Interpreting heterogeneous coefficient spatial autoregressive panel models

LeSage, James P.; Chih, Yao‐Yu

doi:10.1016/j.econlet.2016.02.033

Cited by 39 publications

(37 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1, Table 2 and Table 3), Figure 1 points to a negative relation between µ and α (w α). Importantly, this "proportionality" between the direct and indirect effects is not a feature of the model specification (LeSage and Chih, 2016).…”

mentioning

confidence: 99%

“…Similarly, LeSage and Chih (2016) interpret the colum-sums of wα j (i.e., α j N l=1 w li , assuming that w ii = 0) as "spill-out effects" of unit i. In particular, this spill-out captures the impact of unit i having the unobserved characteristics of j on the outcome of the neighbours of unit i.…”

mentioning

confidence: 99%

“…We then estimate the model by IV to find that, under a sequential exogeneity assumption, states with a larger/smaller estimated individual effect tend to have larger/smaller negative spatial spillovers. In particular, we consider both "spill-in" and "spill-out" effects (LeSage and Chih, 2016), although only the spillout effects turn out to be statistically significant. Also, while the part of the individual effects associated with the private capital produces negative spatial contagion, the part associated with the public capital produces positive spatial contagion.…”

mentioning

confidence: 99%

“…[Insert Table 3] To be precise, LeSage and Chih (2016) interpret the row-sums of wα j (i.e., N l=1 w il α j , assuming that w ii = 0) as the "spill-in effects" on the outcome of unit i. Thus, this spillin effect captures the impact on the outcome of unit i of all the units neighbouring i having the unobserved characteristics of j.…”

mentioning

confidence: 99%

“…Bearing this in mind, we have used the IV estimates reported in Table 3 to analyse the geographical distribution of the estimated direct individual effects and their spatial spillovers. In particular, our spillover effects are based on LeSage and Chih (2016), who define "spill-in" and "spill-out" effects as the cumulating off-diagonal elements from row-and columnsums of the matrix of marginal effects in (2.2), respectively.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Estimating individual effects and their spatial spillovers in linear panel data models: Public capital spillovers after all?

2017

View full text Add to dashboard Cite

Individual-specific effects and their spatial spillovers are not generally identified in linear panel data models. In this paper we present identification conditions under the assumption that covariates are correlated with the individual-specific effects and derive appropriate GLS and IV estimators for the resulting correlated random effects spatial panel data model.We also illustrate the proposed estimators using a Cobb-Douglas production function specification and US state-level data from Munnell (1990). As in previous studies, we find no evidence of public capital spillovers. However, public capital does play a role in the positive "outwards" spatial contagion of the individual effects.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Estimating individual effects and their spatial spillovers in linear panel data models: Public capital spillovers after all?

2017

View full text Add to dashboard Cite

show abstract

Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices

Aquaro

Bailey

Pesaran

2020

J of Applied Econometrics

View full text Add to dashboard Cite

This paper considers the problem of identification, estimation and inference in the case of spatial panel data models with heterogeneous spatial lag coefficients, with and without (weakly) exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of spatial coefficients are derived. Regularity conditions are established for the QML estimators of individual spatial coefficients, as well as their means (the mean group estimators), to be consistent and asymptotically normal. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations for Gaussian and non-Gaussian errors, and with spatial weight matrices of differing degrees of sparsity. The simulation results are in line with the paper's key theoretical findings even for panels with moderate time dimensions, irrespective of the number of cross section units. An empirical application to U.S. house price changes during the 1975-2014 period shows a significant degree of heterogeneity in spill-over effects over the 338 Metropolitan Statistical Areas considered. JEL-Codes: C210, C230.

show abstract

Heterogeneous Coefficient Spatial Regression Panel Models

Chih¹,

LeSage²

2019

Handbook of Regional Science

View full text Add to dashboard Cite

Interpreting heterogeneous coefficient spatial autoregressive panel models

Cited by 39 publications

References 2 publications

Estimating individual effects and their spatial spillovers in linear panel data models: Public capital spillovers after all?

Estimating individual effects and their spatial spillovers in linear panel data models: Public capital spillovers after all?

Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices

Heterogeneous Coefficient Spatial Regression Panel Models

Contact Info

Product

Resources

About