducing a policy that provides agents more access to credit, we find that all population groups denote higher satisfaction levels. Poor agents show the largest improvements. The general satisfaction level across all population groups increases. However, the extra credit accessibility also drives up house prices and leads to higher wealth inequality within the city. If agents have a preference for status rather than for similarity, the effect of the overall inequality is smaller, since agents become more satisfied living in areas with less similar agents. |
| Keywords: | Agent-based modelling,residential choice,housing demand,neighbourhood characteristics,house prices |
| JEL: | C63 R21 R23 R32 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:894&r=all |
| By: | Alberini, Anna; Horvath, Marco; Vance, Colin |
| Abstract: | The demand for motor fuel should decline when its price rises, but how exactly does that happen? Do people drive less, do they drive more carefully to conserve fuel, or do they do both? To answer these questions, we use data from the German Mobility Panel from 2004 to 2019, taking advantage of the fluctuations in motor fuel prices over time and across locales to see how they affect Vehicle Kilometers Traveled (VKT) and on-road fuel economy (expressed in kilometers per liter). Our reduced-form regressions show that while the VKTs driven by gasoline cars decrease when the price of gasoline rises, their fuel economy tends to get worse. It is unclear why this happens. Perhaps attempts to save on gasoline-cutting on solo driving, forgoing long trips on the highway, driving more in the city-end up compromising the fuel economy. By contrast, both the VKTs and the fuel economy of diesel cars appear to be insensitive to changes in the price of diesel. Latent class models confirm our main findings, including the fact that while fuel prices, car attributes, and household and location characteristics explain much of the variation in the VKTs, it remains difficult to capture the determinants of on-road fuel economy. Since the price elasticity of fuel consumption is the difference between the price elasticity of VKT and the price elasticity of the fuel economy, our results suggest that the fuel economy might be the "weakest link" of price-based policies that seek to address environmental externalities, such as a carbon tax. |
| Keywords: | On-Road fuel economy,price elasticity,vehicle kilometers traveled,motor fuel prices |
| JEL: | Q41 Q53 Q54 R41 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:892&r=all |
| By: | Hongxing Liu (Department of Economics, Lafayette College); Joaquín Gómez-Miñambres (Department of Economics, Lafayette College; Economic Science Institute, Chapman University); Danyi Qi (Department of Agricultural Economics and Agribusiness, Louisiana State University) |
| Abstract: | We use a combination of randomized field experiments and online surveys to test how the menu design affects food choices and food waste. In our field experiment, participants face one of two menus a narrow menu that only displays a small portion of food, or a broad menu that also contains bigger portions. While all options are equally available in both menus, they differ in how easy and fast the different choices can be made. Our results show that, compared to the broad menu, participants in the narrow menu ordered smaller portions of food. Importantly, food intake was similar across conditions, leading to significant food waste reduction under the narrow menu. Our online survey suggest that these results are consistent with a combination of anchoring and menu-dependent self-control theories. We discuss the implication of our results to menu design in real world settings. |
| Keywords: | food waste; food choice; menu design; nudge; anchoring; self-control |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:chu:wpaper:20-37&r=all |
| By: | Kukacka, Jiri; Sacht, Stephen |
| Abstract: | This paper offers a simulation-based method for the estimation of heuristic switching in nonlinear macroeconomic models. Heuristic switching is an important feature of modeling strategy since it uses simple decision rules of boundedly rational heterogeneous agents. The simulation study shows that the proposed simulated maximum likelihood method identifies the behavioral effects that stay hidden for standard econometric approaches. In the empirical application, we estimate the structural and behavioral parameters of the US economy. We are especially able to reliably identify the intensity of choice that governs the models' nonlinear dynamics. |
| Keywords: | Behavioral Heuristics,Heuristic Switching Model,Intensity of Choice,Simulated Maximum Likelihood |
| JEL: | C53 D83 E12 E32 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:cauewp:202101&r=all |
| By: | Verweij, Renske; Helmerhorst, Katrien; Keizer, Renske |
| Abstract: | Objective: Our objective is twofold: First, to examine whether, to what extent and for whom (by sex and educational attainment) work-to-family conflict (W→F-conflict) and family-to-work conflict (F→W-conflict) increased from the pre-Covid-19 period to the lockdown period. Second, to examine whether, to what extent and for whom the associations between W→F-conflict/F→W-conflict and perceived parenting (positive encouragement, coercive parenting and parent-child relationship) became stronger. Background: During the Covid-19 lockdown, parents were asked to provide childcare and home-schooling for their children while also being expected to fulfil their work obligations. Under these circumstances, this study was set out to examine how W→F-conflict/F→W-conflict, perceived parenting and their association were affected. Method: Multilevel regression models were applied to longitudinal data collected among 59 employed mothers and 77 employed fathers with a 3-year-old child. Results: We found that F→W-conflict/W→F-conflict increased most strongly among highly educated mothers, followed by lower/medium educated mothers and highly educated fathers, while no increase or even a decrease was observed among lower/medium educated fathers. We found some associations between W→F-conflict/F→W-conflict with perceived parenting, but these were not consistent for fathers nor mothers, nor across waves. Although overall heightened levels of conflict did strongly not spill-over to mothers’ and fathers’ perceived parenting, our results showed that for some parents, in particular those with high working hours, conflict clearly increased with negative implications for their perceived parenting. Conclusion: With some noteworthy exceptions, increases in F→W-conflict/W→F-conflict did not coincide with decreases in perceived parenting, indicating that most parents did not let increased conflict between work and family affect their parenting. |
| Date: | 2021–02–18 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:cfn84&r=all |
| By: | André De Palma; Mogens Fosgerau; Julien Monardo (Université de Cergy-Pontoise, THEMA) |
| Abstract: | We propose the Inverse Product Differentiation Logit (IPDL) model, a structural (inverse) demand model for differentiated products that captures market segmentation with segments that may overlap in any way. The IPDL model generalizes the nested logit model to allow richer substitution patterns, including complementarity in demand, and can be estimated by linear instrumental variable regression using aggregate data. We use the IPDL model to estimate the demand for cereals in Chicago. We then extend it to a general demand model that is consistent with a utility model of heterogeneous, utilitymaximizing consumers. |
| JEL: | C26 D11 D12 L |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ema:worpap:2021-04&r=all |
| By: | Navarro, Matthew L.; Langlois, Tim J.; Murphy, Jeff; Ochwada-Doyle, Faith A. |
| Abstract: | To effectively manage recreational fisheries, managers require an understanding of the drivers of recreational fisher behaviour. In this preliminary study, we explore drivers of recreational fishing site choice in New South Wales (NSW), Australia. In contrast to previous site choice studies, we investigate whether cues of fishing quality (e.g., depth and rugosity), as opposed to catch expectations can be used to explain site choices. We find that recreational fishers in NSW were more likely to visit sites with lower travel cost, greater water depths, and with fish aggregation devices (FADs). Unsurprisingly, the effect of FADs was particularly pronounced on trips targeting pelagic species. This working paper provides some preliminary evidence that cues of fishing quality could be used to explain site choices, but further research is needed particularly involving higher resolution data on habitats that are likely to be important site quality cues. |
| Keywords: | Environmental Economics and Policy |
| Date: | 2021–01–25 |
| URL: | http://d.repec.org/n?u=RePEc:ags:uwauwp:309191&r=all |
| By: | Ondřej Krčál (Masaryk University); Stefanie Peer (Vienna University of Economics and Business); Rostislav Staněk (Masaryk University) |
| Abstract: | We investigate whether the value of time (VOT) depends on when the corresponding preferences are measured: in advance, just before, or after the time period for which the time preferences are being evaluated. We find that the VOT is highest when elicited just before the time period. This is an indication of the VOT being affected by time-inconsistent, and more specifically, present-biased preferences. We argue that this result may explain why time valuations based on stated preference (SP) data are typically found to be lower than those based on revealed preference (RP) data: most RP surveys evaluate the preferences of respondents close to the time period for which the preferences are being measured, whereas the time instances for which preferences are evaluated in SP surveys tend to be more abstract, or referencing past or future time periods. |
| Keywords: | valuation of time, time inconsistency, present bias, hypothetical bias, lab experiment |
| JEL: | C91 D90 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:mub:wpaper:2021-01&r=all |
| By: | Fryzlewicz, Piotr |
| Abstract: | Many existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients: one is “Wild Binary Segmentation 2” (WBS2), a recursive algorithm for producing what we call a ‘complete’ solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing 0 , … , T- 1 change-points, where T is the data length. The other ingredient is a new model selection procedure, referred to as “Steepest Drop to Low Levels” (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter. |
| Keywords: | segmentation; break detection; jump detection; randomized algorithms; adaptive algorithms; multiscale methods; EP/ L014246/1 |
| JEL: | C1 |
| Date: | 2020–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:103430&r=all |
| By: | Alexandre Bonnet R. Costa; Pedro Cavalcanti G. Ferreira; Wagner P. Gaglianone; Osmani Teixeira C. Guillén; João Victor Issler; Yihao Lin |
| Abstract: | The purpose of this paper is to explore machine learning techniques to forecast the oil price. In the era of big data, we investigate whether new automated tools can improve over traditional approaches in terms of forecast accuracy. Oil price point and density forecasts are built from 22 methods, including regression trees (random forest, quantile regression forest, xgboost), regularization procedures (elastic net, lasso, ridge), standard econometric models and forecast combinations, besides the structural factor model of Schwartz and Smith (2000). The database contains 315 macroeconomic and financial variables, used to build high-dimensional models. To evaluate the predictive power of each method, an extensive pseudo out-of-sample forecasting exercise is built, in monthly and quarterly frequencies, with horizons from one month up to five years. Overall, the results indicate a good performance of the machine learning methods in the short run. Up to six months, the lasso-based models, oil future prices, and the Schwartz-Smith model provide the best forecasts. At longer horizons, forecast combinations also become relevant. In several cases, the accuracy gains in respect to the random walk forecast are statistically significant and reach two-digit figures, in percentage terms, using the R2 out-of-sample statistic; an expressive achievement compared to the previous literature. |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:544&r=all |
| By: | Quentin LAJAUNIE |
| Keywords: | , Impulse response functions, Dichotomous model, Recession prediction, Economic cycles |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:leo:wpaper:2852&r=all |