2021/01/25 by Abhimanyu Gupta, Gupta, Abhimanyu, Xi Qu +1
Economics, Econometrics and Finance · Mathematics · #Econometrics (econ.EM) #Economic and Environmental Valuation #FOS: Economics and business #FOS: Mathematics #Spatial and Panel Data Analysis #Statistical Methods and Inference #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2101.10255
openalex publication_date 2021/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the `space' being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of settings. These include spatial error models of varying types and levels of complexity. Under a new smooth spatial dependence condition, our test statistic is asymptotically standard normal. To prove the latter property, we establish a central limit theorem for quadratic forms in linear processes in an increasing dimension setting. Finite sample performance is investigated in a simulation study, with a bootstrap method also justified and illustrated, and empirical examples illustrate the test with real-world data.