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Maximum likelihood estimation of a spatial autoregressive model for origin–destination flow variables

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  • Jeong, Hanbat
  • Lee, Lung-fei

Abstract

We introduce a spatial autoregressive hurdle model for nonnegative origin–destination flows yN,ij. The model incorporates a hurdle formulation to elucidate the different data-generating processes for zero and positive flows. Our model specifies three types of spatial influences on flow yN,ij that quantify the impact of third-party characteristics on the flow yN,ij: (i) the effect of outflows from origin j, (ii) the effect of inflows to destination i, and (iii) the effect of flows among third-party units. We account for two-way fixed effects in the model to capture the inherent characteristics of both origins and destinations. We employ maximum likelihood estimation to estimate the model parameters. To address statistical inference issues, we analyze the asymptotic properties of the ML estimator using the spatial near-epoch dependence concept. We confirm the presence of an asymptotic bias that arises from the fixed effects, whose dimensions grow with the sample size. Applying our model to migration flows among U.S. states, we estimate significant spatial influences, particularly from inflows to destinations and outflows from origins. Our findings support the notion that zero and positive flow formations are distinct. Consequently, our proposed model outperforms the spatial autoregressive Tobit specification for origin–destination flows, thus providing a better fit to the data.

Suggested Citation

  • Jeong, Hanbat & Lee, Lung-fei, 2024. "Maximum likelihood estimation of a spatial autoregressive model for origin–destination flow variables," Journal of Econometrics, Elsevier, vol. 242(1).
  • Handle: RePEc:eee:econom:v:242:y:2024:i:1:s0304407624001362
    DOI: 10.1016/j.jeconom.2024.105790
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    More about this item

    Keywords

    Origin–destination flow; Spatial dependence; Hurdle structure; Fixed effects; Maximum likelihood estimation; U.S. migration flow;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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