單根，共積，格蘭傑爾因果，門檻迴歸及其他計量經濟模式
Unit Root- Cointegration- Granger-Causality- Threshold Regression and Other Econometric Modeling wit
配送方式
-
台灣
- 國內宅配：本島、離島
-
到店取貨：
不限金額免運費
-
海外
- 國際快遞：全球
-
港澳店取：
內容簡介
本書包括了三十篇以計量經濟學為主的學術論文，作者以黃柏農及楊慶偉教授為主。這些論文大部分都是應用在總體經濟學，金融市場及能源市場的經濟模式。這三十篇論文大致上可以分成四類：
(1)格蘭傑爾因果模式，
(2)格蘭傑爾追蹤資料的因果模式，
(3)格蘭傑爾門檻廻歸，
(4)間接因果，隨機漫步，長期記憶，股市波動模式。
本書可以做為經濟或金融研究所的教課本，也可在產業界把它當做參考書，但是重點放在應用方面。黃柏農教授在此要特別感謝他的論文指導恩師俄克拉何馬大學的C.K.Liew教授。同時他更要感謝加州大學聖地牙哥
分校的格蘭傑爾教授(C.W.J.Granger，2003年諾貝爾獎得主）的鼓勵及指導。也在此一併致謝黃明正教授對他在西維琴尼亞大學當訪問學者時的幫助與鼓勵。楊慶偉教授在此要感謝西維琴大學教計量經濟學的維特( T. Witt) 教授；同時感謝當時國立中正大學管理學院的洪家駿院長的賞識。
本書最大的特色是以格蘭傑爾因果檢定做為模式的圭臬，豪無疑問地它是現代科學中最具有突破性的驗証模式。
(1)格蘭傑爾因果模式，
(2)格蘭傑爾追蹤資料的因果模式，
(3)格蘭傑爾門檻廻歸，
(4)間接因果，隨機漫步，長期記憶，股市波動模式。
本書可以做為經濟或金融研究所的教課本，也可在產業界把它當做參考書，但是重點放在應用方面。黃柏農教授在此要特別感謝他的論文指導恩師俄克拉何馬大學的C.K.Liew教授。同時他更要感謝加州大學聖地牙哥
分校的格蘭傑爾教授(C.W.J.Granger，2003年諾貝爾獎得主）的鼓勵及指導。也在此一併致謝黃明正教授對他在西維琴尼亞大學當訪問學者時的幫助與鼓勵。楊慶偉教授在此要感謝西維琴大學教計量經濟學的維特( T. Witt) 教授；同時感謝當時國立中正大學管理學院的洪家駿院長的賞識。
本書最大的特色是以格蘭傑爾因果檢定做為模式的圭臬，豪無疑問地它是現代科學中最具有突破性的驗証模式。
名人推薦
前美國聯邦政府NASA天文物理科學家丘宏義博士推薦
目錄
Preface
I. The Granger Causality Models in Mean and Variance
1. A Bivariate Causality Between Stock Prices and Exchange
Rates: Evidence from Recent Asian Flu
C.W.J. Granger, Bwo-Nung Huang and Chin-Wei Yang
2. An Analysis of Factors Affecting Price Volatility of the US Oil
Market
Chin-Wei Yang, Ming J. Hwang and Bwo-Nung Huang
3. Causality and Cointegration of Stock Markets among the US,
Japan and South China Growth Triangle
Bwo-Nung Huang, Chin-Wei Yang and J.W. Hu
4. Long-run Purchasing Power Parity Revisited : A Monte Carlo
Simulation
Bwo-Nung Huang and Chin-Wei Yang
5. Oil Price Movements and Stock Market Revisited: A Case of
Sector Stock Price Indexes in the G-7 Countries
B.J.Lee , Chin-Wei Yang and Bwo-Nung Huang
6. Volatility of Changes in G-5 Exchange Rates and Its Market
Transmission Mechanism
Bwo-Nung Huang and Chin-Wei Yang
7. Stock Market Integration —An Application of the Stochastic
Permanent Breaks Model
Bwo-Nung Huang and Robert C.W. Fok
8. State Dependent Correlation and Lead-Lag Relation when Volatility of Markets is Large: Evidence from the US and Asian Emerging Markets
Bwo-Nung Huang, Soong-Nark Sohng and Chin-Wei Yang
9. Oil Price Volatility
Ming J. Hwang, Chin-Wei Yang , Bwo-Nung Huang and H. Ohta
II. Granger Causality Models Using Panel Data
1. Causal Relationship between Energy Consumption and GDP
Growth Revisited: A Dynamic Panel Data Approach
Bwo-Nung Huang, Ming J. Hwang and Chin-Wei Yang
2. Military Expenditure and Economic Growth across Different
Groups: A Dynamic Panel Granger-Causality Approach
H. C. Chang, Bwo-Nung Huang and Chin-Wei Yang
3. New Evidence on Demand for Cigarette: A Panel Data Approach
Bwo-Nung Huang, Chin-Wei Yang and Ming J. Hwang
III. Granger Causality Models with Thresholds
1. Demand for Cigarette Revisited: An Application of the Threshold
Regression Model
I. The Granger Causality Models in Mean and Variance
1. A Bivariate Causality Between Stock Prices and Exchange
Rates: Evidence from Recent Asian Flu
C.W.J. Granger, Bwo-Nung Huang and Chin-Wei Yang
2. An Analysis of Factors Affecting Price Volatility of the US Oil
Market
Chin-Wei Yang, Ming J. Hwang and Bwo-Nung Huang
3. Causality and Cointegration of Stock Markets among the US,
Japan and South China Growth Triangle
Bwo-Nung Huang, Chin-Wei Yang and J.W. Hu
4. Long-run Purchasing Power Parity Revisited : A Monte Carlo
Simulation
Bwo-Nung Huang and Chin-Wei Yang
5. Oil Price Movements and Stock Market Revisited: A Case of
Sector Stock Price Indexes in the G-7 Countries
B.J.Lee , Chin-Wei Yang and Bwo-Nung Huang
6. Volatility of Changes in G-5 Exchange Rates and Its Market
Transmission Mechanism
Bwo-Nung Huang and Chin-Wei Yang
7. Stock Market Integration —An Application of the Stochastic
Permanent Breaks Model
Bwo-Nung Huang and Robert C.W. Fok
8. State Dependent Correlation and Lead-Lag Relation when Volatility of Markets is Large: Evidence from the US and Asian Emerging Markets
Bwo-Nung Huang, Soong-Nark Sohng and Chin-Wei Yang
9. Oil Price Volatility
Ming J. Hwang, Chin-Wei Yang , Bwo-Nung Huang and H. Ohta
II. Granger Causality Models Using Panel Data
1. Causal Relationship between Energy Consumption and GDP
Growth Revisited: A Dynamic Panel Data Approach
Bwo-Nung Huang, Ming J. Hwang and Chin-Wei Yang
2. Military Expenditure and Economic Growth across Different
Groups: A Dynamic Panel Granger-Causality Approach
H. C. Chang, Bwo-Nung Huang and Chin-Wei Yang
3. New Evidence on Demand for Cigarette: A Panel Data Approach
Bwo-Nung Huang, Chin-Wei Yang and Ming J. Hwang
III. Granger Causality Models with Thresholds
1. Demand for Cigarette Revisited: An Application of the Threshold
Regression Model
序／導讀
Preface
Both deductive and inductive methods in scientific research have undergone significant changes since the beginning of the 20th century as sciences advance rapidly. Deductive method reached its pinnacle when Russel’s paradox became popular in the field of mathematical logics. The famous barber’s paradox illustrates the inevitable logical dilemma: the only barber in an isolated village does the following: (1) he cuts hair for those who do not cut their own and (2) does not cut hair for those who cut their own. Suppose 90 members in the village do not cut their hair (so barber cuts their hair) and 9 cut their own hair (thus barber does not cut their hair). The question is who cuts the barber’s hair? The intrinsic contradiction arrives in either way. If barber cuts his own hair, then it contradicts the condition the barber cuts hair for those who do not cut their own hair: the barber cuts his own hair if he (the barber) does not cut his hair. On the other hand, if the barber does not cut his hair, then he (the barber) will cut his own hair, another contradiction. In either case, we seem to arrive at an inescapable contradiction: such a barber cannot possibly exist in the logical world.
Godel took a step further to show contradictions are intrinsically inevitable in his famous Incomplete Theorems. Let us start it by trying to prove the statement that “ghost exists” via valid arithmetic rules and true axioms. Suppose at halfway, we arrive at “that ghost does not exist is provable (which is quite acceptable to some of us)“ with all correct logical steps and well-known and time-tested axioms. Assuming for one moment we reject the hypothesis that ghost exists and hence conclude ghost does not exists. The conclusion that “ghost does not exist “can clearly be translated into “that ghost does not exist is provable”. However given the hypothesis is false, its proposition logically derived halfway (that ghost does not exist is provable) cannot be true because all the arithmetic rules and axioms are valid. As a result we reject the proposition derived halfway that “ghost does not exist is provable “so that we have “ghost does not exist is not provable or ghost exist is provable” because in a complete system we have only two possible outcomes: either ghost exists or does not exist or it is provable or not provable. In a nutshell, we have arrived at both that “ghost does not exist is provable” and “ghost exists is provable”. Reader can find out when the hypothesis is supported, we have two contradictory propositions as well: that “ghost exists is provable” and that “ghost does not exist is provable”. There is an intrinsic dilemma between consistency and completeness in the formal deductive logic.
On the other hand, as is commonly understood in inductive methods, one counter-example does not kill a theory. It is the p value that counts: the smaller the p value is, the more likely a null hypothesis is rejected. So those on the inductive track can enjoy fruit born in statistics. Rapid development in time series and panel data econometrics in recent years has rendered modeling in Economics, Financial and other Social Sciences much easier and results more reliable. However, like Godel’s Incomplete Theorem in deductive methodology, there is a nearly insurmountable hurdle to overcome: most statistical results had limited interpretations. As a proverbial example, an ordinary regression that fits beautifully between numbers of new-born babies and numbers of storks arrived at a locality does not provide causality. No a priori causal direction can be given so we arrive at a dead end: do arrivals of storks cause new-born babies or vice versa? It tells a close association only, which does not really yield much useful information. One must wait for a new methodology to break through and this is exactly where Granger causality models come into play.
In an important paper in Econometrica, Professor C.W.J. Granger came up with a valid framework within which causality can be statistically tested (Granger, 1969). Regression analysis with variables in level or differenced forms must first be studied lest one may encounter spurious relationship (Granger and Newbold, 1974). As a consequence, concepts of unit root, cointegration vector and error correction term must be examined before the regression analysis (Engle and Granger, 1987) can be appropriately formulated. It has become a standard procedure nowadays. Our sincere thanks go to Professor C.W.J. Granger and other prominent statisticians, who have advanced the inductive methodology significantly where their contributions can never be over-emphasized.
The aim of the book is to provide hands-on modeling techniques which are virtually indispensable in Economics and Finance and other
social sciences. The availability of statistical or econometric software (RATS, Eviews, Stata, Gauss, SAS, SPSS, R)has rendered empirical testing much more convenient. However many caveats are to be avoided which researchers frequently encounter. Numerous real world examples are provided especially in Economics and Finance. Most data are time series or panel data; one is cross-sectional. Note that all papers in this book were published in peer-reviewed journals This book is suitable to graduate or first year doctoral students who have some the familiarity with an econometric text such as Greene (2003 ).
We are grateful to late Professor C.W.J. Granger, the originator of the Granger Causality in the domain of inductive methodology. His encouragement motivated us to pursue this academic endeavor. His model has made nearly all the economics and finance theories readily testable. The first author would like to thank his late father- Mr. Hai-Nan Huang (黃海南) and mother Mrs. May-Chu Chang (張美珠) for their supports, his wife Wen-Chien Tsai (蔡雯倩), daughter Chun-Jean Huang (黃春靜) and son Brian Jason Huang (黃春浩) for their thoughtful consideration and for their loving and care. The second author would take this opportunity to thank his late parents- Mr. Chien Yang (楊健）and Mrs. Hanying Chang Yang （楊張涵英）- for their unconditional sacrifices, his wife Alice Shichia Tang Yang （唐錫嘉）and daughter Felicia Yang (楊天恩）Gorman for their unlimited patience and his granddaughter Fern Lindsey Gorman (楊長霏) for being so adorable. Without them, this book could not have been made possible.
Both deductive and inductive methods in scientific research have undergone significant changes since the beginning of the 20th century as sciences advance rapidly. Deductive method reached its pinnacle when Russel’s paradox became popular in the field of mathematical logics. The famous barber’s paradox illustrates the inevitable logical dilemma: the only barber in an isolated village does the following: (1) he cuts hair for those who do not cut their own and (2) does not cut hair for those who cut their own. Suppose 90 members in the village do not cut their hair (so barber cuts their hair) and 9 cut their own hair (thus barber does not cut their hair). The question is who cuts the barber’s hair? The intrinsic contradiction arrives in either way. If barber cuts his own hair, then it contradicts the condition the barber cuts hair for those who do not cut their own hair: the barber cuts his own hair if he (the barber) does not cut his hair. On the other hand, if the barber does not cut his hair, then he (the barber) will cut his own hair, another contradiction. In either case, we seem to arrive at an inescapable contradiction: such a barber cannot possibly exist in the logical world.
Godel took a step further to show contradictions are intrinsically inevitable in his famous Incomplete Theorems. Let us start it by trying to prove the statement that “ghost exists” via valid arithmetic rules and true axioms. Suppose at halfway, we arrive at “that ghost does not exist is provable (which is quite acceptable to some of us)“ with all correct logical steps and well-known and time-tested axioms. Assuming for one moment we reject the hypothesis that ghost exists and hence conclude ghost does not exists. The conclusion that “ghost does not exist “can clearly be translated into “that ghost does not exist is provable”. However given the hypothesis is false, its proposition logically derived halfway (that ghost does not exist is provable) cannot be true because all the arithmetic rules and axioms are valid. As a result we reject the proposition derived halfway that “ghost does not exist is provable “so that we have “ghost does not exist is not provable or ghost exist is provable” because in a complete system we have only two possible outcomes: either ghost exists or does not exist or it is provable or not provable. In a nutshell, we have arrived at both that “ghost does not exist is provable” and “ghost exists is provable”. Reader can find out when the hypothesis is supported, we have two contradictory propositions as well: that “ghost exists is provable” and that “ghost does not exist is provable”. There is an intrinsic dilemma between consistency and completeness in the formal deductive logic.
On the other hand, as is commonly understood in inductive methods, one counter-example does not kill a theory. It is the p value that counts: the smaller the p value is, the more likely a null hypothesis is rejected. So those on the inductive track can enjoy fruit born in statistics. Rapid development in time series and panel data econometrics in recent years has rendered modeling in Economics, Financial and other Social Sciences much easier and results more reliable. However, like Godel’s Incomplete Theorem in deductive methodology, there is a nearly insurmountable hurdle to overcome: most statistical results had limited interpretations. As a proverbial example, an ordinary regression that fits beautifully between numbers of new-born babies and numbers of storks arrived at a locality does not provide causality. No a priori causal direction can be given so we arrive at a dead end: do arrivals of storks cause new-born babies or vice versa? It tells a close association only, which does not really yield much useful information. One must wait for a new methodology to break through and this is exactly where Granger causality models come into play.
In an important paper in Econometrica, Professor C.W.J. Granger came up with a valid framework within which causality can be statistically tested (Granger, 1969). Regression analysis with variables in level or differenced forms must first be studied lest one may encounter spurious relationship (Granger and Newbold, 1974). As a consequence, concepts of unit root, cointegration vector and error correction term must be examined before the regression analysis (Engle and Granger, 1987) can be appropriately formulated. It has become a standard procedure nowadays. Our sincere thanks go to Professor C.W.J. Granger and other prominent statisticians, who have advanced the inductive methodology significantly where their contributions can never be over-emphasized.
The aim of the book is to provide hands-on modeling techniques which are virtually indispensable in Economics and Finance and other
social sciences. The availability of statistical or econometric software (RATS, Eviews, Stata, Gauss, SAS, SPSS, R)has rendered empirical testing much more convenient. However many caveats are to be avoided which researchers frequently encounter. Numerous real world examples are provided especially in Economics and Finance. Most data are time series or panel data; one is cross-sectional. Note that all papers in this book were published in peer-reviewed journals This book is suitable to graduate or first year doctoral students who have some the familiarity with an econometric text such as Greene (2003 ).
We are grateful to late Professor C.W.J. Granger, the originator of the Granger Causality in the domain of inductive methodology. His encouragement motivated us to pursue this academic endeavor. His model has made nearly all the economics and finance theories readily testable. The first author would like to thank his late father- Mr. Hai-Nan Huang (黃海南) and mother Mrs. May-Chu Chang (張美珠) for their supports, his wife Wen-Chien Tsai (蔡雯倩), daughter Chun-Jean Huang (黃春靜) and son Brian Jason Huang (黃春浩) for their thoughtful consideration and for their loving and care. The second author would take this opportunity to thank his late parents- Mr. Chien Yang (楊健）and Mrs. Hanying Chang Yang （楊張涵英）- for their unconditional sacrifices, his wife Alice Shichia Tang Yang （唐錫嘉）and daughter Felicia Yang (楊天恩）Gorman for their unlimited patience and his granddaughter Fern Lindsey Gorman (楊長霏) for being so adorable. Without them, this book could not have been made possible.
訂購/退換貨須知
加入金石堂 LINE 官方帳號『完成綁定』，隨時掌握出貨動態：
提醒您！！
金石堂及銀行均不會請您操作ATM! 如接獲電話要求您前往ATM提款機，請不要聽從指示，以免受騙上當！
退換貨須知：
**提醒您，鑑賞期不等於試用期，退回商品須為全新狀態**
-
依據「消費者保護法」第19條及行政院消費者保護處公告之「通訊交易解除權合理例外情事適用準則」，以下商品購買後，除商品本身有瑕疵外，將不提供7天的猶豫期：
- 易於腐敗、保存期限較短或解約時即將逾期。（如：生鮮食品）
- 依消費者要求所為之客製化給付。（客製化商品）
- 報紙、期刊或雜誌。（含MOOK、外文雜誌）
- 經消費者拆封之影音商品或電腦軟體。
- 非以有形媒介提供之數位內容或一經提供即為完成之線上服務，經消費者事先同意始提供。（如：電子書、電子雜誌、下載版軟體、虛擬商品…等）
- 已拆封之個人衛生用品。（如：內衣褲、刮鬍刀、除毛刀…等）
- 若非上列種類商品，均享有到貨7天的猶豫期（含例假日）。
- 辦理退換貨時，商品（組合商品恕無法接受單獨退貨）必須是您收到商品時的原始狀態（包含商品本體、配件、贈品、保證書、所有附隨資料文件及原廠內外包裝…等），請勿直接使用原廠包裝寄送，或於原廠包裝上黏貼紙張或書寫文字。
- 退回商品若無法回復原狀，將請您負擔回復原狀所需費用，嚴重時將影響您的退貨權益。
商品評價