economtrics of money and finance lecture one: introduction · some monetary and nancial time...
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![Page 1: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/1.jpg)
Economtrics of Money and FinanceLecture one: Introduction
Zongxin Qian
School of Finance, Renmin University of China
September 24, 2014
![Page 2: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/2.jpg)
Table of Contents
Course outline
Review of basic econometrics
Introduction to Eviews
How to think about economic/financial questions in aneconometric way
![Page 3: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/3.jpg)
Textbook
I Main textbook张成思,金融计量学–时间序列分析视角,人大出版社
I Other references(more advanced)General time series: Hamilton, Time Series Analysis,Princeton University Press.Multivariate time series: Lutkephol, New Introduction toMultiple Time Series Analysis, Springer.Cointegration: Juselius, The Cointegrated VAR model:Methodology and Applications, Oxford University Press.
I Some contents are not covered in the textbook. In that case,additional materials will be given.
![Page 4: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/4.jpg)
Textbook
I Main textbook张成思,金融计量学–时间序列分析视角,人大出版社
I Other references(more advanced)General time series: Hamilton, Time Series Analysis,Princeton University Press.Multivariate time series: Lutkephol, New Introduction toMultiple Time Series Analysis, Springer.Cointegration: Juselius, The Cointegrated VAR model:Methodology and Applications, Oxford University Press.
I Some contents are not covered in the textbook. In that case,additional materials will be given.
![Page 5: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/5.jpg)
Textbook
I Main textbook张成思,金融计量学–时间序列分析视角,人大出版社
I Other references(more advanced)General time series: Hamilton, Time Series Analysis,Princeton University Press.Multivariate time series: Lutkephol, New Introduction toMultiple Time Series Analysis, Springer.Cointegration: Juselius, The Cointegrated VAR model:Methodology and Applications, Oxford University Press.
I Some contents are not covered in the textbook. In that case,additional materials will be given.
![Page 6: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/6.jpg)
Objectives
I grasp basic tools to understand money, banking, and financialmarkets econometrically
I learn the difference between correlation and causality, andunderstand the importance of exogeneity and identification
I be able to read academic papers with modest technicalcontents
![Page 7: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/7.jpg)
Objectives
I grasp basic tools to understand money, banking, and financialmarkets econometrically
I learn the difference between correlation and causality, andunderstand the importance of exogeneity and identification
I be able to read academic papers with modest technicalcontents
![Page 8: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/8.jpg)
Objectives
I grasp basic tools to understand money, banking, and financialmarkets econometrically
I learn the difference between correlation and causality, andunderstand the importance of exogeneity and identification
I be able to read academic papers with modest technicalcontents
![Page 9: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/9.jpg)
Evaluation
I group assignment (20%)
I referee report (20%)
I final closed-book exam (60%)
![Page 10: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/10.jpg)
Contents
I Basic tools for dynamic econometrical modeling
I Reduced-form time series models: ARIMA/VAR/CVAR andVECM
I Causality, identification and structural models
I Heteroscedasticity modeling
![Page 11: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/11.jpg)
Softwares
I Relatively simple-to-use softwaresEviews/Microfit/RATS/Sata/jmultiThe last one is a free software
I More flexible but also more technical softwaresGauss/Matlab/OX/RAgain the last one is for free
I We shall learn how to apply the methods in the course withEviews. We may use other softwares occasionally.
I Caution: econometrics is not about clicking windows inEviews, copying and pasting result tables. Telling an economicstory with your results is far more important!!!
I Models, both theoretical and econometric ones, should onlybe tools to help you elaborate your idea to readers of yourpaper/report/book. It is the idea itself that is the mostimportant. Being able to build complicated models does notnecessarily mean that you are a good economist.
![Page 12: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/12.jpg)
Softwares
I Relatively simple-to-use softwaresEviews/Microfit/RATS/Sata/jmultiThe last one is a free software
I More flexible but also more technical softwaresGauss/Matlab/OX/RAgain the last one is for free
I We shall learn how to apply the methods in the course withEviews. We may use other softwares occasionally.
I Caution: econometrics is not about clicking windows inEviews, copying and pasting result tables. Telling an economicstory with your results is far more important!!!
I Models, both theoretical and econometric ones, should onlybe tools to help you elaborate your idea to readers of yourpaper/report/book. It is the idea itself that is the mostimportant. Being able to build complicated models does notnecessarily mean that you are a good economist.
![Page 13: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/13.jpg)
Softwares
I Relatively simple-to-use softwaresEviews/Microfit/RATS/Sata/jmultiThe last one is a free software
I More flexible but also more technical softwaresGauss/Matlab/OX/RAgain the last one is for free
I We shall learn how to apply the methods in the course withEviews. We may use other softwares occasionally.
I Caution: econometrics is not about clicking windows inEviews, copying and pasting result tables. Telling an economicstory with your results is far more important!!!
I Models, both theoretical and econometric ones, should onlybe tools to help you elaborate your idea to readers of yourpaper/report/book. It is the idea itself that is the mostimportant. Being able to build complicated models does notnecessarily mean that you are a good economist.
![Page 14: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/14.jpg)
Softwares
I Relatively simple-to-use softwaresEviews/Microfit/RATS/Sata/jmultiThe last one is a free software
I More flexible but also more technical softwaresGauss/Matlab/OX/RAgain the last one is for free
I We shall learn how to apply the methods in the course withEviews. We may use other softwares occasionally.
I Caution: econometrics is not about clicking windows inEviews, copying and pasting result tables. Telling an economicstory with your results is far more important!!!
I Models, both theoretical and econometric ones, should onlybe tools to help you elaborate your idea to readers of yourpaper/report/book. It is the idea itself that is the mostimportant. Being able to build complicated models does notnecessarily mean that you are a good economist.
![Page 15: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/15.jpg)
Softwares
I Relatively simple-to-use softwaresEviews/Microfit/RATS/Sata/jmultiThe last one is a free software
I More flexible but also more technical softwaresGauss/Matlab/OX/RAgain the last one is for free
I We shall learn how to apply the methods in the course withEviews. We may use other softwares occasionally.
I Caution: econometrics is not about clicking windows inEviews, copying and pasting result tables. Telling an economicstory with your results is far more important!!!
I Models, both theoretical and econometric ones, should onlybe tools to help you elaborate your idea to readers of yourpaper/report/book. It is the idea itself that is the mostimportant. Being able to build complicated models does notnecessarily mean that you are a good economist.
![Page 16: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/16.jpg)
Table of Contents
Course outline
Review of basic econometrics
Introduction to Eviews
How to think about economic/financial questions in aneconometric way
![Page 17: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/17.jpg)
OLS
Suppose our empirical model is Y = Xβ + u
e(β̂) = Y − X β̂The OLS estimator chooses a β̂ which minimizes S(β̂) = e(β̂)′e(β̂)Assumptions
I E (u|X ) = 0
I E (uu′) = σ2I
I X is of full collumn rank
Under those assumptions, the OLS estimator is the most efficient inthe class of linear unbiased estimator (the Gauss-Markov theorem)
![Page 18: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/18.jpg)
OLS
Suppose our empirical model is Y = Xβ + ue(β̂) = Y − X β̂
The OLS estimator chooses a β̂ which minimizes S(β̂) = e(β̂)′e(β̂)Assumptions
I E (u|X ) = 0
I E (uu′) = σ2I
I X is of full collumn rank
Under those assumptions, the OLS estimator is the most efficient inthe class of linear unbiased estimator (the Gauss-Markov theorem)
![Page 19: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/19.jpg)
OLS
Suppose our empirical model is Y = Xβ + ue(β̂) = Y − X β̂The OLS estimator chooses a β̂ which minimizes S(β̂) = e(β̂)′e(β̂)
Assumptions
I E (u|X ) = 0
I E (uu′) = σ2I
I X is of full collumn rank
Under those assumptions, the OLS estimator is the most efficient inthe class of linear unbiased estimator (the Gauss-Markov theorem)
![Page 20: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/20.jpg)
OLS
Suppose our empirical model is Y = Xβ + ue(β̂) = Y − X β̂The OLS estimator chooses a β̂ which minimizes S(β̂) = e(β̂)′e(β̂)Assumptions
I E (u|X ) = 0
I E (uu′) = σ2I
I X is of full collumn rank
Under those assumptions, the OLS estimator is the most efficient inthe class of linear unbiased estimator (the Gauss-Markov theorem)
![Page 21: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/21.jpg)
OLS
Suppose our empirical model is Y = Xβ + ue(β̂) = Y − X β̂The OLS estimator chooses a β̂ which minimizes S(β̂) = e(β̂)′e(β̂)Assumptions
I E (u|X ) = 0
I E (uu′) = σ2I
I X is of full collumn rank
Under those assumptions, the OLS estimator is the most efficient inthe class of linear unbiased estimator (the Gauss-Markov theorem)
![Page 22: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/22.jpg)
Specialty of financial time series
I Financial econometrical models typically use time series dataand do not satisfy those assumptions.
I Financial time series are persistent and in many models, theconditional variance is not a constant.
I In those cases, most obviously, assumption 2 fails.
I Assumption 1 also fails if the dependent variable is a persistentfinancial time series. This is because even if ut is uncorrelatedto yt−1, ut−1 certainly is correlated to yt−1. In this case, theOLS estimator is biased but still consistent if some additionalassumptions holds (will discuss in coming lectures). In thiscase, you can still use OLS if your sample is large enough.
![Page 23: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/23.jpg)
Specialty of financial time series
I Financial econometrical models typically use time series dataand do not satisfy those assumptions.
I Financial time series are persistent and in many models, theconditional variance is not a constant.
I In those cases, most obviously, assumption 2 fails.
I Assumption 1 also fails if the dependent variable is a persistentfinancial time series. This is because even if ut is uncorrelatedto yt−1, ut−1 certainly is correlated to yt−1. In this case, theOLS estimator is biased but still consistent if some additionalassumptions holds (will discuss in coming lectures). In thiscase, you can still use OLS if your sample is large enough.
![Page 24: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/24.jpg)
Specialty of financial time series
I Financial econometrical models typically use time series dataand do not satisfy those assumptions.
I Financial time series are persistent and in many models, theconditional variance is not a constant.
I In those cases, most obviously, assumption 2 fails.
I Assumption 1 also fails if the dependent variable is a persistentfinancial time series. This is because even if ut is uncorrelatedto yt−1, ut−1 certainly is correlated to yt−1. In this case, theOLS estimator is biased but still consistent if some additionalassumptions holds (will discuss in coming lectures). In thiscase, you can still use OLS if your sample is large enough.
![Page 25: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/25.jpg)
Specialty of financial time series
I Financial econometrical models typically use time series dataand do not satisfy those assumptions.
I Financial time series are persistent and in many models, theconditional variance is not a constant.
I In those cases, most obviously, assumption 2 fails.
I Assumption 1 also fails if the dependent variable is a persistentfinancial time series. This is because even if ut is uncorrelatedto yt−1, ut−1 certainly is correlated to yt−1. In this case, theOLS estimator is biased but still consistent if some additionalassumptions holds (will discuss in coming lectures). In thiscase, you can still use OLS if your sample is large enough.
![Page 26: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/26.jpg)
Some monetary and financial time series: CPI
Figure: China’s Quarterly CPI
96
98
100
102
104
106
108
1102001/01
2001/08
2002/03
2002/10
2003/05
2003/12
2004/07
2005/02
2005/09
2006/04
2006/11
2007/06
2008/01
2008/08
2009/03
2009/10
2010/05
2010/12
2011/07
2012/02
2012/09
2013/04
99
100
100
101
101
102
102
CPI CPI
![Page 27: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/27.jpg)
Some monetary and financial time series: exchange rate
Figure: China’s real effective exchange rate
0
20
40
60
80
100
120
140
2005
08
2005
11
2006
02
2006
05
2006
08
2006
11
2007
02
2007
05
2007
08
2007
11
2008
02
2008
05
2008
08
2008
11
2009
02
2009
05
2009
08
2009
11
2010
02
2010
05
2010
08
2010
11
2011
02
2011
05
2011
08
2011
11
2012
02
2012
05
2012
08
2012
11
Source: CEIC
![Page 28: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/28.jpg)
Some monetary and financial time series: exchange rate
Figure: China’s 3m treasury bill rate
Source: CEIC
![Page 29: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/29.jpg)
Some monetary and financial time series: exchange rate
Figure: Shanghai Stock index A share
Source: CEIC
![Page 30: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/30.jpg)
Model specifications and OLS
I Including redundant variables to the r.h.s. reduces estimationefficiency.
I Omitting important explanatory variables causes estimationbias.
I Adding restrictions to the model may improve estimationefficiency.
I But adding false restrictions to the model generatesestimation bias.
I Those results not only apply to OLS but also to many otherestimators. They are frequently encountered byeconometricians interested in many fields of economicsincluding financial economics.
![Page 31: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/31.jpg)
Model specifications and OLS
I Including redundant variables to the r.h.s. reduces estimationefficiency.
I Omitting important explanatory variables causes estimationbias.
I Adding restrictions to the model may improve estimationefficiency.
I But adding false restrictions to the model generatesestimation bias.
I Those results not only apply to OLS but also to many otherestimators. They are frequently encountered byeconometricians interested in many fields of economicsincluding financial economics.
![Page 32: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/32.jpg)
Model specifications and OLS
I Including redundant variables to the r.h.s. reduces estimationefficiency.
I Omitting important explanatory variables causes estimationbias.
I Adding restrictions to the model may improve estimationefficiency.
I But adding false restrictions to the model generatesestimation bias.
I Those results not only apply to OLS but also to many otherestimators. They are frequently encountered byeconometricians interested in many fields of economicsincluding financial economics.
![Page 33: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/33.jpg)
Model specifications and OLS
I Including redundant variables to the r.h.s. reduces estimationefficiency.
I Omitting important explanatory variables causes estimationbias.
I Adding restrictions to the model may improve estimationefficiency.
I But adding false restrictions to the model generatesestimation bias.
I Those results not only apply to OLS but also to many otherestimators. They are frequently encountered byeconometricians interested in many fields of economicsincluding financial economics.
![Page 34: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/34.jpg)
Model specifications and OLS
I Including redundant variables to the r.h.s. reduces estimationefficiency.
I Omitting important explanatory variables causes estimationbias.
I Adding restrictions to the model may improve estimationefficiency.
I But adding false restrictions to the model generatesestimation bias.
I Those results not only apply to OLS but also to many otherestimators. They are frequently encountered byeconometricians interested in many fields of economicsincluding financial economics.
![Page 35: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/35.jpg)
Basic inference based on OLS estimation
I Simplest assumption on the error term u ∼ i .i .d .N(0, σ2I )
I Under this assumption β̂ ∼ N(β, σ2(X ′X )−1)
I t = β̂i−βiσ̂ξ
−1/2ii
, where ξii is the row i , column i element of the
matrix (X ′X )−1
β̂i−βiσξ
−1/2ii
∼ N(0, 1)
σ̂2
σ2 ∼ χ2(T − K )
I t =
β̂i−βiσξ
−1/2iiσ̂σ
∼ t(T − K )
![Page 36: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/36.jpg)
Basic inference based on OLS estimation
I Simplest assumption on the error term u ∼ i .i .d .N(0, σ2I )
I Under this assumption β̂ ∼ N(β, σ2(X ′X )−1)
I t = β̂i−βiσ̂ξ
−1/2ii
, where ξii is the row i , column i element of the
matrix (X ′X )−1
β̂i−βiσξ
−1/2ii
∼ N(0, 1)
σ̂2
σ2 ∼ χ2(T − K )
I t =
β̂i−βiσξ
−1/2iiσ̂σ
∼ t(T − K )
![Page 37: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/37.jpg)
Basic inference based on OLS estimation
I Simplest assumption on the error term u ∼ i .i .d .N(0, σ2I )
I Under this assumption β̂ ∼ N(β, σ2(X ′X )−1)
I t = β̂i−βiσ̂ξ
−1/2ii
, where ξii is the row i , column i element of the
matrix (X ′X )−1
β̂i−βiσξ
−1/2ii
∼ N(0, 1)
σ̂2
σ2 ∼ χ2(T − K )
I t =
β̂i−βiσξ
−1/2iiσ̂σ
∼ t(T − K )
![Page 38: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/38.jpg)
Basic inference based on OLS estimation
I Simplest assumption on the error term u ∼ i .i .d .N(0, σ2I )
I Under this assumption β̂ ∼ N(β, σ2(X ′X )−1)
I t = β̂i−βiσ̂ξ
−1/2ii
, where ξii is the row i , column i element of the
matrix (X ′X )−1
β̂i−βiσξ
−1/2ii
∼ N(0, 1)
σ̂2
σ2 ∼ χ2(T − K )
I t =
β̂i−βiσξ
−1/2iiσ̂σ
∼ t(T − K )
![Page 39: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/39.jpg)
OLS: more complicated cases
Figure: OLS asymptotics in more general cases
Source: Hamilton(1994)
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MLE
I L(Y |β) =∏T
t=1 L(Yt |β)L(Yt |β) is conditional probability/density of Yt given β
I The MLE choose parameters in β which maximize thelikelihood function.
I Typically, it is easier to work with the log likelihoodlogL(Y |β) = ΣT
t=1logL(Yt |β)
![Page 41: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/41.jpg)
MLE
I L(Y |β) =∏T
t=1 L(Yt |β)L(Yt |β) is conditional probability/density of Yt given β
I The MLE choose parameters in β which maximize thelikelihood function.
I Typically, it is easier to work with the log likelihoodlogL(Y |β) = ΣT
t=1logL(Yt |β)
![Page 42: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/42.jpg)
MLE under normality
I Yt = a + bXt + ut , u ∼ N(0, σ2)
I L(Yt |β) = (2πσ2)−1/2exp{−[Yt − (a + bXt)]2/2σ2}I logL(Y |β) = ΣT
t=1logL(Yt |β) =
−Tlog(2πσ2)2 − 1
2σ2 ΣTt=1[Yt − (a + bXt)]2
I The maximum obtains when ΣTt=1[Yt − (a + bXt)]2 is
minimized. MLE=OLS!
![Page 43: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/43.jpg)
MLE under normality
I Yt = a + bXt + ut , u ∼ N(0, σ2)
I L(Yt |β) = (2πσ2)−1/2exp{−[Yt − (a + bXt)]2/2σ2}
I logL(Y |β) = ΣTt=1logL(Yt |β) =
−Tlog(2πσ2)2 − 1
2σ2 ΣTt=1[Yt − (a + bXt)]2
I The maximum obtains when ΣTt=1[Yt − (a + bXt)]2 is
minimized. MLE=OLS!
![Page 44: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/44.jpg)
MLE under normality
I Yt = a + bXt + ut , u ∼ N(0, σ2)
I L(Yt |β) = (2πσ2)−1/2exp{−[Yt − (a + bXt)]2/2σ2}I logL(Y |β) = ΣT
t=1logL(Yt |β) =
−Tlog(2πσ2)2 − 1
2σ2 ΣTt=1[Yt − (a + bXt)]2
I The maximum obtains when ΣTt=1[Yt − (a + bXt)]2 is
minimized. MLE=OLS!
![Page 45: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/45.jpg)
MLE under normality
I Yt = a + bXt + ut , u ∼ N(0, σ2)
I L(Yt |β) = (2πσ2)−1/2exp{−[Yt − (a + bXt)]2/2σ2}I logL(Y |β) = ΣT
t=1logL(Yt |β) =
−Tlog(2πσ2)2 − 1
2σ2 ΣTt=1[Yt − (a + bXt)]2
I The maximum obtains when ΣTt=1[Yt − (a + bXt)]2 is
minimized. MLE=OLS!
![Page 46: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/46.jpg)
Table of Contents
Course outline
Review of basic econometrics
Introduction to Eviews
How to think about economic/financial questions in aneconometric way
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Starting Eviews
Figure: Eviews interface
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Create a new workfile
Figure: Create a new workfile
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Create a new object
Figure: Create a new object
![Page 50: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/50.jpg)
Estimate an equation
Figure: Estimate an equation
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Postestimation
Figure: postestimation
Dependent Variable: INF_COREMethod: Least SquaresDate: 09/10/13 Time: 19:35Sample (adjusted): 1978 2007Included observations: 30 after adjustments
Variable Coefficient Std. Error t-Statistic Prob.
INF_CORE(-1) 0.640693 0.082579 7.758561 0.0000INF_CORE(-1)*DV -0.568887 0.157075 -3.621752 0.0013
OKY_OECD(-1) -5.368782 3.429265 -1.565578 0.1300YGAP_OECD(-1) 2.123996 0.941751 2.255369 0.0331
C 2.615833 0.618778 4.227419 0.0003
R-squared 0.828510 Mean dependent var 4.889859Adjusted R-squared 0.801071 S.D. dependent var 3.362778S.E. of regression 1.499847 Akaike info criterion 3.799614Sum squared resid 56.23849 Schwarz criterion 4.033147Log likelihood -51.99422 Hannan-Quinn criter. 3.874324F-statistic 30.19523 Durbin-Watson stat 1.370900Prob(F-statistic) 0.000000
![Page 52: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/52.jpg)
Importing data
file: open: foreign data as workfileor there is a software called stattransfer which can flexibly transferdata formats. But you have to pay for it yourself since our schooldoes not buy license for it.
![Page 53: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/53.jpg)
Table of Contents
Course outline
Review of basic econometrics
Introduction to Eviews
How to think about economic/financial questions in aneconometric way
![Page 54: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/54.jpg)
Instability
Figure: US Phillips curve slope over time
12
197510
1980
8
10
1974 19798
1970
19711972
1976197719786
Infl
atio
n ra
te (
%)
19671968
196919734
19611962
1964
196519662
1960 1963
00 1 2 3 4 5 6 7 8 9 10
Unemployment rate (%)
Figure: Source: Gorton(2011)
![Page 55: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/55.jpg)
Omitted variable and media claims
I Many media articles use some informal form of OLS forargumentation
I Look at this one: China’s financial development is lower thanthe USA, but its growth rate is higher, so finance is notrelevant for real development.
I But is this example representative?
I Can’t the difference in growth caused by other factors?
I USA is a more development economy which is normally slowerin growth since it is already close to the steady state butChina is in transition
I China has a higher population density which can create alarger market which provides more space for divisions of laborand specialization
![Page 56: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/56.jpg)
Omitted variable and media claims
I Many media articles use some informal form of OLS forargumentation
I Look at this one: China’s financial development is lower thanthe USA, but its growth rate is higher, so finance is notrelevant for real development.
I But is this example representative?
I Can’t the difference in growth caused by other factors?
I USA is a more development economy which is normally slowerin growth since it is already close to the steady state butChina is in transition
I China has a higher population density which can create alarger market which provides more space for divisions of laborand specialization
![Page 57: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/57.jpg)
Omitted variable and media claims
I Many media articles use some informal form of OLS forargumentation
I Look at this one: China’s financial development is lower thanthe USA, but its growth rate is higher, so finance is notrelevant for real development.
I But is this example representative?
I Can’t the difference in growth caused by other factors?
I USA is a more development economy which is normally slowerin growth since it is already close to the steady state butChina is in transition
I China has a higher population density which can create alarger market which provides more space for divisions of laborand specialization
![Page 58: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/58.jpg)
Omitted variable and media claims
I Many media articles use some informal form of OLS forargumentation
I Look at this one: China’s financial development is lower thanthe USA, but its growth rate is higher, so finance is notrelevant for real development.
I But is this example representative?
I Can’t the difference in growth caused by other factors?
I USA is a more development economy which is normally slowerin growth since it is already close to the steady state butChina is in transition
I China has a higher population density which can create alarger market which provides more space for divisions of laborand specialization
![Page 59: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/59.jpg)
Omitted variable and media claims
I Many media articles use some informal form of OLS forargumentation
I Look at this one: China’s financial development is lower thanthe USA, but its growth rate is higher, so finance is notrelevant for real development.
I But is this example representative?
I Can’t the difference in growth caused by other factors?
I USA is a more development economy which is normally slowerin growth since it is already close to the steady state butChina is in transition
I China has a higher population density which can create alarger market which provides more space for divisions of laborand specialization
![Page 60: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/60.jpg)
Reverse causality
I You find financial stock returns negatively correlated with gov.bond spreads
I Can you jump to conclude that worse financial sectorincreases the probability of gov. default?
I Not sure, ’cause there is possibility that the reality is the otherway around.
I The gov. default risk is too high and the financial sector holdstoo high a share of gov. bonds, so its performance isnegatively affected.
![Page 61: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/61.jpg)
Reverse causality
I You find financial stock returns negatively correlated with gov.bond spreads
I Can you jump to conclude that worse financial sectorincreases the probability of gov. default?
I Not sure, ’cause there is possibility that the reality is the otherway around.
I The gov. default risk is too high and the financial sector holdstoo high a share of gov. bonds, so its performance isnegatively affected.
![Page 62: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/62.jpg)
Reverse causality
I You find financial stock returns negatively correlated with gov.bond spreads
I Can you jump to conclude that worse financial sectorincreases the probability of gov. default?
I Not sure, ’cause there is possibility that the reality is the otherway around.
I The gov. default risk is too high and the financial sector holdstoo high a share of gov. bonds, so its performance isnegatively affected.
![Page 63: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/63.jpg)
Summary
I You have to think right before you can model right.
Roadmap for an empirical study typically looks like this:
I You first think about the causality chains, then write down amodel.
I Second, you derive testable hypothesis from the model andput it to data.
I If the hypothesis is accepted, then maybe your model is good.Otherwise, you may have to think again and rework on yourmodel.
I Notice that, models are only abstraction of the real world.Very often, there are omitted factors in the model. In manycases, you have to test your model in a much richer contextand require statistical identification of the model.
![Page 64: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/64.jpg)
Summary
I You have to think right before you can model right.Roadmap for an empirical study typically looks like this:
I You first think about the causality chains, then write down amodel.
I Second, you derive testable hypothesis from the model andput it to data.
I If the hypothesis is accepted, then maybe your model is good.Otherwise, you may have to think again and rework on yourmodel.
I Notice that, models are only abstraction of the real world.Very often, there are omitted factors in the model. In manycases, you have to test your model in a much richer contextand require statistical identification of the model.
![Page 65: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/65.jpg)
Summary
I You have to think right before you can model right.Roadmap for an empirical study typically looks like this:
I You first think about the causality chains, then write down amodel.
I Second, you derive testable hypothesis from the model andput it to data.
I If the hypothesis is accepted, then maybe your model is good.Otherwise, you may have to think again and rework on yourmodel.
I Notice that, models are only abstraction of the real world.Very often, there are omitted factors in the model. In manycases, you have to test your model in a much richer contextand require statistical identification of the model.
![Page 66: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/66.jpg)
Summary
I You have to think right before you can model right.Roadmap for an empirical study typically looks like this:
I You first think about the causality chains, then write down amodel.
I Second, you derive testable hypothesis from the model andput it to data.
I If the hypothesis is accepted, then maybe your model is good.Otherwise, you may have to think again and rework on yourmodel.
I Notice that, models are only abstraction of the real world.Very often, there are omitted factors in the model. In manycases, you have to test your model in a much richer contextand require statistical identification of the model.
![Page 67: Economtrics of Money and Finance Lecture one: Introduction · Some monetary and nancial time series: CPI Figure:China’s Quarterly CPI 110 108 106 104 102 100 98 96 ôlÿ W!³¥](https://reader035.vdocuments.pub/reader035/viewer/2022081410/60a31616cabd0211c87b4b05/html5/thumbnails/67.jpg)
Summary
I You have to think right before you can model right.Roadmap for an empirical study typically looks like this:
I You first think about the causality chains, then write down amodel.
I Second, you derive testable hypothesis from the model andput it to data.
I If the hypothesis is accepted, then maybe your model is good.Otherwise, you may have to think again and rework on yourmodel.
I Notice that, models are only abstraction of the real world.Very often, there are omitted factors in the model. In manycases, you have to test your model in a much richer contextand require statistical identification of the model.