應用動態灰關聯分析研究影響台灣經濟成長率主要因素

灰色系統理論( Gray System Theory) 發表至今約40年，應用小樣本、少數據、不確定性等特性進行研究且研究範圍也越來越廣泛，近十年來有關灰關聯相關研究都屬於靜態資料分析，且都大多採用初值化，最後的結果只能產出一個關聯序；在相同的研究條件下，若使用不同的數據前處理方式，會出現關聯序排序不一致情形。本研究對象為台灣，探究7項(經濟成長率(%)、出生率、台灣之總人口數(千人)、失業率(%)、平均每人所得(美元)、存款加權平均利率(%)、消費者物價指數(CPI)、國民所得(NI))影響台灣經濟成長率之經濟指標因素，運用創新的動態灰色關聯分析，經過動態移動方式產生時間上的先後關係，將靜態資料運用移動的特性產生大量動態原始資料，深入探討使用不同的數據前處理方式分析靜態灰色關聯分析和動態灰色關聯分析之間的差異，發現不同數據前處理方式經過動態灰色關聯分析可產生一組新的關聯序，最後可再將各因素以景氣指標區別（領先、同步及落後指標）對經濟成長的影響程度。本研究結果有以下貢獻：一、本研究使用之六種數據前處理方式進行動態灰關聯分析，結果發現除了初值化X6(消費者物價值數) 和區間值化X7(國民所得(百萬元))靜態灰關聯經過動態灰關聯後的關聯度值是下降的，其它因素關聯度值都比原本靜態灰關聯的關聯度值都高，可證明原始資料經過動態灰關聯分析後會有時間上的先後關係。二、動態灰關聯研究，利用不同數據前處理方法，最後能讓產生一組新的關聯序，對照靜態灰關聯的排序找出最佳影響因素。三、利用動態方式產生時間上的先後關係，產生新的景氣指標找出各因素為影響經濟成長之領先、同步及落後指標。

關鍵字

動態灰關聯分析；台灣經濟成長率；景氣指標

並列摘要

Ever since the grey system theory was presented about 40 years ago, its characteristics such as small samples, few data, and uncertainty have been used for study in the literature with increasingly wider scope. Recent studies on grey relation have included static data analyses, and most of them have adopted initial values with only a relational order produced. Under the same study conditions, if different data preprocessing methods are used, then the relational order will be ranked differently. This study took Taiwan as the object to explore 7 economic indices (birth rate (%), Taiwan’s total population (thousand people), unemployment rate (%), income per capita (USD), weighted average interest rate on deposits (%), Consumer Price Index (CPI), and national income (NI)) and how they affect the economic growth rate. An innovative grey relational dynamic analysis was carried out to generate a time order by the dynamic movement mode, and the static data produced a large amount of dynamic original data with the characteristics of movement. The differences in analyses between grey relational static analysis and grey relational dynamic analysis via different data preprocessing methods were further discussed, finding that different data preprocessing method s generated a new set of relational orders through the latter. Finally, the prosperity index was used to identify the effects of all factors on economic growth (leading, synchronization, and lagging indices). The results of this study have the following co ntributions: 1. In this study, 6 data preprocessing methods were used for grey relational dynamic analysis, and the results showed that, except for the relational degrees of initial value X6 (consumer price index) and interval value X7 (NI (million dollars)) in the static grey relation declining after dynamic grey relation, the relational degrees of other factors were higher than those in the original static grey relation.It can be proved that the original data will have a chronological relationship after dynamic gray correlation analysis. 2. Dynamic gray correlation research, using different data pre-processing methods, can finally generate a new set of correlation orders, and compare the static gray correlation order to find the best influencing factors. 3. Use dynamic methods to generate time sequence, and find out from the new economic indicators that the various factors are leading, synchronization, and lagging indices.