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Unformatted text preview: Financial Econometrics and Statistics:
Past, Present, and Future
Past,
By
Dr. ChengFew Lee
Distinguished Professor of Finance, Rutgers University, USA
Distinguished
Editor, Review of Quantitative Finance and Accounting
Editor,
Editor, Review of Pacific Basin Financial Markets and Policies To be presented at the “The 4th NCTU International Finance Conference ” on January 7, 2011. Outline
1. Introduction
1.
2. Single equation regression methods
3. Simultaneous equation models
4. Panel data analysis
5. Alternative methods to deal with measurement error
6. Time series analysis
7. Spectral Analysis
8. Statistical distributions
9. Principle components and factor analyses
10. Nonparametric, Semiparametric, and GMM analyses
11. Path analysis
12. Cluster analysis
13. Summary and concluding remarks 1. Introduction
1.
Financial econometrics and statistics have become more important for
empirical research in both finance and accounting. Asset pricing and corporate
finance research have used both econometrics and statistics, such as single
equation multiple regression, simultaneous regression, panel data analysis.
Portfolio theory and management have used different statistics distributions,
such as normal distribution, stable distribution, and log normal distribution.
Options and futures have used binomial distribution, log normal distribution,
noncentral chi square distribution, and so on. Auditing has used sampling
technique to determine the sampling error for auditing. The main purpose of
this handbook is to review financial econometrics and statistics used in the
research of finance and accounting for last five decades. Some suggestions to
apply these techniques in future research are also recommended.
apply
The second section of this paper will discuss alternative single equation
regression estimation methods. Section 3 will discuss simultaneous equation
models. Section 4 will discuss panel data analysis. Section 5 will discuss
alternative methods to deal with measurement error. Section 6 will discuss time
series analysis. Section 7 will discuss spectral Analysis. Section 8 will discuss
statistical distribution. Section 9 will discuss principle components and factor
analyses. Section 10 will discuss nonparametric, semiparametric, and GMM
analyses. Section 11 will discuss path analysis. Section 12 will discuss cluster
analysis. Finally, section 13 will summarize the paper.
analysis. 2.
2. Single equation regression methods In this section, we will discuss important issues related to single equation
In
regression estimation method. They are (a) heteroskedasticity, (b)
specification error, (c) measurement error, (d) quantile regression, and
(e) testing structural change.
(e)
a. Heteroskedasticity
 White method
 NeweyWest method
b. Specification error
 Thursby, JASA (1985)
 “Alternative Specifications and Estimation Methods for Determining
“Alternative
Random Beta Coefficients: Comparison and Extensions,” (with Robert
C.R. Rkok and David C. Cheng), Journal of Financial Studies, October
1996
1996
 “Power of Alternative Specification Errors Tests in Identifying
“Power
Misspecified Market Models,” (with David C. Cheng), The Quarterly
Review of Economics and Business, Fall, 1986.
Review
 Cheng and Lee, QREB (1986)
 Maddala et al., Handbook of Statistics 14: Statistics Methods in Finance
Maddala
(1996)
(1996) 2.
2.
c.
 Single equation regression methods Measurement error
Lee and Jen, JFQA (1978)
Kim, JF (1995)
Kim, Handbook of Quantitative Finance and Risk Management (2010)
Miller and Modigliani, AER (1966) d. Quantile regression
e. Nonlinear regression
BoxCox transformation
 Lee JF (1976)
 Lee JFQA (1977)
 Lee JFQA ()
 “Generalized Financial Ratio Adjustment Processes and Their Implications,” (with
“Generalized
Thomas J. Frecka), Journal of Accounting Research, Spring, 1983.
Thomas
 “A Generalized Functional Form Approach to Investigate the Density Gradient
“A
and the Price Elasticity of Demand for Housing,” (with James B. Kau), Urban
Studies, April, 1976.
Studies,
 Liu (2005)
 Kau, Lee, and Sirmans. Urban Econometrics: Model developments and empirical
Kau,
results (1986)
results 2.
2. Single equation regression methods f. Testing structural change
 Yang (1989)
 Lee et al. (2010) Optimal payout ratio under …
 Lee et al. (2010) Threshold..
 Chow test and moving chow test
(Chow, Econometrica, 1960)
(Strucchange: An R Package for Testing for Structural Change in Lineaer Regression Models,
(Strucchange:
Journal of Statistical Software, 2002)
Journal
 Threshold regression
(Hansen, Journal of Business & Economic Statistics, 1997)
(Hansen, Econometrica, 1996, 2000)
(Journal of Econometrics, 1999, 2000).
(Journal
 Generalize fluctuation test
(Juan and Hornik, Eonometric Reviews, 1995)
g. Probit and Logit regression for credit risk analysis
 Hwang, R.C.*, Cheng, K.F., and Lee, C.F. (2009). On multipleclass prediction of issuer
Hwang,
crediting ratings. Journal of Applied Stochastic Models in Business and Industry, 25, 535crediting
550. (SCI)
 Hwang, R.C.*, Wei, H.C., Lee, J.C., and Lee, C.F. (2008). On prediction of financial distress
Hwang,
using the discretetime survival model. Journal of Financial Studies, 16, 99129. (TSSCI)
using
 Cheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using the discretetime semiparametric hazard model. Accepted by Quantitative Finance. (SSCI) 3.
3. Simultaneous equation models In this section, we will discuss alternative methods to deal with simultaneous equation
models. There are (a) 2 stage least square (2SLS) method, (b) seemly uncorrelated
regression (SUR) method, (c) 3 stage least square (3SLS) method, and (d)
disequilibrium estimation method.
disequilibrium
a. 2 stage least square (2SLS) method
 Lee JFQA (1976)
 M&M AER (1966)
 Chen et al., Corporate Governance and International Review (2007)
b. Seemly uncorrelated regression (SUR) method
 Lee JFQA (1981)
c. 3 stage least square (3SLS) method
 Chen et al., Corporate Governance and International Review (2007)
d.
 Disequilibrium estimation method
Tsai (2005)
CW Sealy JF (1979)
Lee, Tsai, and Lee, subjected to revision for Quantitative Finance (2010)
WJ Mayer, Journal of Econometrics, 1989
RW David, JBF, 1987
C Martin, Review of Economics and Statistics, 1990 4.
4. Panel data analysis In this section, we will discuss important issues related to panel data
analysis. There are (a) fixed effect model, (b) random effect model, and
(c) clustering effect model.
(c)
 Wooldridge, Econometric Analysis of Cross Secion and Panel Data, MIT
Wooldridge,
Press (2002)
Press
 BalTagi, Econometric Analysis of Panel Data, Wiley (2008)
 Hsiao, Analysis of Panel Data, Cambridge University Press (2002)
a. Fixed effect model
 Lee JFQA (1977)
 Lee et al. JCF (2010)
b. Random effect model
 Lee JFQA (1977)
c.
 Clustering effect model of panel data analysis
Thompson (2006)
Cameron, Gelbach, and Miller (2006)
Petersen (2009) 5.
5. Alternative methods to deal with
Alternative
measurement error
measurement In this section, we will discuss Alternative methods to deal with
In
measurement error problem. They are (a) LISREL model, (b) multifactor
and multiindicator (MIMIC) model, and (c) partial least square method.
and
 Lee (1973)
a. LISREL model
 Titman and Wessal JF (1988)
 Chang (1999)
 Chang and Lee QREF (2008)?
b. Multifactor and multiindicator (MIMIC) model
 Lee et al. QREB (2009)
 Wei (1984)
c.
 Partial least square method
JE Core  Journal of Law, Economics, and Organization (2000)
Ittner et al. AR (1997)
Lambert and Lacker () 6. Time series analysis
6.
 In this section, we will discuss important models in time series analysis. They are (a)
ARIMA, (b) ARCH, (c) GARCH, and (d) Fractional GARCH.
ARIMA,
Anderson, T. W., The statistical Analysis of Time Series (1994), WileyInterscience.
Hamilton, J. D., Time Series Analysis (1994), Princeton University Press. a. ARIMA
 Myers, JFM (1991)
b. ARCH
 Lien and Shrestha, JFM (2007)
c. GARCH
 Lien, JFM (2010)
d. Fractional GARCH
 Leon and VaelloSebastia, JBF (2009)
e.
 Combined forecasting
Lee (1996)
Lee and Cummins (1998) 7. Spectral Analysis
7.  In this section, we will discuss the spectral
analysis.
analysis.
Chacko and Viceira, Journal of
Chacko
Econometrics (2003)
Econometrics
Heston, RFS (1993)
Anderson, T. W., The statistical Analysis
Anderson,
of Time Series (1994)
of 8. Statistical distributions
8.
In this section, we will discuss different statistical distributions. They are (a) binomial
In
distribution, (b) poisson distribution, (c) normal distribution, (d) log normal
distribution, (e) Chisquare distribution, (f) noncentral Chisquare distribution, (g)
Wishart distribution, (h) stable distribution, and (i) other distributions.
Wishart
a. Binomial distribution
 Cox, Ross, and Rubinstein (1979)
 Rendleman and Barter (1979)
b. Poisson distribution
c. Normal distribution
d. Log Normal distribution
 Chu (1984)
e. Chisquare distribution
f. Noncentral Chisquare distribution
 M. Schroder, Journal of Finance (1989)
g. Wishart distribution
 Chen and Lee, Management Science (1981)
h. Stable distribution
 E. Fama, JASA (1971)
i. Other distributions 9.
9. Principle components and factor
Principle
analyses
analyses In this section, we will discuss principle
In
components and factor analyses.
components
 Anderson, T. W., An Introduction to
Anderson,
Multivariate Statistical Analysis (2003),
WileyInterscience.
WileyInterscience.
a.Principle components
b.Factor analyses 10.
10. Nonparametric, Semiparametric, and
Nonparametric,
GMM analyses
GMM In this section, nonparametric, semipaprmetric, and GMM analyses will be
In
discussed.
discussed.
a. Nonparametric analysis
 AitSahalia and Lo, Journal of Econometrics (2000)
b. Semiparametric analysis
 Hwang, R.C.*, Chung, H., andChu, C.K. (2009). Predicting issuer credit ratings
Hwang,
using a semiparametric method. Accepted by Journal of Empirical Finance.
 Cheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using
Cheng,
the discretetime semiparametric hazard model. Accepted by Quantitative
Finance.
 Hwang, R.C.*, Cheng, K.F., and Lee, J.C. (2007). A semiparametric method for
Hwang,
predicting bankruptcy. Journal of Forecasting, 26, 317342.
c. GMM analysis
c.
 Chen et al., Corporate Governance and International Review (2007)
 Brick et al. “The Motivations for Issuing Putable Debt: An Empirical Analysis”
Brick
forthcoming for Handbook of Quantitative Finance and Econometrics, 2011.
forthcoming 11. Path analysis
11.
In this section, path analysis will be
In
discussed.
discussed. 12.
12. Cluster analysis In this section, Cluster analysis will be
discussed.
discussed.
 Brown and Goetzmann (JFE, 1997)
 Finding Groups in Data: An Introduction to
Finding
Cluster Analysis, L Kaufman, Peter J
Rousseeuw, Wiley, 2005
Rousseeuw, 13.
13. Summary and concluding remarks In this paper, we have review both financial
econometrics and statistics methods which
has been used in finance and accounting
research for last four decades. In this
handbook, we include research papers in
both finance and accounting which present
different methodologies in detailed.
Therefore, it will be very useful to
researcher when they try to perform similar
kind of research.
kind References
References
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Chang,
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Cheng, K.F.,Chu, C.K., and Hwang, R.C.* (2009). Predicting bankruptcy using the discretetime
semiparametric hazard model. Accepted by Quantitative Finance. (SSCI)
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Hwang, R.C.*, Cheng, K.F., and Lee, C.F. (2009). On multipleclass prediction of issuer crediting
ratings. Journal of Applied Stochastic Models in Business and Industry, 25, 535550. (SCI)
Hwang, R.C.*, Cheng, K.F., and Lee, J.C. (2007). A semiparametric method for predicting bankruptcy.
Journal of Forecasting, 26, 317342.
Hwang, R.C.*, Chung, H., and Chu, C.K. (2009). Predicting issuer credit ratings using a
semiparametric method. Accepted by Journal of Empirical Finance. (SSCI)
Hwang, R.C.*, Wei, H.C., Lee, J.C., and Lee, C.F. (2008). On prediction of financial distress using the
discretetime survival model. Journal of Financial Studies, 16, 99129. (TSSCI)
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