Matthias Eckardt, Philipp Otto
arXiv 18 Jul 2025 · Statistics — Applications
arXiv:2507.14389 · PDF · DOI · OpenAlex · Extracted main text
Compositional data, such as regional shares of economic sectors or property transactions, are central to understanding structural change in economic systems across space and time. This paper introduces a spatiotemporal multivariate autoregressive model tailored for panel data with composition-valued responses at each areal unit and time point. The proposed framework enables the joint modelling of temporal dynamics and spatial dependence under compositional constraints and is estimated via a quasi maximum likelihood approach. We build on recent theoretical advances to establish identifiability and asymptotic properties of the estimator when both the number of regions and time points grow. The utility and flexibility of the model are demonstrated through two applications: analysing property transaction compositions in an intra-city housing market (Berlin), and regional sectoral compositions in Spain's economy. These case studies highlight how the proposed framework captures key features of spatiotemporal economic processes that are often missed by conventional methods.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kelejian, H. H. and Prucha, I. R (1998) A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive distur… | 0.644 | 2 | 2 | 100% |
| 2 | Aitchison, J (2001) Simplicial inference | 0.644 | 2 | 2 | 100% |
| 3 | Pawlowsky‐Glahn, V. and Buccianti, A (2011) Compositional Data Analysis | 0.644 | 2 | 2 | 100% |
| 4 | Otto, P (2024) A multivariate spatial and spatiotemporal ARCH model self | 0.644 | 2 | 2 | 100% |
| 5 | Tsagris, M., Preston, S., and Wood, A. T. A (2016) Improved classification for compositional data using the $$-transformation | 0.511 | 2 | 1 | 100% |
| 6 | Yu, J., de Jong, R., and Lee, L.-f (2008) Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both $n$ and $T$ are large | 0.511 | 2 | 1 | 100% |
| 7 | Yang, K. and Lee, L.-f (2017) Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models | 0.511 | 2 | 1 | 100% |
| 8 | Aitchinson, J. and Shen, S (1980) Logistic-normal distributions:some properties and uses | 0.405 | 1 | 1 | 100% |
| 9 | Aitchinson, J (1983) Principal component analysis of compositional data | 0.405 | 1 | 1 | 100% |
| 10 | Aitchison, J (1986) The Statistical Analysis of Compositional Data | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 38 scored citations.