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研究生:張光亮
研究生(外文):Kuang-Liang Chang
論文名稱:結構轉換模型於風險值、資產配置決策、與貨幣回饋法則之運用
論文名稱(外文):Value-at-Risk、Portfolio Allocation and Monetary Feedback Rule: Applications of the Multivariate Regime Switching Model
指導教授:鍾經樊鍾經樊引用關係
指導教授(外文):Ching-Fan Chung
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:經濟學研究所
學門:社會及行為科學學門
學類:經濟學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:中文
論文頁數:127
中文關鍵詞:均數-變異數方法資產配置績效極大化效用函數方法Autoregressive Logit Regime Switching (ALRS) 模型三變量三狀態馬可夫狀態轉換模型貨幣基數回饋法則GARCH 模型Regime Switching AR-ARCH (SWARCH) 模型風險值回饋係數時間落後
外文關鍵詞:time lagsSWARCHALRSutility maximizationmean-varianceportfolio allocation performancefeedback coefficientmonetary feedback ruleValue-at-RiskGARCHtrivariate three-state Markov regime-switching model
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本論文將狀態轉換模型 (regime-switching model) 運用到財務經濟學與總體經濟學,
其內容包含風險值 (Value at Risk) 之計算、
資產配置決策之制定、
與探討貨幣基數回饋法則。

第一章在於比較不同計量模型對於美法二國股票資產組合風險值 (Value at Risk, VaR) 的預測精確度,
比較的計量模型包含 GARCH 模型、 Hamilton and Susmel (1994) 的 Regime-Switching AR-ARCH (SWARCH) 模型、
與 Chung (2003) 的 Autoregressive Logit Regime-Switching (ALRS) 模型,
使用失敗率 (failure rate) 與 Kupiec (1995) 概似比檢定法 (likelihood ratio test) 來衡量預測精確度。
實證結果發現,
GARCH 模型的預測績效遠遠低於 SWRACH 與 ALRS 模型,
所以條件常態分配假設確實會忽略金融資產分配具有厚尾的現象,
造成 GAR-CH 模型估算之 VaR 無法反映真實的風險程度。
同屬於具有狀態轉換結構的 SWARCH 與 ALRS 模型,
將美國與法國股票市場結構轉換的特徵納入分析架構,
考量報酬率分配厚尾、 期望值和變異數隨狀態改變、 與波動叢聚現象 (SWACH 模型波動叢聚特性不明顯),
資產組合報酬率 VaR 之預測能力較 GARCH 模型佳。
在投資損失超過風險值的可能性等於百分之一的情況下,
SWARCH 模型的預測能力較 ALRS 模型好;
在投資損失超過風險值的可能性等於百分之二點五、百分之五的情況下,
ALRS 模型的預測精確度較 SWARCH 模型好。

在美國與法國股票報酬率呈現狀態轉換的二元常態分配之假設下,
第二章研究極大化 CA-RA 效用函數 (constant absolute risk aversion utility)
與 CRRA 效用函數 (constant relative risk aversion utility) 所決定的資產配置決策是否與均數---變異數方法相同,
並且探討不同狀態轉換模型的資產配置績效。
實證結果有二個發現:
第一,
在允許資產報酬率出現狀態轉換的情況下,
極大化 CARA 效用函數與均數---變異數方法所決定的資產配置決策非常接近;
但是極大化 CRRA 效用函數與均數---變異數方法所決定的資產配置決策差距頗大。
第二,
不論資產配置決策方法與風險趨避度為何,
使用 Chung (2003) 的 Autoregressive Logit Regime Switching (ALRS)
模型來執行資產配置決策可以獲得最大的累積報酬,
ALRS 模型的資產配置績效比 Regime Switching AR-ARCH (SWARCH)、 GARCH 模型還要好。

文獻上常見的回饋法則存在二個缺點:
第一,
假設線性的回饋法則,
線性的設定方式無法反應 Bernanke and Mishkin (1992) 的貨幣管理當局危機意識行為 ---
當某一貨幣政策目標有危機時,
貨幣管理當局會提高該目標的重要性。
第二,
目標值事前就已經決定,
事先決定的目標值無法將決策時間落後 (time lags) 的問題納入考量。
第三章的目的即是在檢驗美國貨幣管理當局是否對產出和物價具有危機意識,
實證模型是一個根據貨幣基數成長法則的三變量三狀態馬可夫狀態轉換模型,
允許貨幣基數成長率、
實質產出成長率、
與物價成長率受到相同隨機變數的影響,
以內生決定狀態及其轉換的時間點,
並且在設定目標時將未來經濟情況納入考量。
實證結果發現,
在 1974 年第 1 季到 1975 年第 3 季、
與 1979 年第 1 季到 1980 年第 3 季 (低產出與高通膨狀態) 的時期,
產出成長率大幅下跌 (實際成長率低於目標值),
產出缺口回饋係數的絕對值變大,
這個結果顯示在產出成長率下跌時,
貨幣管理當局對產出偏離目標所採取的因應措施較為強烈。
美國在上述二段期間出現通貨膨脹危機,
但是貨幣管理當局並未採取緊縮措施來抑制通貨膨脹,
此結論與 Clarida et al. (2000) 的看法相符---
在聯邦理事主席 Volcker 上任之前,
美國並未對通貨膨脹進行管制。
所以美國貨幣管理當局在上述期間對產出具有危機意識,
但是對物價並無明顯危機意識。
This dissertation analyses the applications of the multivariate regime switching model to
estimate the Value-at-Risk (VaR),
the portfolio allocation, and the monetary feedback rule。

In Chapter 1, we analyse the application of a switching volatility model
to forecast the distribution of returns and to estimate the VaR of a portfolio of sotcks.
The calculated VaR values are also compared with the generalized autoregressive
conditional heteroscedasticity (GARCH) model,
and the Regime Switching AR-ARCH (SWARCH) model (Hamilton and Susmel, 1994),
and the Autoregressive Logit Regime Switching (ALRS) model (Chung, 2003).
Using a portfolio composed of two stocks, the US stock and French stock,
we evaluate the forecasting performance of VaR obtained from the above approaches by
computing Kupiec''s (1995) likelihood ratio tests on the empirical failure rates.
The empirical results show that the VaR values calculated from the bivariate SWARCH and ALRS models
outperform the GARCH model.
We show that the GARCH model based on conditional normal distribution assumption is at odds
with reality and often results in misleading estimates of VaR.
At the 1 \% level,
the SWARCH model significantly outperforms the ALRS model.
In contrast,
the ALRS model outperforms the SWARCH model with 2.5\% and 5\% tail probabilities.

It is frequently asserted that mean-variance analysis applies exactly only when distributions
are normal or utility functions are quadratic,
suggesting that it gives almost optimum results only when distributions are apporximately normal
or utility functions look almost like a parabola.
On the other hand,
the mean-variance approximation (a second-order Taylor expansion of the utility function around the mean
of portfolio return) is equivalent to the utility maximization.
Regime-switching models have been successfully used to model many financial time series.
Based on regime switching models,
in Chapter 2 we examine whether investors with exponential and power utility functions
choose mean-variance efficient portfolio when returns are state-dependent bivariate normal distribution.
We illustrate the use of regime switching models (SWARCH, ALRS) in portfoilio choice problems and
compare the performances of regime switching models.
The results show that the portfolios of exponential utility investors plot very closely to the MV-efficient
frontier.
However, portfolio allocations are marked differences between
utility maximization and mean-variance approximation.
We also find that, compared to GARCH and SWARCH frameworks,
the portfolio choices resulting from ALRS model lead to higher investment performances.

Many monetary feedback rules are assumed to be linear,
i.e. the feedback coefficients of target variables are constants.
Linear rules have difficulty in capturing the discretionary actions taken by monetary authorities,
especially when monetary authorities encounter unexpected or drastic changes in economic situations.
Furthermore, the object targets do not include current information and ignore the problem of time lags.
The empirical model we propose is a trivariate three-state Markov
regime-switching model that is originated from McCallum''s (1987) monetary base growth rule.
The three macro variables we consider are the monetary base growth,
the real GDP growth, and the inflation rate.
It is assumed that these three variables are subject to the same
Markov regime-switching variable in determining their three states.
The feedback coefficients and the weighted coefficients are governed by a state variable.
Our empirical
results using US''s quarterly data suggest a low growth-high inflation state,
and a high growth-medium inflation state,
and a medium growth-low inflation state :
the former includes 1974Q1 -- 1975Q3,
and 1979Q1 -- 1980Q3.
The main finding of this chapter is that
the behavior of the Feds does exhibit some features of crisis mentality in real GDP growth,
but not in inflation rate.
1 資產組合風險值衡量:狀態轉換模型之運用 1
1.1 前言2
1.2 風險值之計算與相關發展4
1.2.1 風險值之計算4
1.2.2 分配假設7
1.3 實證方法說明8
1.3.1 狀態轉換模型9
1.3.2 資產配置決策13
1.3.3 狀態轉換下之 VaR 衡量14
1.4 實證結果15
1.4.1 各種狀態轉換模型之估計結果15
1.4.2 資產配置決策實證結果26
1.4.3 資產配置報酬率之風險值26
1.5 結論37
附錄 1.A39
附錄 1.B46
參考文獻49

2 資產配置決策與資產配置績效:狀態轉換模型之運用53
2.1 前言54
2.2 狀態轉換模型與資產配置57
2.2.1 狀態轉換模型57
2.2.2 資產配置決策61
2.3 實證結果66
2.3.1 各種狀態轉換模型之估計結果66
2.3.2 資產配置決策實證結果77
2.4 結論94
參考文獻96

3 非線性的貨幣回饋法則:三變量三狀態馬可夫狀態轉換模型之運用99
3.1 前言100
3.2 模型設立103
3.2.1 三變量三狀態馬可夫狀態轉換模型105
3.2.2 目標值之設定109
3.3 估計結果110
3.4 結論121
附錄124
參考文獻125
ch1:
高櫻芬 (2002), 涉險值之衡量---多變量 GARCH 模型之應用,
經濟論文叢刊, 30:3, 273-312。

黎明淵 (2000), 馬可夫轉換模型應用性與合用性探討,
國立政治大學國際貿易研究所博士論文。

Ang, Andrew and G. Bekaert (2000a), International Assest Allocation with Regime Shifts, The Review of Financial Studies, 15, 1137-1187.

Ang, Andrew and G. Bekaert (2000b), How do Regimes Affect Asset Allocation ?,working paper.

Alexander, C. O. and C. T. Leigh (1997), On the Covariance Matrices Used in Value at Risk Model,Journal of Derivatives, 4, 50-62.

Amilon, H. (2000), Comparison of Mean-Variance and Exact Utility Maximization in Stock Portfolio Selection, working paper.


Billio M. and L. Pelizzon (2000), Value-at-Risk : a Multivariate Switching Regime Approach,Journal of Emprical Finance, 7, 531-554.

Bollerslev, T. (1986), Generalized Autoregressive Conditional Heteroskedasticity,Journal of Econometrics,31, 307-327.

Bollerslev, T. (1990), Modelling the Coherence in Short-Run Nominal Exchange Rates:A Multivariate Generalized ARCH Model,Review of Economics and Statistics, 72, 498-505.

Bollerslev, T., R. Y. Chou and K. F. Kroner (1992), ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence,Journal of Econometrics, 52, 5-59.

Butler K. C. and D. C. Joaquin (2002), Are the Gains from International Portfolio Dviersification Exaggerated ? The Influence of Downside Risk in Bear Markets,Journal of International Money and Finance, 21, 981-1011.

Cai, J. (1994), A Markov Model of Switching-Regime ARCH,
Journal of Business and Economic Statistics, 12, 309-316.

Chung, C. F. (2003), The Autoregressive Logit Regime Switching Model and Its Allpication to the Analysis of Return Volatility and Trading Volume,working paper.

Clark, R. G. and H. D. Silva (1998) State-Dependent Assest Allocation, The Journal of Portfolio Management, 57-64.

Dueker, M.J. (1997), Markov Switching in Garch Processes and Mean-Reverting Stock-Market Volatility,Journal of Business and Economic Statistics, 15, 26-34.


Duffie D. and J. Pan (1997), An Overview of Value at Risk,
Journal of Derivatives, 7-49.

Edwards, S. and R. Susmel (2001), Volatility Dependence and Contagion in Emerging Equity Markets, Journal of Development Economics, 66, 505-532.

Engle, R. F. (1982), Autoregressiv Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation,Econometrica, 50, 987-1007.

Engel, C. and J. D. Hamilton (1990), Long Swings in the Dollar: Are They in the Data and Do Markets Know It ?
The American Economic Review, 80, 689-713.

Francq, C. and J. M. Zakoian (2001), Stationarity of Multivariate Markov-Switching ARMA Models,Journal of Econometrics, 102, 339-364.

French, K. R. and J. M. Poterba (1990), Japan and U.S. Cross-border Common Stock Investments,Journal of the Japanese and International Economics, 4, 476-493.

French, K. R. and J. M. Poterba (1991), Investor Diversification and International Equity Markets,
The American Economic Review, 81, 222-226

Giot, P. and S. Laurent (2002), Value-at-Risk for Long and Short Trading Positions, Journal of Applied Econometrics. (in press)

Gray, S. F. (1996), Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process,Journal of Financial Economics, 42, 27-62.

Goldfeld, S. M. and Quandt, R. E. (1973), A Markov Model for Switching Regressions,Journal of Econometrics, 1, 3-16.

Grubel, H. G. (1968), Internationally Dviersified Portfolios: Welfare Gains and Capital Flows,The American Economic Review,1299-1314.

Hamilton, J. D. (1988), Rational Expectations Econometric
Analysis of Change in Rregime: An Investigation of The Term Structure of Interest Rates,Journal of Economic Dynamics and Control, 12, 385-423.

Hamilton, J. D. (1989), A New Approach to the Economic
Analysis of Non-stationary Time Series and the Business Cycle,Econometrica, 57, 357-384.

Hamilton, J. D. (1990), Analysis of Time Series Subject to Change in Regime,Journal of Econometrics, 45, 39--70.

Hamilton, J. D. and G. Lin (1996), Stock Market Volatility and the Business Cycle,Journal of Applied Econometrics, 11, 573-593.

Hamilton, J. D. and R. Susmel (1994), Autoregressiv Conditional Heteroskedasticity and Changes in Regime,
Journal of Econometrics, 64, 307-333.

Hendrick, D. (1995), Evaluation of Value at Risk Models using Historical Data,FRBNY Economic Policy Review, 2, 39-69.

Hull, J. and A. White (1998), Value at Risk When Daily Changes in Market Variables Are Not Normally Distributed,
Journal of Derivatives, 5, 9-19.

Jackson, P., D. J. Maude and W. Perraudin (1997), Bank Capital and Value at Risk, The Journal of Derivatives, 73-89.

Jorion, P. (1997), The New Benchmark for Controlling Market Risk,Irwin.


Klaassen, F. (2002), Improving Garch Volatility Forecasts with Regime-Switching GARCH, Empirical Economics, 27, 363-394.

Kroll, Y., H. Levy and H. M. Markowitz (1984), Mean-Variance Versus Direct Utility Maximization,The Journal of Finance, 39, 47-61.

Kupiec, P. H. (1995), Techniques for Verifying the Accuracy of Risk Measurement Models, Journal of Derivatives, 37-84.

Levy, H. and M. Sarnat (1970), International Diversification of Investment Portfolios, The American Economic Review, 668-675.

Lamoureux, C. G. and W. D. Lastrapes (1990), Persistence in Variance, Structural Change and the Garch Model,
Journal of Business and Economic Statistics, 8, 225-234.

Longin, F. M. (2001), Beyond the VaR, Journal of Derivatives, 37-48.

Maheu. J. M. and T. H. McCurdy (2000), Identifying Bull and Bear Markets in Stock Returns, Journal of Business and Economic Statistics, 18, 100-112.


Pulley, L. B. (1981), A Gernal Mean-Variance Approximation to Expected Utility for Short Holding Periods,
The Journal of Financial and Quantitative Analysis, 16, 361-373.

Ramchand, L. and R. Susmel (1998), Volatility and cross Correlation cross Major Stock Markets, Journal of Empirical Finance, 5, 397-416.

Schaller, H. and S. V. Norden (1997), Regime Switching in Stock Market Returns,Applied Financial Economics, 7, 177-191.


Sola, M. , F. Spagnolo and N. Spagnolo (2002), A Test for Volatility Spillover,Economics Letters, 76, 77-84.

Solnik, B. H. (1974), Why Not Diversify Internationally Rather Than Domestically ?,Financial Analysts Journal, 48-54.


Turner, C. M. , R. Startz and C. R. Nelson (1989), A Markov Model of Heteroskedasticity, Risk,and Learning in the Stock Market,Journal of Financial Economics, 25, 3-22.

Uysal, E. , F. H. Trainer and J. Reiss (2001), Revisiting Mean-Variance Optimization, The Journal of Portfolio Management, 71-81.


Venkataraman, S. (1997), Value at Risk for a Mixture of Normal Distributions:The Use of Quasi-Bayesian Estimation Techniques, Federal Reserved Bank of Chicago Economics Perspectives, 21, 2-13.

ch2:

Ang, A. and G. Bekaert (2002a), International Assest Allocation With Regime Sfits, The Review of Financial Studies, 15, 1137-1187.

Ang, A. and G. Bekaert (2002b), How do Regimes Affect Asset Allocation ?,working paper.

Amilon, H. (2000), Comparison of Mean-Variance and Exact Utility Maximizationi n Stock Portfolio Selection, working paper.

Barberis, N. (2000), Investing for the Long Run when Returns are Predictable, Journal of Finance, 55, 225-265.

Best, M. J. and R. R. Gauer (1991), On the Sensitivity of
Mean-Variance-Efficient Portfolios to Changes in Assest Means:Some Analytical and Computational Results,The Review of Financial Studies, 4, 315-342.

Black, F. and R. Litterman (1992), Global Portfoloio Optimization, Financial Analysts Journal, 28-43.

Bollerslev, T. (1990), Modelling the Coherence in Short-Run Nominal Exchange Rates:A Multivariate Generalized ARCH Model, Review of Economics and Statistics, 72, 498-505.


Cai, J. (1994), A Markov Model of Switching-Regime ARCH,
Journal of Business and Economic Statistics, 12, 309-316.

Campbell, J. Y. and L. M. Viceira (2002),Strategic Asset Allocation, Oxford University Press.

Chung, C. F. (2003), The Autoregressive Logit Regime Switching Model and Its Allpication to the Analysis of Return Volatility and Trading Volume,working paper.

Clark, R. G. and H. D. Silva (1998), State-Dependent Assest Allocation,The Journal of Portfolio Management, 57-64.

Dueker, M. J. (1997), Markov Switching in Garch Processes and Mean-Reverting Stock-Market Volatility,Journal of Business and Economic Statistics, 15, 26-34.


Edwards, S. and R. Susmel (2001), Volatility Dependence and Contagion in Emerging Equity Markets,Journal of Development Economics, 66, 505-532.

Engel, C. and J. D. Hamilton (1990), Long Swings in the Dollar: Are They in the Data and Do Markets Know It ?
The American Economic Review, 80, 689-713.

French, K. R. and J. M. Poterba (1991), Investor Diversification and International Equity Markets,
American Economic Review, 81, 222-226.


Goldfeld, S. M. and Quandt, R. E. (1973), A Markov Model for Switching Regressions,Journal of Econometrics, 1, 3-16.


Grauer, R. R. (1986), Normality, Solverncy, and Portfolio Choice, The Journal of Financial and Quantitative Analysis, 21, 265-278.

Gray, S. F. (1996), Modeling the Conditional Distribution of Interest Rates as a Regime-Switching Process,
Journal of Financial Economics, 42, 27-62.


Grubel, H. G. (1968), Internationally Dviersified Portfolios: Welfare Gains and Capital Flows, The American Economic Review, 1299-1314.

Hamilton, J. D. (1988), Rational Expectations Econometric
Analysis of Change in Rregime: An Investigation of The Term Structure of Interest Rates,Journal of Economic Dynamics and Control, 12, 385-423.

Hamilton, J. D. (1989), A New Approach to the Economic
Analysis of Non-stationary Time Series and the Business Cycle, Econometrica, 57, 357-384.

Hamilton, J. D. (1990), Analysis of Time Series Subject to Change in Regime, Journal of Econometrics, 45, 39-70.

Hamilton, J. D. and G. Lin (1996), Stock Market Volatility and the Business Cycle,Journal of Applied Econometrics, 11, 573-593.

Hamilton, J. D. and R. Susmel (1994), Autoregressiv Conditional Heteroskedasticity and Changes in Regime,
Journal of Econometrics, 64, 307-333.


Klaassen, F. (2002), Improving Garch Volatility Forecasts with Regime-Switching GARCH, Empirical Economics, 27, 363-394.

Kroll, Y., H. Levy and H. M. Markowitz (1984), Mean-Variance Versus Direct Utility Maximization,The Journal of Finance, 39, 47-61.

Lamoureux, C. G. and W. D. Lastrapes (1990), Persistence in Variance, Structural Change and the Garch Model,
Journal of Business and Economic Statistics, 8, 225-234.

Maheu. J. M. and T. H. McCurdy (2000), Identifying Bull and Bear Markets in Stock Returns, Journal of Business and Economic Statistics, 18, 100-112.

Markowitz, H. (1952), Portfolio Selection, Journal of Finance.

Michaud, R. O. (1989), The Markowitz Optimization Enigma: Is Optimaized Optimal ?, Financial Analysts Journal.

Pulley, L. B. (1981), A Gernal Mean-Variance Approximation to Expected Utility for Short Holding Periods,
The Journal of Financial and Quantitative Analysis, 16, 361-373.

Ramchand, L. and R. Susmel (1998), Volatility and Cross Correlation aAross Major Stock Markets,Journal of Empircial Finance, 5, 397-416.

Schaller, H. and S. V. Norden (1997), Regime Switching in Stock Market Returns,Applied Financial Economics, 7, 177-191.

Schwebach, R. G. and J. P. Olienyk and J. K. Zumwalt (2002),The Impact of Financial Crises on International Diversification,Global Finance Journal, 13, 147-161.

Schwert, G. W. (1989), Business Cycles, Financial Crises, and Stock Volatility,Carnegie-Rochester Conference Series on Public Policy, 31, 83-126.

Sola, M. , F. Spagnolo and N. Spagnolo (2002), A Test for Volatility Spillover,Economics Letters, 76, 77-84.

Turner, C. M. , R. Startz and C. R. Nelson (1989), A Markov Model of Heteroskedasticity, Risk,
and Learning in the Stock Market,Journal of Financial Economics, 25, 3-22.


Uysal, E. , F. H. Trainer and J. Reiss (2001), Revisiting Mean-Variance Optimization, The Journal of Portfolio Management, 71-81.

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Barro, R. J. and D. Gordon (1983), A Positive Theory of Monetary Policy in a Natural Rate Model, Journal of Political Economy, 91, 589-610.

Bernanke, B. S. and F. S. Mishkin (1992), Central Bank Behavior and the Strategy of Monetary Policy :Observations from Six Industrialized Countries,in O. Blanchard and S. Fischer (eds.), NBER Macroeconomics Annual. Cambridege, MA: MIT Press.

Blanchard, O. J. and S. Fischer (1989), Lectures on Macroeconomics.Cambridge, MA: MIT Press.

Brunner, A. D. (1994), The Federal Funds Rate and the Implementation of Monetary Policy: Estimating the
Federal Reserve''s Reaction Function,International Finance Discussion Paper no. 466,Board of Governors of the Federal Reserve System.

Clarida, R., J. Gali and M. Gertler (1998), Monetary Policy Rules in Practice: Some Internatioanl Evidence,
European Economic Review, 42, 1033-1067.

Clarida, R., J. Gali and M. Gertler (2000), Monetary Policy Rules and Macroeconomic Stability: Evidence and some Theory,Quarterly Journal of Economics, 115, 147-180.

Dennis, R. (2003), The Policy Preference of the US Federal Reserve,Federal Reserve Bank of San Francisco, mineo.

Dueker, M. and A. M. Fischer (1996), Inflation targeting in a small open economy :Empirical results for Switzerland,
Journal of Monetary Economics, 37, 89-103.

Dueker, M. and G. Kim (1999), A Monetary Policy Feedback Rule in Korea''s Fast-Growth Economy, Journal of International Financial Markets, Institutions and Money, 9, 19-31.

Favero, C. and R. Rovelli (2003), Macroeconomic Stability and the Preferences of the Fed. A Formal Analysis 1961-98,
Journal of Money Credit and Banking, forthcoming.

Friedman, M. (1960), A Program for Monetary Stability,
New York: Fordham University Press.

Garcia, R. and Perron, P. (1996), An Analysis of the Real Interest Rate under Regime Shifts, The Review of Economics and Statistics, 78, 111-125.

Hakes, D. and E. N. Gamber (1992), Does the Federal Reserve Respond to Errant Money Growth ?Evidence from Three Monetary Regime : Note,Journal of Money, Credit and Banking, 24, 127-134.

Hall, T. E. (1990), McCallum''s Base Growth Rule: Results for the United States,West Germany, Japan, and Canada,
Weltwirtschaftliches Archiv, 126, 630-642.

Hamilton, J. D. (1988), Rational Expectations Econometric Analysis of Change in Rregime: An Investigation of The Term Structure of Interest Rates, Journal
of Economic Dynamics and Control, 12, 385--423.

Hamilton, J. D. (1989), A New Approach to the Economic Analysis of Non-stationary Time Series and the Business Cycle, Econometrica, 57, 357--384.

Hamilton, J. D. (1990), Analysis of Time Series Subject to Change in Regime,Journal of Econometrics, 45, 39--70.


Huchet, M. (2003), Does Single Monetary Policy Have Asymmetric Real Effects in EMU ?,Journal of Policy Modeling, 25, 151-178.

Judd, J. P. and B. Motley (1991), Nominal Feedback Rules for Monetary Policy,Federal Reserve Bank of San Francisco, Economic Review, 3-17.

Karras, G. (1996), Why Are tahe Effects of Money-Supply Shocks Asymmetric ?Convex Aggregate Supply or Pushing on a String ? , Journal of Macroeconomics, 18, 605-619.

Kim, C. J. (1993), Sources of Money growth Uncertainty and Economic Activity:The Time-Varying Parameter with Heteroscedastic Disturbances, Review of Economics and Statistics, 483-492

Kydland, F. and E. Prescott (1977), Rules Rahter Than Discretion:The Inconsistency of Optimal Plans,Journal of Political Economy, 85, 473-492.

McCallum, B. T. (1987), The Case for Rules in the Conduct of Monetary Policy : A Concrete Example, Economic Review, 10-18.

McCallum, B. T. (1988), Robustness Properties of a Rule for Monetary Policy, Carnegie-Rochester Conference Series on Public Policy, 29, 173-203.

McCallum, B. T. (1990), Could a Monetary Base Rule Have Prevented the Great Depression ? , Journal of Monetary Economics, 26, 3-26.

McCallum, B. T. (1993), Specification and Analysis of a Monetary Policy Rule for Japan, Bank of Japan Monetary and Economic Studies, 11, 1-45.


McCallum, B. T. (2002), The Use of Policy Rules in Monetary Policy Analysis,Shadow Open Market Committee.

McCarthy, J. (1995), VARs and the Identification of Monetary Policy Shocks: A Critical Analysis of Linearity Assumptions,Federal Reserve Bank of New York, manuscript.

Orphanides, A. (2001), Monetary Policy Rules, Macroeconomic Stability and Inflation: A View from the Trenches,Board of Governors of the Federal Reserve System, mimeo.

Pagan, A. (1984), Econometric Issues in the Analysis of Regressions with Generated Regressors,International Economic Review, 25, 221-247.

Phillips, A. W. (1957), Stabilization Policy and the Time-Form of Lagged Responeses, Economic Journal, 67, 265-277.


Rhee, W. and R. W. Rich (1995), Inflation and the Asymmetric Effects of Money on Output Fluctuations,
Journal of Macroeconomics, 17, 683-702.

Rogoff, K. (1985), The Optimal Degree of Commitment to an Intermediate Monetary Target, Quarterly Journal of Economics, 100, 169-190.

Rudebusch, G. G. (1998), Do Measures of Monetary Policy in a VAR Make Sense ?, International Economic Review, 39, 907-931.

Shen, C. H. and D. R. Hakes (1995), Monetary Policy aas a Decision-Making Hierarchy:The Case of Taiwan,Journal of Macroeconomics, 17, 357-368.

Shen, C. H. and S. C. Hsu (2000), Discretionary Monetary Feedback Rule : The Taiwan Case,Academia Economic Papers, 4, 339-364.

Stark, T. and D. Croushore (1998), Evaluating McCallum''s Rule when Monetary Policy Matters, Journal of Macroeconomics, 20, 451-485.

Stock, J. H. and M. H. Watsom, Evidence on Structural Instability in Macroeconomic Time Series Relations,
Journal of Business and Economic Statistics, 14, 1411-1430.

Taylor, J. B. (1993), Discretion versus Policy Rule in Practice, Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
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