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on Discrete Choice Models |
| By: | Kelsey Jack; Kathryn McDermott; Anja Sautmann |
| Abstract: | Multiple price lists are a convenient tool to elicit willingness to pay (WTP) in surveys and experiments, but choice patterns such as “multiple switching” and “never switching” indicate high error rates. Existing measurement approaches often do not provide accurate standard errors and cannot correct for bias due to framing and order effects. We propose to combine a randomization approach with a random-effects latent utility model to detect bias and account for error. Data from a choice experiment in South Africa shows that significant order effects exist which, if uncorrected, would lead to distorted conclusions about subjects’ preferences. We provide templates to create a multiple price list survey instrument in SurveyCTO and analyze the resulting data using our proposed methods. |
| JEL: | C91 C93 D46 O12 Q51 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30433&r= |
| By: | Mia Lu; Nick Netzer |
| Abstract: | We extend the swaps index of rationality, introduced by Apesteguia and Ballester (2015) for a finite set of alternatives, to the standard consumer choice setting with infinite commodity spaces. Applications include consumer demand from competitive budget sets and the state-space approach to choice under uncertainty. We are primarily interested in Apesteguia and Ballester's result that the swaps index recovers the decision-maker's true preference from choice data for a large class of boundedly rational behavioral models. We show that this result still holds in the consumer choice setting under a suitably defined monotonicity condition. This condition is satisfied for various models of interest but violated for others. |
| Keywords: | Measures of rationality, revealed preference, behavioral welfare economics |
| JEL: | D01 D11 D60 D90 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:zur:econwp:418&r= |
| By: | Sebastian Kripfganz (University of Exeter Business School); Jörg Breitung (University of Cologne) |
| Abstract: | In the presence of unobserved group-specific heterogeneity, the conventional fixed-effects and random-effects estimators for linear panel data models are biased when the model contains a lagged dependent variable and the number of time periods is small. We present a computationally simple bias-corrected estimator with attractive finite-sample properties, which is implemented in our new xtdpdbc Stata package. The estimator relies neither on instrumental variables nor on specific assumptions about the initial observations. Because it is a method-of-moments estimator, standard errors are readily available from asymptotic theory. Higher-order lags of the dependent variable can be accommodated as well. A useful test for the correct model specification is the Arellano–Bond test for residual 3 autocorrelation. The random-effects versus fixed-effects assumption can be tested using a Hansen overidentification test or a generalized Hausman test. The user can also specify a hybrid model, in which only a subset of the exogenous regressors satisfies a random-effects assumption. |
| Date: | 2022–09–10 |
| URL: | http://d.repec.org/n?u=RePEc:boc:lsug22:05&r= |
| By: | Nick Cox (Durham University, UK) |
| Abstract: | This is a round-up of not quite utterly obvious tips and tricks for graph axes, using both official and community-contributed commands. Ever needed a logarithmic scale but found default labels undesirable? a slightly non-standard scale such as logit, reciprocal or root? a tick to be suppressed? labels between ticks, not at them? automagic choice of “nice” labels under your control? Community-contributed commands mentioned will include mylabels, myticks, nicelabels, niceloglabels, qplot and transplot. |
| Date: | 2022–09–10 |
| URL: | http://d.repec.org/n?u=RePEc:boc:lsug22:11&r= |
| By: | Álvaro A. Gutiérrez-Vargas (Research Centre for Operation Research and Statistics (ORSTAT), KU Leuven); Ziyue Zhu (Research Centre for Operation Research and Statistics (ORSTAT), KU Leuven); Martina Vandebroek (Research Centre for Operation Research and Statistics (ORSTAT), KU Leuven) |
| Abstract: | Stata has a strong suite of survey data-analysis references and tools and remains the primary choice for researchers working with survey data. On the other hand, R is the primary choice for data visualization in many academic papers, given its flexibility, especially when using the ggplot2 package based on the design philosophy of The Grammar of Graphics. An unfulfilled need for many researchers is innovatively presenting survey data-analysis results without feeling limited by working within one statistical software only. This presentation discusses a workflow of using Stata for analysis and exporting the results through the postfile commands, then handing the data off to R to create a rich array of figures. As a proof of concept, the presentation will show results from an ongoing health economics research project from the Philippines of around 200,000 observations from national income and expenditure survey data to create publication-quality dumbbell plots, concentration curves, and Pen’s parades. Finally, the presentation will briefly describe how to share code and results in a public repository like Github. |
| Date: | 2022–09–10 |
| URL: | http://d.repec.org/n?u=RePEc:boc:lsug22:17&r= |
| By: | Hintermann, Beat (University of Basel); Thommen, Christoph |
| Abstract: | Using data from a field experiment, we provide estimates for the own-price elasticity of train travel in Switzerland. Our estimates are based on exogenous changes to the level of discounts for long-distance trains and thus avoid the usual endogeneity problem between demand-dependent discounts. Besides the price, we also vary the length of the pre-sale period during the experiment, which allows us to recover the relative effectiveness of pricing and timing measures. We compute own-price elasticities of around -0.7. Extending the pre-sale deadline by one hour leads to an increase in the pre-sale of discount tickets by 2.1%, which is equivalent to a price decrease by 3.1%. Reducing the price by 10% causes customers to purchase the discount ticket 7 hours earlier. Our results help design measures for peak-shifting in transport at least societal cost. |
| Keywords: | Field Experiments, Public Transport Systems, Train, Dynamic Pricing, Switzerland |
| JEL: | L92 R41 L11 C93 |
| Date: | 2022–07–15 |
| URL: | http://d.repec.org/n?u=RePEc:bsl:wpaper:2022/05&r= |