Homogeneity pursuit in panel data models: Theory and application

Wang, Wuyi; Phillips, Peter C.B.; Su, Liangjun

doi:10.1002/jae.2632

Cited by 48 publications

(23 citation statements)

References 48 publications

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“…, G K 0 such that β i = β j for all i, j ∈ G k and all 1 ≤ k ≤ K 0 . The problem of estimating the unknown groups and their unknown unknown number has been studied in different versions of this modelling framework in Bonhomme and Manresa (2015), Su et al (2016), Wang et al (2018) and Su and Ju (2018) among others. Note that our clustering methods can be adapted in a straightforward way to a number of semiparametric models which are middle ground between the fully parametric panel models just discussed and our nonparametric framework.…”

Section: Introductionmentioning

confidence: 99%

Multiscale clustering of nonparametric regression curves

Vogt

Linton

2020

Journal of Econometrics

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In a wide range of modern applications, we observe a large number of time series rather than only a single one. It is often natural to suppose that there is some group structure in the observed time series. When each time series is modelled by a nonparametric regression equation, one may in particular assume that the observed time series can be partitioned into a small number of groups whose members share the same nonparametric regression function. We develop a bandwidth-free clustering method to estimate the unknown group structure from the data. More precisely speaking, we construct multiscale estimators of the unknown groups and their unknown number which are free of classical bandwidth or smoothing parameters. In the theoretical part of the paper, we analyze the statistical properties of our estimators. Our theoretical results are derived under general conditions which allow the data to be dependent both in time series direction and across different time series. The technical analysis of the paper is complemented by a simulation study and a real-data application.

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Section: Introductionmentioning

confidence: 99%

Multiscale clustering of nonparametric regression curves

Vogt

Linton

2020

Journal of Econometrics

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“…see Ke et al (2015), Ke et al (2016), Su et al (2016), Su and Ju (2018), Wang et al (2018), Wang and Su (2019), Su and Jin (2019), Ando and Bai (2017), Bonhomme and Manresa (2015), and the references therein. To make the description of the proposed methodology more generic, from now on, y ij does not have to be the length of the second-birth interval;…”

Section: The Proposed Multilevel Modelling Strategymentioning

confidence: 99%

A New Multilevel Modeling Approach for Clustered Survival Data

Zhang

2020

Econom. Theory

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In multilevel modeling of clustered survival data, to account for the differences among different clusters, a commonly used approach is to introduce cluster effects, either random or fixed, into the model. Modeling with random effects may lead to difficulties in the implementation of the estimation procedure for the unknown parameters of interest because the numerical computation of multiple integrals may become unavoidable when the cluster effects are not scalars. On the other hand, if fixed effects are used, there is a danger of having estimators with large variances because there are too many nuisance parameters involved in the model. In this article, using the idea of the homogeneity pursuit, we propose a new multilevel modeling approach for clustered survival data. The proposed modeling approach does not have the potential computational problem as modeling with random effects, and it also involves far fewer unknown parameters than modeling with fixed effects. We also establish asymptotic properties to show the advantages of the proposed model and conduct intensive simulation studies to demonstrate the performance of the proposed method. Finally, the proposed method is applied to analyze a dataset on the second-birth interval in Bangladesh. The most interesting finding is the impact of some important factors on the length of the second-birth interval variation over clusters and its homogeneous structure.

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“…Finally, our paper contributes to the recent panel data literature on group clustering such as Wang et al (2018) who allow for heterogeneous-across but homogeneous-within groups slope coefficients. While our set-up also features unobserved individual health status classified within groups, we explicitly identify their changes across time.…”

Section: Introductionmentioning

confidence: 97%

Endogenous health groups and heterogeneous dynamics of the elderly

Amengual

Bueren

Crego

2021

J of Applied Econometrics

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We propose a methodology to classify individuals into few but meaningful health groups by estimating a panel Markov switching model that exploits rich information from panel household surveys. Using the HRS, we identify four persistent health groups, depending on individual's physical and mental disabilities. Our classification outperforms existing health measures at explaining entry in nursing homes, home health care, out-of-pocket medical expenses, and mortality for individuals in the HRS, ELSA, and SHARE. Through a workhorse model of savings, we recover an asset cost of bad health that is twice as big as when using self-reported health.

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Homogeneity pursuit in panel data models: Theory and application

Cited by 48 publications

References 48 publications

Multiscale clustering of nonparametric regression curves

Multiscale clustering of nonparametric regression curves

A New Multilevel Modeling Approach for Clustered Survival Data

Endogenous health groups and heterogeneous dynamics of the elderly

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