New submissions

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New submissions for Fri, 27 Mar 20

[1]  arXiv:2003.11565 [pdf]
Title: The Millennial Boom, the Baby Bust, and the Housing Market
Subjects: General Economics (econ.GN)

As baby boomers have begun to downsize and retire, their preferences now overlap with millennials' predilection for urban amenities and smaller living spaces. This confluence in tastes between the two largest age segments of the U.S. population has meaningfully changed the evolution of home prices in the United States. Utilizing a Bartik shift-share instrument for demography-driven demand shocks, we show that from 2000 to 2018 (i) the price growth of four- and five-bedroom houses has lagged the prices of one- and two-bedroom homes, (ii) within local labor markets, the relative home prices in baby boomer-rich zip codes have declined compared with millennial-rich neighborhoods, and (iii) the zip codes with the largest relative share of smaller homes have grown fastest. These patterns have become more pronounced during the latest economic cycle. We show that the effects are concentrated in areas where housing supply is most inelastic. If this pattern in the housing market persists or expands, the approximately 16.5 trillion in real estate wealth held by households headed by those aged 55 or older will be significantly affected. We find little evidence that these upcoming changes have been incorporated into current prices.

[2]  arXiv:2003.11985 [pdf]
Title: Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM)
Comments: 25 pages, 3 figures, 2 tables, discussion paper
Subjects: Theoretical Economics (econ.TH); Multiagent Systems (cs.MA)

In recent years, many scholars praised the seemingly endless possibilities of using machine learning (ML) techniques in and for agent-based simulation models (ABM). To get a more comprehensive understanding of these possibilities, we conduct a systematic literature review (SLR) and classify the literature on the application of ML in and for ABM according to a theoretically derived classification scheme. We do so to investigate how exactly machine learning has been utilized in and for agent-based models so far and to critically discuss the combination of these two promising methods. We find that, indeed, there is a broad range of possible applications of ML to support and complement ABMs in many different ways, already applied in many different disciplines. We see that, so far, ML is mainly used in ABM for two broad cases: First, the modelling of adaptive agents equipped with experience learning and, second, the analysis of outcomes produced by a given ABM. While these are the most frequent, there also exist a variety of many more interesting applications. This being the case, researchers should dive deeper into the analysis of when and how which kinds of ML techniques can support ABM, e.g. by conducting a more in-depth analysis and comparison of different use cases. Nonetheless, as the application of ML in and for ABM comes at certain costs, researchers should not use ML for ABMs just for the sake of doing it.

Cross-lists for Fri, 27 Mar 20

[3]  arXiv:2003.11991 (cross-list from stat.ME) [pdf, ps, other]
Title: Estimating Treatment Effects with Observed Confounders and Mediators
Subjects: Methodology (stat.ME); Machine Learning (cs.LG); Econometrics (econ.EM); Machine Learning (stat.ML)

Given a causal graph, the do-calculus can express treatment effects as functionals of the observational joint distribution that can be estimated empirically. Sometimes the do-calculus identifies multiple valid formulae, prompting us to compare the statistical properties of the corresponding estimators. For example, the backdoor formula applies when all confounders are observed and the frontdoor formula applies when an observed mediator transmits the causal effect. In this paper, we investigate the over-identified scenario where both confounders and mediators are observed, rendering both estimators valid. Addressing the linear Gaussian causal model, we derive the finite-sample variance for both estimators and demonstrate that either estimator can dominate the other by an unbounded constant factor depending on the model parameters. Next, we derive an optimal estimator, which leverages all observed variables to strictly outperform the backdoor and frontdoor estimators. We also present a procedure for combining two datasets, with confounders observed in one and mediators in the other. Finally, we evaluate our methods on both simulated data and the IHDP and JTPA datasets.

Replacements for Fri, 27 Mar 20

[4]  arXiv:1911.02678 (replaced) [pdf, ps, other]
Title: Relative Maximum Likelihood Updating of Ambiguous Beliefs
Authors: Xiaoyu Cheng
Comments: 49 pages, 4 figures
Subjects: Theoretical Economics (econ.TH)
[5]  arXiv:1911.10009 (replaced) [pdf, ps, other]
Title: Guarantees in Fair Division: general or monotone preferences
Comments: 32 pages
Subjects: Theoretical Economics (econ.TH)
[6]  arXiv:1911.11226 (replaced) [pdf, other]
Title: A new set of cluster driven composite development indicators
Comments: Accepted in EPJ Data Science
Subjects: General Economics (econ.GN); Physics and Society (physics.soc-ph); Statistical Finance (q-fin.ST)
[7]  arXiv:1911.12623 (replaced) [pdf, other]
Title: A Principal-Agent approach to study Capacity Remuneration Mechanisms
Comments: 46 pages
Subjects: General Economics (econ.GN)
[8]  arXiv:2003.11221 (replaced) [pdf, other]
Title: Susceptible-Infected-Recovered (SIR) Dynamics of COVID-19 and Economic Impact
Subjects: Populations and Evolution (q-bio.PE); General Economics (econ.GN)
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