Estimation of fixed effects panel regression models with separable and nonseparable space–time filters

Lee, Lung‐fei; Yu, Jihai

doi:10.1016/j.jeconom.2014.08.006

Cited by 26 publications

(14 citation statements)

References 45 publications

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“…• Consider a spatial autoregressive (SAR) process such as e t = λW N e t + ε t = S −1 N ε t . If we assume S −1 N is uniformly bounded in both row and column sums in absolute value as in Lee and Yu (2015), and ε t is i.i.d. or α-mixing sequence over time, it is easy to check such a SAR process satisfies Assumption 2(i).…”

Section: Appendix a Related Discussion Of The Main Resultsmentioning

confidence: 99%

Heterogeneous panel data models with cross-sectional dependence

Gao

Xia²,

Zhu

2020

Journal of Econometrics

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This paper considers a semiparametric panel data model with heterogeneous coefficients and individual-specific trending functions, where the random errors are assumed to be serially correlated and cross-sectionally dependent. We propose mean group estimators for the coefficients and trending functions involved in the model. It can be shown that the proposed estimators can achieve an asymptotic consistency with rates of root−N T and root−N T h, respectively as (N, T) → (∞, ∞), where N is allowed to increase faster than T. Furthermore, a statistic for testing homogeneous coefficients is constructed based on the difference between the mean group estimator and a pooled estimator. Its asymptotic distributions are established under both the null and a sequence of local alternatives, even if the difference between these estimators vanishes considerably fast (can achieve root-N T 2 rate at most under the null) and consistent estimator available for the covariance matrix is not required explicitly. The finite sample performance of the proposed estimators together with the size and local power properties of the test are demonstrated by simulated data examples, and an empirical application with the OECD health care expenditure dataset is also provided.

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Section: Appendix a Related Discussion Of The Main Resultsmentioning

confidence: 99%

Heterogeneous panel data models with cross-sectional dependence

Gao

Xia²,

Zhu

2020

Journal of Econometrics

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“…The impact of a change in one of the explanatory variables over space falls by the factor ρW for every higher-order neighbor, and over time by the factor τ for every next time period. Due to this property, Lee and Yu ( 2015 ) label it as the separable space-time filter. Although this empirical regularity does not have to be met in theory, empirical evidence in favor of it has been found in many studies.…”

Section: Temporal and Spatiotemporal Lags: Andmentioning

confidence: 99%

The dynamic general nesting spatial econometric model for spatial panels with common factors: Further raising the bar

Elhorst

2021

Rev Reg Res

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“…Debarsy, Ertur, and LeSage () discuss the properties of dynamic spatial Durban models (DSDM) and the interpretation of the coefficients in these models. Lee and Yu () point out that better models than the DSDM exist if time and space effects cannot be separated, but if time and space effects are separable, the computational gains from the DSDM prove superior to existing models for practical estimations (Parent & LeSage, ).…”

Section: Empirical Analysismentioning

confidence: 99%

State cigarette taxes and health expenditures: Evidence from dynamic spatial lag panel models

Hoffer

Humphreys

Ruseski

2019

Papers in Regional Science

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This study explores the relationship between state cigarette taxes and state expenditures on health and hospitals. We address two major sources of endogeneity from (i) the relationship between tax rates and expenditure decisions and (ii) spatial dependence in expenditure policies by using tobacco production as an instrument for cigarette tax rates and through a dynamic spatial Durbin model. We estimate the cigarette tax rate expenditure elasticity to be 0.03 (SR) and 0.87 (LR) for state health spending and 0.05 (SR) and 0.79 (LR) for state hospital spending. Increases in cigarette taxes did not reduce state spending on health over this period.

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Estimation of fixed effects panel regression models with separable and nonseparable space–time filters

Cited by 26 publications

References 45 publications

Heterogeneous panel data models with cross-sectional dependence

Heterogeneous panel data models with cross-sectional dependence

The dynamic general nesting spatial econometric model for spatial panels with common factors: Further raising the bar

State cigarette taxes and health expenditures: Evidence from dynamic spatial lag panel models

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