Determining the number of groups in latent panel structures with an application to income and democracy

Lu, Xun; Su, Liangjun

doi:10.3982/qe517

Cited by 35 publications

(30 citation statements)

References 27 publications

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“…Interestingly, these four groups distribute in roughly four different quadrants in the plane. Table 2 reports the estimation results for each group-specific parameter and those for the pooled estimates, all of which are bias-corrected by using Lu and Su's (2017) bias correction formula and Arellano's (1987) country cluster-robust standard errors. The last column in Table 2 reports the estimate of the long run effect of income on democracy, 1 ∕(1 − 2 ).…”

Section: Estimation Resultsmentioning

confidence: 99%

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Homogeneity pursuit in panel data models: Theory and application

Wang

Phillips

2018

J of Applied Econometrics

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Summary This paper studies the estimation of a panel data model with latent structures where individuals can be classified into different groups with the slope parameters being homogeneous within the same group but heterogeneous across groups. To identify the unknown group structure of vector parameters, we design an algorithm called Panel‐CARDS. We show that it can identify the true group structure asymptotically and estimate the model parameters consistently at the same time. Simulations evaluate the performance and corroborate the asymptotic theory in several practical design settings. The empirical application reveals the heterogeneous grouping effect of income on democracy.

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Section: Estimation Resultsmentioning

confidence: 99%

“…This theorem shows that when the time fixed effects are added to our model, the Panel‐CARDS still gives the oracle estimator with high probability. For inference, one needs to know the asymptotic distribution of

{\overset{α}{true}}^{oracle}

; see Theorem 4.3 in Lu and Su ().…”

Section: Asymptotic Analysis Of Panel‐cardsmentioning

confidence: 99%

Homogeneity pursuit in panel data models: Theory and application

Wang

Phillips

2018

J of Applied Econometrics

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“…We follow the recent econometric literature and treat the unobserved group memberships as a structural parameter. Inference in panel models with a latent group structure has been studied in Lin and Ng (2012), , Sarafidis and Weber (2015), Ando and Bai (2016), Vogt and Linton (2017), Wang, Phillips, and Su (2016), Lu and Su (2017), Vogt and Schmid (2017), and Gu and Volgushev (2018). 5 Previous studies address inference with respect to the group-specific regression curves.…”

Section: Related Literaturementioning

confidence: 99%

Confidence Set for Group Membership

Dzemski¹,

Okui

2018

SSRN Journal

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We develop new procedures to quantify the statistical uncertainty of data-driven clustering algorithms. In our panel setting, each unit belongs to one of a finite number of latent groups with group-specific regression curves. We propose methods for computing unit-wise and joint confidence sets for group membership. The unit-wise sets give possible group memberships for a given unit and the joint sets give possible vectors of group memberships for all units. We also propose an algorithm that can improve the power of our procedures by detecting units that are easy to classify.The confidence sets invert a test for group membership that is based on a characterization of the true group memberships by a system of moment inequalities. To construct the joint confidence, we solve a high-dimensional testing problem that tests group membership simultaneously for all units.We justify this procedure under N, T → ∞ asymptotics where we allow T to be much smaller than N . As part of our theoretical arguments, we develop new simultaneous anti-concentration inequalities for the MAX and the QLR statistics. Monte Carlo results indicate that our confidence sets have adequate coverage and are informative. We illustrate the practical relevance of our confidence sets in two applications.

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“…Computationally, we develop an iterative method to compute the estimates by combining the K-means (Bonhomme and Manresa, 2015) algorithm with Lasso. This algorithm works particularly well when the number of groups is not large, which is typically the case in many applications (Bonhomme and Manresa, 2015;Vogt and Linton, 2016;Lu and Su, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Lu and Su (2017) also studied the same empirical data with group-specific slope coefficients, but they considered individual and time-specific two-way fixed effect panel models.…”

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confidence: 99%

Heterogeneous Structural Breaks in Panel Data Models

Okui

Wang

2017

SSRN Journal

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This paper develops a new model and estimation procedure for panel data that allows us to identify heterogeneous structural breaks. We model individual heterogeneity using a grouped pattern. For each group, we allow common structural breaks in the coefficients. However, the number, timing, and size of these breaks can differ across groups. We develop a hybrid estimation procedure of the grouped fixed effects approach and adaptive group fused Lasso. We show that our method can consistently identify the latent group structure, detect structural breaks, and estimate the regression parameters. Monte Carlo results demonstrate the good performance of the proposed method in finite samples. An empirical application to the relationship between income and democracy illustrates the importance of considering heterogeneous structural breaks. participants at various seminars and conferences for their valuable comments and suggestions.

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Determining the number of groups in latent panel structures with an application to income and democracy

Cited by 35 publications

References 27 publications

Homogeneity pursuit in panel data models: Theory and application

Homogeneity pursuit in panel data models: Theory and application

Confidence Set for Group Membership

Heterogeneous Structural Breaks in Panel Data Models

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