本研究旨在探討影響信用卡簽帳金額變動的因素，以提供金融機構相關資訊，以便進行行銷決策。與過去的研究相比，本研究嘗試利用景氣指標來探索與信用卡簽帳金額之間的關聯性，以了解金融機構可以關注哪些景氣指標，以更好地掌握信用卡簽帳金額每月的變動情況。本研究利用 2009 年至 2023 年的月資料，以 ADF 單根檢定處理時間序列變數，再藉由 Lasso 模型找出重要變數，最後利用格蘭傑因果關係檢定及 VAR 向量自我迴歸等統計方式，來找出景氣指標對信用卡簽帳金額之間的關係及建立迴歸模型。我們的實證結果發現，工業和服務業受僱員工淨進入率與信用卡簽帳金額變動率存在格蘭傑因果關係。當工業和服務業受僱員工淨進入率呈現正值時，表示新增的就業機會超過失業情況，通常被視為經濟狀況良好的指標。在這種情況下，民眾可能會增加使用信用卡進行消費。相反地，如果淨進入率為負，則意味著失業情況超過新增的就業機會，暗示經濟不景氣或勞動力市場不穩定。在這種情況下，民眾可能會減少使用信用卡進行消費。

This study aims to explore the factors influencing changes in credit card spending to provide relevant information for financial institutions for marketing decisions. In comparison with past research, this study attempts to utilize business indicators to explore the correlation between credit card spending and business indicators, aiming to understand which business indicators financial institutions should focus on to better grasp the monthly variations in credit card spending. Our data, collected monthly, spans from January 2009 to December 2023.This study uses the ADF test to handle time series variables, identifies important variables by using the Lasso model, and finally, uses statistical methods such as Granger causality tests and vector autoregression (VAR) to identify the relationship between credit card spending and business indicators and establish regression models. Our results reveal that there exists a Granger causality relationship between the net entry rate of employees in the industrial and service sectors and the rate of change in credit card spending. When the net entry rate of employees in the industrial and service sectors is positive, indicating that the number of new job opportunities exceeds unemployment situations, it is generally regarded as an indicator of good economic conditions. In such circumstances, people may increase their use of credit card. Conversely, if the net entry rate is negative, it implies that unemployment situations exceed new job opportunities, suggesting economic downturns or labor market instability. In such scenarios, people may reduce their use of credit cards.

摘要
目錄
1.前言………………………………………………………………………………1
2.文獻回顧……………………………………………………………………3
3.研究資料與方法………………………………………………………7
4.實證結果……………………………………………………………………16
5.結論……………………………………………………………………………20
附錄…………………………………………………………………………………23
參考文獻…………………………………………………………………………25

1.王慧男 (2014)，「信用卡消費金額與總體經濟變數之長期均衡關係」，碩士論文，台北大學統計學系碩士班。
2.王鑫源 (2020)，「信用卡消費金額與總體經濟指標相關性分析-以信用卡處理中心資料庫為例」，碩士論文，輔仁大學金融國際企業學系碩士班。
3.林玟吟與王智賢 (2002)，「信用卡交易對貨幣經濟的影響—台灣之實證研究」， 《人文及社會科學集刊》，16，465-485。
4.洪淑玲 (2010)，「信用卡簽帳金額與總體經濟因素關聯性之研究」，碩士論文，世新大學財務金融學系碩士班。
5.財團法人聯合信用卡處理中心 (2023)，「聯卡中心 40 週年紀念特刊」。
6.張志正 (2019)，「總體經濟對信用卡簽帳金額之彈性係數影響」，碩士論文，台北大學企業管理學系碩士班。
7.郭迺鋒 (2017)，「應用大數據提升台灣民間消費預測」，委託研究計劃。
8.鍾經樊與詹維玲 (2008)，「台灣總體經濟與金融穩定之實證研究」， 《中央銀行季刊》，30，15-44。
9.Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., ... & Eshun, J. (2023), “A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions,” Decision Analytics Journal, 6, 100163.
10.Agarwal, S., Chomsisengphet, S., Meier, S., & Zou, X. (2020), “In the mood to consume: Effect of sunshine on credit card spending,” Journal of Banking & Finance, 121, 105960.
11.Bagga, S., Goyal, A., Gupta, N., & Goyal, A. (2020), “Credit card fraud detection using pipeling and ensemble learning,” Procedia Computer Science, 173, 104-112.
12.Dickey, D. A., & Fuller, W. A. (1981), “Likelihood ratio statistics for autoregressive time series with a unit root,” Econometrica: journal of the Econometric Society, 1057-1072.
13.Granger, C. W. (1969), “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica: journal of the Econometric Society, 424-438.
14.Granger, C. W., & Newbold, P. (1974), “Spurious regressions in econometrics,” Journal of econometrics, 2(2), 111-120.
15.Hirshleifer, D., & Shumway, T. (2003), “Good Day Sunshine: Stock Returns and the Weather,” The Journal of Finance, 58(3), 1009-1032.
16.Horvath, A., Kay, B., & Wix, C. (2023), “The Covid-19 shock and consumer credit: Evidence from credit card data,” Journal of Banking and Finance, 152, 106854.
17.Nelson, C. R., & Plosser, C. R. (1982), “Trends and random walks in macroeconmic time series: some evidence and implications,” Journal of monetary economics, 10(2), 139-162.
18.Schwartz, G. (1978), “Estimating the dimension of a model,” Ann. Stat., 6, 461-464.
19.Sims, C. A. (1980), “Macroeconomics and reality,” Econometrica: journal of the Econometric Society, 1-48.
20.Srinath, T., & Gururaja, H. S. (2022), “Explainable machine learning in identifying credit card defaulters,” Global Transitions Proceedings, 3(1), 119-126.
21.Stockman, A. C. (1981), “Anticipated inflation and the capital stock in a cash in-advance economy,” Journal of monetary economics, 8(3), 387-393.
22.Teng, H. W., & Lee, M. (2019), “Estimation procedures of using five alternative machine learning methods for predicting credit card default,” Review of Pacific Basin Financial Markets and Policies, 22(03), 1950021.
23.Tibshirani, R. (1996), “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288.
24.Viren, M. (1992), “Financial innovations and currency demand: Some new evidence,” Empirical Economics, 17(4), 451-461.