近年來隨著區域均衡發展的推行，公、私部門對於各縣市區域性統計資料的需求與日遞增。然而大型抽樣調查之小區域結果，常因樣本數較少而產生過大的變異，改善上述問題之統計方法稱為「小區域估計」。本文利用各調查，區域開互相借助力量的方法，建立結合橫斷面與時間序列特性之小區域時空估計模型，並利用分群的方法使模型對於不同的區域特性提供更有效的估計。另外對於缺乏輔助資料以致模型無法建立的問題，亦提出可行的解決方法。最後並以台灣地區人力資源調查為實例進行小區域估計，以檢驗小區域時空估計模型之效果。由於國內許多大型調查之抽樣設計均以人力資源調查為藍本，因此小區域時空估計模型除提供人力資源有效之小區域估計外，亦可為其他調查所使用，以獲致可靠的小區域估計方法。

Small area estimation is becoming increasingly important because of recent ,growing demand for reliable small area statistics. In Taiwan, the current estimations of labor force series for local governments have unacceptably large ,standard errors due to unduly small size of the sample in the sub-areas. Mixed,effects models have been proposed to improve estimates for given small areas by ,borrowing strength from neighboring areas. In .this paper, we proposed mixed ,effects model to solve the small area estimation problem by combining cross-sectional and time series data. We also classify small areas into several groups ,by their characteristics to overcome the lack of auxiliary data. Our mixed effects ,model can reduce variation of the current survey estimates and produce more ,reasonable results.