Informed regional policy needs good regional data. As regional data series for key economic variables are generally absent whereas national-level time series data for the same variables are ubiquitous, we suggest an approach that leverages this advantage. We hypothesize the existence of a pervasive “common factor” represented by the national time series that affects regions differentially. We provide an empirical illustration in which national FDI is used in place of panel data for FDI, which are absent. The proposed methodology is tested empirically with respect to the determinants of regional demand for housing. We use a quasi-experimental approach to compare the results of a “common correlated effects” (CCE) estimator with a benchmark case when absent regional data are omitted. Using three common factors relating to national population, income and housing stock, we find mixed support for the common correlated effects hypothesis. We conclude by discussing how our experimental design may serve as a methodological prototype for further tests of CCE as a solution to the absent spatial data problem.

知情的区域政策需要良好的区域数据。由于通常缺少关键经济变量的区域数据序列，而全国范围内相同变量的时间序列数据无处不在，因此我们建议一种利用这种优势的方法。我们假设存在一个以国家时间序列为代表的普遍性“共同因素”，该因素对地区的影响不同。我们提供了一个经验说明，其中使用了国家FDI来代替缺少FDI面板数据的情况。对区域住房需求的决定因素进行了经验方法检验。当缺少区域数据时，我们使用准实验方法将“共同相关效应”（CCE）估计器的结果与基准案例进行比较。使用与人口有关的三个共同因素，在收入和住房存量方面，我们发现对常见的相关影响假设的支持不一。最后，我们讨论了我们的实验设计如何作为CCE进一步测试的方法原型，以解决缺少空间数据的问题。