The Fence Methods

Jiang, Jiming

doi:10.1155/2014/830821

Cited by 6 publications

(2 citation statements)

References 46 publications

(97 reference statements)

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“…This is called 'model averaging'. Also, one would like to combine the concepts of significance and importance into one procedure, and the so-called 'fence method' provides the required flexibility by allowing the criterion of optimality for the selection within the fence to incorporate scientific, economical, political, and even legal concerns, in addition to statistical ones (Jiang et al, 2008, andNguyen, 2015).…”

Section: Estimation Resultsmentioning

confidence: 99%

Earthquake Risk Embedded in Property Prices: Evidence From Five Japanese Cities

Ikefuji

Laeven

Magnus

et al. 2021

Journal of the American Statistical Association

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A c c e p t e d M a n u s c r i p tWe analyze the impact of short-run (90 days) and long-run (30 years) earthquake risk on real estate transaction prices in five Japanese cities (Tokyo, Osaka, Nagoya, Fukuoka, and Sapporo), using quarterly data over the period 2006-2015. We exploit a rich panel data set (331,343 observations) with property characteristics, ward attractiveness information, macroeconomic variables, and longrun seismic hazard data, supplemented with short-run earthquake probabilities generated from a seismic excitation model using historical earthquake occurrences. We design a hedonic property price model that allows for subjective probability weighting, employ a multivariate error components structure, and develop associated maximum likelihood estimation and variance computation procedures. Our approach enables us to identify the total compensation for earthquake risk embedded in property prices, to decompose this into pieces stemming from short-run and long-run risk, and to distinguish between objective and subjectively weighted ('distorted') earthquake probabilities. We find that objective long-run earthquake probabilities have a statistically significant negative impact on property prices, whereas short-run earthquake probabilities become statistically significant only when we allow them to be distorted. The total compensation for earthquake risk amounts to an average -2.0% of log property prices, slightly more than the annual income of a middle-income Japanese household.

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Section: Estimation Resultsmentioning

confidence: 99%

Earthquake Risk Embedded in Property Prices: Evidence From Five Japanese Cities

Ikefuji

Laeven

Magnus

et al. 2021

Journal of the American Statistical Association

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“…If there are multiple models within the fence at the minimum dimension, then the model with the smallest Q(α) is selected. For a recent review of the fence and related methods, see Jiang (2014).…”

Section: The Simplified Adaptive Fencementioning

confidence: 99%

mplot: An R Package for Graphical Model Stability and Variable Selection Procedures

Tarr¹,

Müller²,

Welsh

2018

J. Stat. Soft.

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The mplot package provides an easy to use implementation of model stability and variable inclusion plots (Müller and Welsh 2010;Murray, Heritier, and Müller 2013) as well as the adaptive fence (Jiang, Rao, Gu, and Nguyen 2008; Jiang, Nguyen, and Rao 2009) for linear and generalized linear models. We provide a number of innovations on the standard procedures and address many practical implementation issues including the addition of redundant variables, interactive visualizations and the approximation of logistic models with linear models. An option is provided that combines our bootstrap approach with glmnet for higher dimensional models. The plots and graphical user interface leverage state of the art web technologies to facilitate interaction with the results. The speed of implementation comes from the leaps package and cross-platform multicore support.Keywords: model selection, variable selection, linear models, mixed models, generalized linear models, fence, R. Graphical tools for model selectionIn this article we introduce the mplot package (Tarr, Müller, and Welsh 2018) for R (R Core Team 2017), which provides a suite of interactive visualizations and model summary statistics for researchers to use to better inform the variable selection process and is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/ package=mplot. The methods we provide rely heavily on various bootstrap techniques to give an indication of the stability of selecting a given model or variable and even though not done here, could be implemented with resampling methods other than the bootstrap, for example cross-validation. The 'm' in mplot stands for model selection/building and we anticipate that in future more graphs and methods will be added to the package to further aid better and more stable building of regression models. The intention is to encourage researchers to engage more closely with the model selection process, allowing them to pair

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