Economics - Research Publications
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ItemPosterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference(De Gruyter, 2023)Increases in the use of Bayesian inference in applied analysis, the complexity of estimated models, and the popularity of efficient Markov chain Monte Carlo (MCMC) inference under conjugate priors have led to more scrutiny regarding the specification of the parameters in prior distributions. Impact of prior parameter assumptions on posterior statistics is commonly investigated in terms of local or pointwise assessments, in the form of derivatives or more often multiple evaluations under a set of alternative prior parameter specifications. This paper expands upon these localized strategies and introduces a new approach based on the graph of posterior statistics over prior parameter regions (sensitivity manifolds) that offers additional measures and graphical assessments of prior parameter dependence. Estimation is based on multiple point evaluations with Gaussian processes, with efficient selection of evaluation points via active learning, and is further complemented with derivative information. The application introduces a strategy to assess prior parameter dependence in a multivariate demand model with a high dimensional prior parameter space, where complex prior-posterior dependence arises from model parameter constraints. The new measures uncover a considerable prior dependence beyond parameters suggested by theory, and reveal novel interactions between the prior parameters and the elasticities.
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ItemFood Price Elasticities for Policy Interventions: Estimates from a Virtual Supermarket Experiment in a Multistage Demand Analysis with (Expert) Prior Information(WILEY, 2021-12)Food price elasticities (PEs) are essential for evaluating the impacts of food pricing interventions to improve dietary and health outcomes. This paper innovates the use of experimental purchasing data from a recent New Zealand virtual supermarket experiment to estimate PEs for a large set of disaggregated foods across major food groups relevant for food policies in a Bayesian multistage demand framework. We propose the use of available prior information to elicit prior demand parameter assumptions that are consistent with published PEs and economic assumptions and are weighted according to expert knowledge, increasing precision in PE inference and policy predictions, and yielding somewhat stronger price effects.
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ItemEfficient selection of hyperparameters in large Bayesian VARs using automatic differentiation(Wiley, 2020-09-01)Large Bayesian vector autoregressions with the natural conjugate prior are now routinely used for forecasting and structural analysis. It has been shown that selecting the prior hyperparameters in a data‐driven manner can often substantially improve forecast performance. We propose a computationally efficient method to obtain the optimal hyperparameters based on automatic differentiation, which is an efficient way to compute derivatives. Using a large US data set, we show that using the optimal hyperparameter values leads to substantially better forecast performance. Moreover, the proposed method is much faster than the conventional grid‐search approach, and is applicable in high‐dimensional optimization problems. The new method thus provides a practical and systematic way to develop better shrinkage priors for forecasting in a data‐rich environment.
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ItemModeling and calculating the effect of treatment at baseline from panel outcomes(ELSEVIER SCIENCE SA, 2007-10)
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ItemStudy protocol: combining experimental methods, econometrics and simulation modelling to determine price elasticities for studying food taxes and subsidies (The Price ExaM Study)(BMC, 2016-07-19)BACKGROUND: There is a need for accurate and precise food price elasticities (PE, change in consumer demand in response to change in price) to better inform policy on health-related food taxes and subsidies. METHODS/DESIGN: The Price Experiment and Modelling (Price ExaM) study aims to: I) derive accurate and precise food PE values; II) quantify the impact of price changes on quantity and quality of discrete food group purchases and; III) model the potential health and disease impacts of a range of food taxes and subsidies. To achieve this, we will use a novel method that includes a randomised Virtual Supermarket experiment and econometric methods. Findings will be applied in simulation models to estimate population health impact (quality-adjusted life-years [QALYs]) using a multi-state life-table model. The study will consist of four sequential steps: 1. We generate 5000 price sets with random price variation for all 1412 Virtual Supermarket food and beverage products. Then we add systematic price variation for foods to simulate five taxes and subsidies: a fruit and vegetable subsidy and taxes on sugar, saturated fat, salt, and sugar-sweetened beverages. 2. Using an experimental design, 1000 adult New Zealand shoppers complete five household grocery shops in the Virtual Supermarket where they are randomly assigned to one of the 5000 price sets each time. 3. Output data (i.e., multiple observations of price configurations and purchased amounts) are used as inputs to econometric models (using Bayesian methods) to estimate accurate PE values. 4. A disease simulation model will be run with the new PE values as inputs to estimate QALYs gained and health costs saved for the five policy interventions. DISCUSSION: The Price ExaM study has the potential to enhance public health and economic disciplines by introducing internationally novel scientific methods to estimate accurate and precise food PE values. These values will be used to model the potential health and disease impacts of various food pricing policy options. Findings will inform policy on health-related food taxes and subsidies. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12616000122459 (registered 3 February 2016).
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ItemAnalysis of treatment response data from eligibility designs(ELSEVIER SCIENCE SA, 2008-06)
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ItemBayesian Fuzzy Regression Discontinuity Analysis and Returns to Compulsory Schooling(Wiley, 2016-09)This paper is concerned with the use of a Bayesian approach to fuzzy regression discontinuity (RD) designs for understanding the returns to education. The discussion is motivated by the change in government policy in the UK in April of 1947, when the minimum school leaving age was raised from 14 to 15-a change that had a discontinuous impact on the probability of leaving school at age 14 for cohorts who turned 14 around the time of the policy change. We develop a Bayesian fuzzy RD framework that allows us to take advantage of this discontinuity to calculate the effect of an additional year of education on subsequent log earnings for the (latent) class of subjects that complied with the policy change. We illustrate this approach with a new dataset composed from the UK General Household Surveys.
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ItemBayesian Treatment Effects Models with Variable Selection for Panel Outcomes with an Application to Earnings Effects of Maternity Leave(Elsevier, 2016-07)We propose two alternative Bayesian treatment effect modeling and inferential frameworks for panel outcomes to estimate dynamic earnings effects of a long maternity leave on mothers’ subsequent earnings. Modeling of the endogeneity of the treatment and the panel structure of the earnings are based on the modeling tradition of the Roy switching regression model and the shared factor approach, respectively. We implement stochastic variable selection to test, for example, for the presence of different dynamics under the treatment. Exploiting a change in maternity leave policy and Austrian registry data we identify substantial negative but steadily decreasing earnings effects over a 5 years period.
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ItemMarijuana on Main Street? Estimating Demand in Markets with Limited Access(AMER ECONOMIC ASSOC, 2016-08)Marijuana is the most common illicit drug with vocal advocates for legalization. Among other things, legalization would increase access and remove the stigma of illegality. Our model disentangles the role of access from preferences and shows that selection into access is not random. We find that traditional demand estimates are biased resulting in incorrect policy conclusions. If marijuana were legalized, those under 30 would see modest increases in use of 28 percent, while on average use would increase by 48 percent (to 19.4 percent). Tax policies are effective at curbing use, where Australia could raise AU$1 billion (and the United States US$12 billion). (JEL D12, H25, K14, K42)