本文採用動態要素需求模型的架構，並且考量了景氣波動之下狀態的不同，以分析其對產業動態的影響。本文的實證分析對象為2003-2006 年的台灣10 個高科技產業，應用馬可夫轉換模型及動態要素需求模型分析。估計時馬可夫轉換模型採用數值方法來處理最大概似法的問題，在動態要素需求模型採用一般動差估計法作為計量分析基礎，實證結果顯示以下兩點: (1) 考慮了景氣的波動，利用資本和勞動的擴張策略，不一定對產出有正面的影響;反之正確的對產業循環波動的預測是很重要的。(2) 技術對產出成長貢獻度效果皆為正向，這也說明了近年來各高科技產業為何致力於技術升級。本文提出如何建構一個考慮到景氣波動和不確定性的產業動態模型，並瞭解基本面因素的變化如何影響投資行為、技術和產業面對需求變化的調整過程。

In markets with cyclical fluctuations, firms may have different dynamic decision rules facing upturns and downturns of industry cycles. This paper extends the dynamic factor demand model to consider industry cycles. Because investment behavior could be endogenous uncertainty involved on industry dynamics, the current industry dynamic models with state-of-the-art would not appropriately interpret industry dynamics. In order to solve the uncertain problem, we utilize the idea of transfer probability in Markov switching model to catch the industry cyclical behavior. Explicitly incorporating the Markov regime switching mechanism based on Nelson and Kim (2000), this paper measures the firm’s dynamic adjustments when facing upturns and downturns of industry cycles. The empirical work is based on firm level data of Taiwan high-technology industries. The empirical results show that the expansionary strategy in labor and capital usage may not have positive impacts on output when considering uncertainty that may be casued by business cycles. To have correct prediction in cyclical fluctuation becomes important task for high-technology firms. However, the positive contribution of exogenous technology to output growth is so significant. This proves why every industry tries to impel technology in recent years. The industry dynamic model integrated with cyclical fluctuation and demand uncertainty allows us to examine how sharp changes in financial factors might affect investment behavior, technological nature and adjustment effects for industries in facing demand and investment shocks.

I. Introduction.........................................................................................................................3
II. Theoretical Models
2.1 Cyclical Fluctuations.........................................................................................................7
2.2 Endogenized Uncertainty and Adjustments.....................................................................10
2.3 Industry Dynamics............................................................................................................14
III. Empirical Findings
3.1 Data Source and Variable Specification..........................................................................19
3.2 Cyclical fluctuations in Markov switching model..............................................................21
IV. Conclusion ......................................................................................................................28

REFERENCES......................................................................................................................30
APPENDIX.............................................................................................................................34

Andreas, H. (2000), “The Business Cycle and Industry Comovement,” Federal Reserve Bank of Richmond Economic Quarterly Volume 86/1.
Berman, J. and J. Pfleeger. (1997), “Which Industries are Sensitive to Business Cycles?” Economists, Office of Employment Projections, Bureau of Labor Statistics, Vol.120, No. 2.
Berndt, R., C.J. Morrison and G.C. Watkins (1981), “Dynamic Models of Energy Demand: An Assessment and Comparison,” in E.R. Berndt and B.C. Fields (eds.), Modeling and Measuring Natural Resource Substitution, Cambridge, MA: MIT Press, pp. 259-289.
Burns, A. and W. Mitchell. (1946), “Measuring Business Cycles,” NBER, New York.
Chen, Shyh-Wei. (2003), “Is Taiwan's 10th Business Cycle Over Yet? A simple note, Taiwan,” Economic Forecast and Policy, 33, 39-60.
Chow, G. (1960), “Tests of the Equality between Two Sets of Coefficients in Two Linear Regressions,” Econometrica 28, 561–605.
Chung, Ching-Fan, Vei-Lin Chan and Kuang-Liang Chang (2004),“Wealth Effects on Consumption in Taiwan: An Application of the Multivariate Markov Regime-Switching Model,”IEAS Working Paper No. 04-A007.
Denny M., A.F. Melvyn and L. Waverman (1981), “Substitution Possibilities for Energy: Evidence from U.S. and Canadian Manufacturing Industries,” in E.R. Berndt and B.C. Fields (eds.), Modeling and Measuring National Resource Substitution, Cambridge, MA: MIT Press, pp. 230-258.
Eiichiro K. (2003), “A Markov Perfect Industry Dynamics with Dynamic Demands,” California Institute of Technology.
Ericson, R. and A. Pakes. (1995), “Markov-Perfect Industry Dynamics: A Framework for Empirical work,” Review of Economic Studies 62(1), 53 – 82.
Farley, J.U. and M.J. Hinich. (1970), “A Test for a Shifting Slope Coefficient in a Linear Model,” Journal of the American Statistical Association, 65, 1320-1329.
Goldfeld, S.M. and R.E. Quandt. (1973), “A Markov Model Switching Regressions,” Journal of Economics 1 3-16.
Hamilton, J.D. (1989), “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica 57, 357–384.
Hansen, L.P. (1982), “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, 50, 1029-1054.
Lee Hsiu-yun and Wen-te Lee (2006), “Intrinsic Bubbles and Regime-Switching Dividends: An Investigation into the Long-run Process of Taiwan Stock Prices,” IEAS, 18-3, 443-471. (in Chinese)
Kim, J.H. (2004), “Bootstrap Prediction Intervals for Autoregression using Asymptotically Mean-Unbiased Estimators,” International Journal of Forecasting, Elsevier, vol. 20(1), pp85-97.
Kim, C.J. and C.R. Nelson. (1999), “State-Space Models with Regime-Switching: Classical and Gibbs-Sampling Approaches with Applications,” MIT Press.
Kim, I.M. and G..S. Maddala. (1991), “Multiple Structural Breaks and Unit Roots in Exchange Rates,” Paper presented at the Econometric Society Meeting at New Orleans, Dec. 1991.
Kim, H.J. and D. Siegmund (1989), ‘‘The Likelihood Ratio Test for a Change-Point in Simple Linear Regression,” Biometrika 76, 409–423.
Liu, Wen-Hsien and Yih-Luan Chyi (2006), “A Markov Regime-Switching Model for the Semiconductor Industry Cycles,” Economic Modelling, Vol. 23, pp. 569-578, 2006.
Liu, Wen-Hsien (2005), “Determinants of the Semiconductor Industry Cycles,” Journal of Policy Modeling, Vol. 27, pp. 853-866, 2005.
Monck, C.S.P., R.B. Porter, P. Quintas and P. Wynarczyk. (1998), “Science Parks and the Growth of High Technology Firms,” London: Croom Helm.
Neftci, S.N. (1984), “Are Economic Time Series Asymmetric over the Business Cycles?” Journal of Political Economy, vol.92, no.2.
Pakes, A. and P. McGuire. (1994), “ComputingMarkov-Perfect Nash Equilibria: Numerical Implications of a Dynamic Dierentiated Product Model,” Rand Journal of Economics 25(4), 555-89.
Pakes, A. and P. McGuire. (2001), “Stochastic Algorithms, Symmetric Markov Perfect Equilibrium, and the Curse of Dimensionality,” Econometrica 69(5), 1261 – 1281.
Pfann, G. (1994), “Factor Demand with Nonlinear Shortrun Fluctuations,” Journal of Economic Dynamics and Control 20, p315-332. 1996.
Ploberger, W., W. Kramer and W. Kontrus (1989), ‘‘A New Test for Structure Stability in the Linear Regression Model,” Journal of Econometrics 40, 307–318.
Quandt, R.E. (1972), “A New Approach to Estimating Switching Regressions,” Journal of the American Statistical Association, 67, 306–310.
Quandt, R.E. (1960), “Tests of the Hypothesis that a Linear Regression System Obeys Two Separate Regimes,” Journal of the American Statistical Association 55, 324–330.
Quandt, R.E. (1958), “The Estimation of the Parameters of a Linear Regression System Obeying Two Separate Regimes,” Journal of the American Statistical Association 53, 873–880.
Romer, P.M. (1990), “Endogenous Technological Change,” Journal of Political Economy, 98, S71–S102.
Hsu Shih-Hsun and Chung-Ming Kuan (2001), “Identifying Taiwan's Business Cycles in 90's: An Application of the Bivariate Markov Switching Model and Gibbs Sampling,” IEAS, 13:5, 515-540. (in Chinese)
Chen Shyh-Wei and Chung-Hua Shen (2003), “An Investigation on Duration Dependence: Evidence from Taiwan’s Business Cycles,” IEAS, 34, 63-92. (in Chinese)
Solow, R. (1957), “Technical Change and the Aggregate Production Function,” Review of Economics and Statistics, vol. 39(3), pp.312-330.
Tsai, Diana H. A. and Marc Lin (2005), “Industrial and Spatial Spillovers and Productivity Growth: Evidence from Taiwan High-Technology Plant Level Data,” Journal of Productivity Analysis, 23, 109–129.
Tsai, Diana H. A. and Tze-Gan Chen (2002),“Dynamic Adjustments of the Intra- and Inter-Industry R & D Spillovers: Evidence from Taiwanese Electronics Plant Level Data,”Journal of Social Sciences and Philosophy 14:(3), 289–327. (in Chinese)