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Spatial lag dependence in the presence of missing observations

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  • Takahisa Yokoi

    (Tohoku University)

Abstract

We explore the estimation effectiveness of spatial lag models in the presence of missing observations. Spatial lag models are used to measure interdependency between dependent variables. If there are no missing data, it is easy to interpret this spatial autocorrelation process. Very sparsely sampled data are sometimes used in empirical studies. For such data, we observe only a small part of a population containing possible mutual dependencies. Simulation studies based on artificial data confirm the relation between the sampling rate and selection ratio of spatial and non-spatial models. Our findings include the following: (1) Negative spatial autocorrelation of the data-generating process (DGP) may not be observed. (2) Positive spatial autocorrelation of the DGP may be observed, but it is downward-biased. (3) We obtain less-biased estimates if we use a non-row-standardized weight matrix. (4) Non-spatial models tend to be selected in preference to the correct model, the spatial lag model. (5) Estimates of regression coefficients remain almost unbiased.

Suggested Citation

  • Takahisa Yokoi, 2018. "Spatial lag dependence in the presence of missing observations," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 25-40, January.
    • Handle: RePEc:spr:anresc:v:60:y:2018:i:1:d:10.1007_s00168-015-0737-2
    DOI: 10.1007/s00168-015-0737-2
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    References listed on IDEAS

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    1. James P. LeSage & R. Kelley Pace, 2004. "Models for Spatially Dependent Missing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 233-254, September.
    2. Harry Kelejian & Ingmar Prucha, 2010. "Spatial models with spatially lagged dependent variables and incomplete data," Journal of Geographical Systems, Springer, vol. 12(3), pages 241-257, September.
    3. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    4. Giuseppe Arbia & Giuseppe Espa & Diego Giuliani, 2016. "Dirty spatial econometrics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(1), pages 177-189, January.
    5. D A Griffith & R J Bennett & R P Haining, 1989. "Statistical Analysis of Spatial Data in the Presence of Missing Observations: A Methodological Guide and an Application to Urban Census Data," Environment and Planning A, , vol. 21(11), pages 1511-1523, November.
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    Cited by:

    1. Michal Kvasnička, 2022. "Can we ignore spatial dependence when evaluating mergers?," Empirical Economics, Springer, vol. 62(3), pages 1323-1344, March.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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