Bidarkota, P.V. (1998), ”The Comparative Forecast Performance of Univariative and Multivariate Models: an Application to Real Interest Rate Forecasting.” International Journal of forecasting, Vol.14, pp.457-468.
Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroscedasticity.” Journal of Econometrics, Vol.31, pp.307-327.
Box, G. E. P. and Jenkins, G.M. (1976), Time Series Analysis Forecasting and Control, 2nd ed., Holden-Day, San Francisco.
Charitou, A. and Charalambous, C. (1996), “The Prediction of Earnings Using Financial Statement Information: Empirical Evidence with Logit Models and Artificial Neural Networks.” International Journal of Intelligent Systems in Accounting, Finance and Management, Vol.5, pp.199-215.
Denton, J. (1995), “Causal Forecasting with Neural Networks.” Journal of Business Forecasting Methods and Systems, vol.14, pp.17-20.
Dickey, D. and Fuller, W. (1979), “Distribution of the Estimators for Autoregressive Time Series with a Unit Root.” Journal of American Statistical Association, Vol.74, pp.427-431.
Engle, R.F. (1982),”Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation.” Econometrica, Vol.55, pp.987-1008.
Gonzalez, S. (2000), “Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models.” Working paper, Canada: Department of Finance.
Gujarati, D.N. (2003), Basic Econometrics, 4th ed., McGraw-Hill.
Judd, J. P. and Rudebusch, G.G. (1998), “Taylor’s Rule and the Fed: 1970-1997.” Federal Reserve Bank of San Francisco Economic Review, Vol.3, pp.3-16.
Kim, S. T. and Noh, H. J. (1997), ”Predictability of Interest Rates Using Data Mining Tool：A Comparative Analysis of Korea and the US.” Expert Systems with Applications, Vol.13, pp.85-95.
Lewis, C. D. (1982), Industrial and Business Forecasting Methods, Butterworths.
Ljung, G. M. and Box, G. E. P. (1978), “On a Measure of Lock of Fit in Time Series Models.” Biometrika, Vol. 65, pp.297-303.
Mehra, Y. P. (2002), “Level and Growth Policy Rules and Actual Fed Policy since 1979.” Journal of Economics & Business, Vol.54, pp.575-594.
Pollard, P. S. (2003), “A Look Inside Two Central Banks: The European Central Bank and the Federal Reserve”, Federal Reserve Bank of ST. Louis Review, Vol.85, pp.11-30.
Qi, M. and Zhang, G. P. (2001), “An Investigation of Model Selection Criteria for Neural Network Time Series Forecasting”, European Journal of Operational Research, Vol.132, pp.666-680.
Roley, V. and Sellon, G. H. (1996), “The Response of the Term Structure of Interest Rates to Federal Funds Rate Target Changes”, working paper, Federal Reserve Bank of Kansas City.
Saltoglu, B. (2003), “Comparing Forecasting Ability of Parametric and Non-parametric Methods：an Application with Canadian Monthly Interest Rates.” Applied Financial Economics, Vol.13, pp.169-176.
Sarno, L. Thornton, D.L. and Valente, G. (2004), “Federal Funds Rate Prediction”, working paper, Federal Reserve Bank of ST. Louis.
尤子源（2001），隔夜拆款利率預測模型之研究比較，高雄第一科技大學金融營運系碩士論文。伍宇文（1996），「應用類神經網路於利率預測之研究分析」，台灣經濟金融月刊，第三十八卷第八期，頁1~9。
初曉峰（2004），利率預測模型之研究—以台灣地區票劵市場為例，實踐大學企業管理研究所碩士論文。吳蕙如（1997），「利率預測與銀行準備部位操作之研究—神經網路與遺傳程式之應用」，財稅研究，第二十九卷第三期，頁121~139。林貞純（1996），「利率預測模式建立之研究—時間數列模式（ARIMA）分析」，中國工商學報，第一十八期，頁267~300。陳徵勇（2003），「利率預測方法簡介」，華南金控月刊，第十一期，頁10~16。
葉怡成（1995），類神經網路—模式應用與實作，儒林出版社。
葉小蓁（1998），時間序列分析與應用，國立台灣大學。
蔡政良（2004），最適利率預測模型之建構----以泰勒法則、ARIMA為實證分析，長庚大學企業管理研究所碩士論文。鍾俊達（2003），運用類神經網路預測台灣貨幣市場利率—以30天期融資性商業本票為例，東吳大學企業管理研究所碩士論文。蘇木村、張孝德（2003），機器學習：類神經網路、模糊系統以及基因演算法，台北全華圖書公司。
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