能源政策與產業發展對經濟成長之影響

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Title:	能源政策與產業發展對經濟成長之影響
Other Titles:	The effects of energy policy and industry development on economic growth
Authors:	汪雲龍;Wang, Yun-lung
Contributors:	淡江大學財務金融學系碩士在職專班邱建良;黃健銘;Chiu, Chien-Liang;Huang, Chien-Ming
Keywords:	能源政策;產業發展;經濟成長;馬可夫鍊轉換模型;Energy Policy;Industry Development;economic growth;Markov- Switching Models
Date:	2016
Issue Date:	2017-08-24 23:34:15 (UTC+8)
Abstract:	本研究主要探討能源政策和產業發展對於經濟成長的影響。實證模型採用混合分配模型(Mixture Distribution Models)和馬可夫轉換模型(Markov-Switching Models)，並透過文獻探討擷取總共7個總體經濟指標—經濟成長率、物價成長率、貨幣市場狀態、進出口貿易收支、能源生產價值、電子產業發展和政府總合支出放入模型中加以分析討論。本研究的樣本資料選自台灣經濟新報(Taiwan Economic Journal, TEJ)2001年第一季至2015年第四季的資料，共計415筆。研究結果發現，能源生產價值與經濟成長為負相關，而電子產業發展指數則與台灣的經濟成長為正相關。在其他變數方面，物價和政府支出，都與台灣的經濟成長呈現負相關。最後，貨幣市場狀態和進出口貿易收支都與台灣的經濟成長呈現正相關。 Hamilton(1989)首先發展出馬可夫轉換模型，由於該模型允許母體參數在不同時期，具有間斷性跳動性質，且跳動次數並不限定為一，並利用馬可夫鏈(Markov chain)的機制來控制狀態間的切換，解決混合分配模型狀態跳動毫無規則的問題，以掌控金融與經濟變數所面臨的結構性改變。馬可夫轉換模型與混合分配最大的差異，是利用馬可夫鏈的機制來掌控狀態間的切換，以解決混合配模型狀態跳動毫無規則的問題。故本論文除採用混合分配模型外，再引入馬可夫轉換模型進行經濟成長狀態模型估計。 This study aim is to examaint the effects of enegy policy and industry development on economic growth. Our empirical model use Mixture Distribution Models and Markov-Switching Models and according to the literature review, we gather the 7 macroeconomic factors which are economic growth rate, inflation, money market, balance of payment, energy price, electronics industry, total government expenditure into the model for analysis. Moreover, all kind of index we use are gatherd from Taiwan Economic Jurrnal (TEJ) over peried from 2001 Q1 to 2015 Q4 included 415 quarterly data. The results find that there is a statisticaly negative relationship between energy price and economics growth while the electronics industry is positive relationship. Otherwise, inflation and government expenditure are negative relationship with economic growth. Finally, money market and balance of payment are positive relationship with economic growth. Hamilton(1989) first developed to run for Markov-Switching models, because it allows the parent parameters at different times, with the intermittent nature of the beating, and beat a number of times is not limited to, use of the mechanisms and Markov chains to control the state switch between, to solve Mixture Distribution Models beating no rules issues in order to control the structural changes in the financial and economic variables are facing. Markov-Switching models with mixed allocation biggest difference is the use of Markov chain mechanism to control the switching between states in order to solve the mixed model state with no rules to the beating. Therefore, this paper except a Mixture Distribution Models, the reintroduction Markov-Switching Models of economic growth model estimation.
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