康奈尔大学洪永淼教授计量经济学访谈

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 Interview with Professor Yongmiao HONG

I. Welcome and Opening Remarks
Anny:  Good afternoon.  As winter comes round, many memories, hopes and aspirations burgeon in our hearts when we join this grand academic conference, which is so inspiriting and so charged with profound educational meaning.
The distinguished guest who will address us today is Professor Yongmiao Hong. Professor Yongmiao Hong is a great econometrician of our time. He is best known, perhaps, for his achievements in economics research and especially for the expertise that concerns us here this afternoon, which is econometrics. Apart from his numerous econometrics publications in high ranking journals, his dedications to education win him respect worldwide.
Professor Hong, could you tell us something about how you came to study economics and what attracted you about econometrics?
 
related link：https://bbs.pinggu.org/thread-278869-1-1.html&page=1
agenda：https://bbs.pinggu.org/thread-286382-1-1.html&page=1

 Professor Ruilong Yang, Dean of the School of Economics, Renmin University，delievers below  welcome address. 

[此贴子已经被作者于2008-4-26 15:59:11编辑过]

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关键词：计量经济学 康奈尔大学 计量经济 康奈尔 洪永淼 计量经济学 教授 康奈尔大学 访谈 洪永淼

热烈欢迎洪永淼教授光临人大经济论坛，洪永淼教授在百忙之中仍抽空指导经济学子，并与论坛经济学爱好者进行在线交流，在此，我谨代表中国人民大学经济学院对洪永淼教授的光临表示欢迎和感谢，预祝此次访谈获得圆满成功！

中国人民大学经济学院　杨瑞龙

这里对华人的几个搞计量的经济学学家做一个简短的评论。

在世界上，华人现在做计量得很多，按照名气排名的话，Chen Xiaohong （Yale & NYU）, Fan jianqing(Princeton)(统计+计量),Lee Lungei(Ohio State), Hong yongmiao(Cornell),Li Qi(TAMU), Ai Chunyong(Florida), Bai Jushan(NYU), Fan Yanqing(Vandbilt), Xiao Zhijie(BU), 老一辈的还有 Chow G. (Princeton)(邹至庄), 刁锦寰(Wiconsin)等。都是正教授。

从数量上讲，洪永淼的贡献很多。另外一个很牛的人就是 Li Qi. 他们和很多在美国的华人经济学家一样，写了很多文章。在很多顶尖杂志上灌了很多水。但是很多是修修补补，我们称为Brick-Mover, 贡献很marginal.

但是美国这个社会和中国不一样，质量重于数量。

最典型的是陈晓红 （Yale and NYU）,一个矮小的中国女人，就靠两篇论文（证明了GMM在SEIVE中得一致性，效率性和Normality ）。刚刚成了econometric society的 fellow.这是大陆出生的经济学家中的第一个。econometric society的 fellow是每一个经济学家的梦想，估计是除了 nobel 炸药奖之后的荣誉了。可以这么讲，要获得炸药奖，首先必须是econometric society的 fellow。要不，根本没有机会获得提名。
感谢洪永淼教授为中国经济学做的贡献，我们也希望洪永淼教授能够成为econometric society的 fellow。

Background Information

It's our great honor and pleasure to announce that the world-class econometrician Professor Yongmiao Hong, the Cheung Kong Scholar Chair Professor of Xiamen University and the  Director of Wang Yanan Institute for Studies in Economics (WISE)，will visit Renmin University Economics Site for an online academic activity.Professor Ruilong Yang, Dean of School of Economics of Renmin University has attached great importance to Professor Yongmiao Hong’s academic activity, and has delivered HIS address at 2th post: “Warm welcome to Professor Yongmiao Hong! I have the great pleasure to extend, on behalf the school of Economics of Renmin University, our most  cordial greetings and sincere thanks to Professor Yongmiao Hong for taking time out of his busy schedule to guide economics students and communicate with economics enthusiasts. Join us in making this online academic successful!”
 
Professor Yongmiao Hong is a Tenured Professor of Cornell University, a Cheung Kong Scholars Chair Professor of Xiamen University and an Awardee of several funds for Distinguished Young Scientists of National Natural Sciences Foundation of China, and among others.

Born in 1964, Professor Yongmiao Hong received his B.S. in Physics from Xiamen University in 1985. Over the period 1986~1988, he completed his post- graduate studies at the Economics Training Center of Renmin University and at the Economics Department of Xiamen University. Upon graduation from Xiamen University with a M.S. degree in Economics, he traveled overseas to attend University of California at San Diego, where he was a recipient of various awards and studied under the supervision of Professor Clive Granger and Halbert White (2003 Nobel Laureates). 

Today as an internationally renowned Econometrician，Professor Yongmiao Hong specialized in Econometrics, Time Series Analysis & Application, Financial Econometrics, and Economics and Empirical Research in Financial Markets in China. In his distinguished academic career, he has published extensively on such TOP journals as Econometrica, Econometric Theory, Journal of Econometrics, Journal of Political Economy, Journal of Quarter Economics, Review of Economic Studies, Review of Financial Studies, Review of Economics and Statistics, and among others.   Based on standardized pages on 16 leading theoretical econometrics publications,   World Econometrics Rankings 1989-2005,  written by Badi Baltagi and recently published on Econometric Theory  (Volume 23, pp952-1012, 2007), places Professor Yongmiao Hong,  with 340 standardized pages, among the top 15  theoretical econometricians worldwide over the period 1989--2005. Over the subperiods 1995--2005 and 2000-2005, Professor Yongmiao Hong has the 7th position in theoretical econometrics based on the above standardized page counts.

Apart from his notable academic achievements, Professor Yongmiao Hong is praised ALL OVER the world for his immense contributions to the field of economic education. He has devoted much of his energy to teaching. To students, Professor Yongmiao Hong is not just brilliant; he is gracious, supportive, and inspiring, too. Professor Yongmiao Hong is a unique blend of soul, talent, and superb credentials.

Professor Yongmiao Hong has been exerting prominent influence on economics theory and education worldwide. For more information about Professor Yongmiao Hong, please refer to www.wise.xmu.edu.cn/viewNews.asp?id=442

Despite his busy schedule, Professor Yongmiao Hong sets up time to take part in online academic activity and shares his insight on economics with us.  Readers who would like to communicate with Professor Yongmiao Hong are welcomed to post questions in this thread. The deadline of collecting questions is Jan 7. Let's make full use OF this chance to exchange ideas and explore THE latest research developments in the area of economics.

Thanks

（This article may not be copied, imitated or used, in whole or in part, without express prior written permission of Anny.）  

人大经济论坛荣幸地邀请到厦门大学王亚南经济研究院院长、世界顶尖经济学家洪永淼教授访问论坛。中国人民大学经济学院杨瑞龙院长非常重视本活动,在第十二楼致词:“热烈欢迎洪永淼教授光临人大经济论坛，洪永淼教授在百忙之中仍抽空指导经济学子，并与论坛经济学爱好者进行在线交流，在此，我谨代表中国人民大学经济学院对洪永淼教授的光临表示欢迎和感谢，预祝此次访谈获得圆满成功！”。

洪永淼教授生于1964年，1985年毕业于厦门大学物理系，1986~1988年就读于中国人民大学“经济学培训中心”和厦门大学经济。1988年在厦大取得硕士学位后，在美国加州大学圣地牙哥分校师从2003年诺贝尔经济学奖获得者克莱夫.格兰杰（Clive　Granger）爵士和赫柏特.怀特（Halbert　White）教授。   

现在，洪永淼教授是一位享誉海内外的计量经济学家。他对计量经济学理论、非参数计量经济学、非线性时间序列分析、金融计量经济学、中国经济和金融市场实证研究等领域有独到研究。在他杰出的学术生涯中，他广泛地在Econometrica、Econometric Theory、 Journal of Econometrics、Journal of Political Economy、Journal of Quarter Economics、Review of Economic Studies、Review of Financial Studies和Review of Economics and Statistics等国际权威期刊上发表研究成果。国际权威计量经济学期刊Econometric Theory根据在16种一流期刊发表计量经济学学术成果的数量，对全世界计量经济学家进行排名，洪永淼教授在(1995年~)位居“世界计量经济学家排行榜”前10名。https://bbs.pinggu.org/thread-278869-1-1.html&page=1
  

鉴于洪永淼教授出众的能力，他被委任为美国康奈尔大学终身教授、厦门大学“长江学者”讲座教授，并且获得国家自然科学基金海外杰出青年科学基金。

 除了令世界瞩目的学术研究成果，洪永淼教授对经济学教育事业也做出了不可磨灭的贡献， 受到广泛的赞扬和爱戴。洪永淼教授倾心教导学生。 对于学生们，洪永淼教授不仅才华横溢， 还和蔼可亲、给予无私帮助、以及激发无穷动力。 他是天资和人格完美结合的典范。

洪教授对世界经济学理论发展和经济学教育产生了卓越影响。更多详情请参见洪教授个人主页:www.wise.xmu.edu.cn/viewNews.asp?id=442  

洪永淼教授在百忙之中抽空光临论坛, 与大家分享他对经济学的洞察。欢迎读者在本贴提问，同洪永淼教授交流。本次征集问题1月7日截止。望大家充分利用这个机会, 交换学术心得，探讨前沿动态。

谢谢！

*****************************

洪永淼教授部分著作及报道

I. 洪永淼教授在人大授课
经典经济计量方法及其应用(一)

经典经济计量方法及其应用(二)

经典经济计量方法及其应用(三)


(China Managerial Labor Market)

II. 洪永淼教授精心育人、深受爱戴
洪永淼教授为漳州校区学子讲授“大学生的人生规划”
洪永淼教授归国开讲
倾力打造一流经济学家的摇篮_厦门大学校报）
王亚南经济研究院国际化办学的探索和实践
中国经济学教育与研究必须国际化（光明日报）

III. 媒体报道
2005中国经济学年会采访洪永淼教授
止于至善：修身育人的完美境界
中国经济学教育科研网论坛洪永淼教授访谈录

IV. 洪永淼教授计量经济理论的研究摘录
论中国计量经济学教学与研究
Understanding Modern Econometrics

V. 洪永淼教授非参数计量经济学的研究摘录
Nonparametric Specification Tests of Discrete Time Spot Interest Rate Models in China
Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence
Consistent Specification Testing Via Nonparametric Series Regression
Nonparametric Specification Testing for Continuous-Time Models with Applications to Term Structure of Interest Rates

VI. 洪永淼教授非线性时间序列分析的研究摘录
A Test for Volatility Spillover with Application to Exchange Rates
Can The Random Walk Model be Beaten in Out-Of-Sample Density Forecasts Evidence from Intraday Foreign Exchange Rates
Consistent Testing for Serial Correlation of Unknown Form
Diagnostic Checking for the Adequacy of Nonlinear Time Series Models
Generalized Spectral Tests for Conditional Mean Models in Time Series with Conditional Heteroscedasticity of Unknown Form
Hypothesis testing in Time Series via the Empirical Characteristic Function a Generalized Spectral Density Approach
Inference on Via Generalized Spectrum and Nonlinear Time Series Models
Serial Correlation and Serial Dependence
Validating forecasts of the joint probability density of bond yields Can affine models beat random walk
Wavelet-Based Testing for Serial Correlation of Unknown Form in Panel Models

VII. 洪永淼教授金融计量的研究摘录
Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation
Model-Free Evaluation of Directional Predictability in Foreign Exchange Markets
金融计量的新近发展

VIII. 洪永淼教授关于中国经济和金融市场的实证研究摘录
Autonomy and Incentives in Chinese State Enterprises
China’s Evolving Managerial Labor Market
中国股市与世界其他股市之间的大风险溢出效应
中国市场利率动态研究
中国股市是弱式有效的吗
建立中国微观数据库有助于更有效的政府决策

[此贴子已经被作者于2008-4-15 14:31:23编辑过]

II. Keynote Address by Professor Yongmiao Hong

Yongmiao Hong:Good afternoon, everyone!

It is a great honor to be invited to attend this online forum on RenDa BBS, sharing views on econometrics and economics education in China. First of all, I’d like to thank RenDa BBS and the managers of this forum to provide such an excellent opportunity. In what follows, I will talk about some recent developments in econometrics, particularly time series econometrics. Then I will introduce financial econometrics, a relatively new field emerging from the application of econometrics to finance. I will discuss econometrics education and research in China, also and internationalization of Chinese economics.

Before I discuss the aforementioned issues, let me first introduce my academic career and research. I was born in a village near Xiamen Island where I received my primary and high school education. Because my physics exam earned the highest score among all subjects in my national college entrance examination, I chose Physics as a major at Xiamen University (XMU). This choice was not rational from today’s point of view (except for maximizing the chance to be admitted), because I had no idea about my research and career interest in the future. I received a BA in Physics in 1985, and then was admitted to the MS program in Physics at XMU. I completed all required core MS courses in Physics, I found that Physics was not my interest, and so transferred in 1986 to Economics Training Center at RenDa, which was initiated by Professor Gregory Chow and sponsored by Ford Foundation. I have been most grateful to Professor Chow and Professor Gejia Shu, the first dean of School of Economics at XMU, who allowed XMU students in science majors to apply for the Ford program. I’m also most grateful to RenDa, where I started my economic career.

During one year study at RenDa, I learnt intermediate macroeconomics, intermediate microeconomics, public economics, law and economics, development economics, decision making under uncertainty, taught by American professors, and socialist economy, taught by RenDa professors. One year later, I went back to XMU to write my MA thesis, which compared Konai’s Economics of Shortage (a theory for a socialist economy) and the disequilibrium model in a capitalist market economy (e.g., Benassy). Upon earning an MA in Economics in 1988, I was admitted to the PHD program in Department of Economics, University of California at San Diego (UCSD), majoring in econometric theory. During this period, I also did an empirical panel data project on economic reforms of Chinese state owned enterprises with professors at UCSD. I learnt a great deal about the application of econometrics and Chinese economy by dealing with real data. After receiving a PHD in 1993, I became an assistant professor in Department of Economics at Cornell, a tenured associate professor in 1998, and a full professor in 2001.

My doctoral dissertation was on model specification testing using nonparametric series regressions in a cross-sectional context. The basic idea is to compare the sums of squared residuals between a parametric regression model and a nonparametric series regression model. The sum of squared residuals of the former is close to that of the latter when the former is correctly specified, but becomes larger when it is misspecified. This is essentially a generalized F-test with a large number of degrees of freedom. I derived the asymptotic distribution of the proposed test statistics.

At Cornell, I attempted to apply the nonparametric method to a time series context, and I found that it has been used to estimate the spectral density function of a time series. The spectral density function and the autocovariance function are two basic analytic tools in time series analysis. I developed tests for serial correlation of unknown form in the residuals of a time series regression model. The idea is to use the nonparametric method to estimate the spectral density function of an estimated residual series and checks whether it looks like a “flat spectrum”. If there exists no serial correlation (i.e., the residuals are a white noise process), the spectral density function is a constant (so is flat) as a function of frequency.

In working on testing serial correlation, I received a critism that the spectral density function cannot capture nonlinear serial dependence such as ARCH effects (i.e., positive autocorrelations in squared residuals). Nonlinear dependence is an important feature in macroeconomies and financial markets. In response to this critism, I came up with a solution. The basic idea is to first transform the original time series data via a nonlinear function and then inspect the spectral density of the transformed time series. This new approach can capture nonlinear serial dependence such as ARCH effects, and it is termed as the generalized spectral analysis.

Because subtle nonlinear dependent features often occur in financial markets, a natural application of nonparametric methods and generalized spectrum is to financial markets and financial time series data. Thus, I became interested in financial econometrics. I developed econometric tests for market efficiency, Granger causality in risk, and continuous-time models. I also did some work in empirical finance, including return predictability in stock markets and foreign exchange markets, and interest rate term structure dynamics.

In 2002, I was appointed as a special-term professor in Department of Economics, School of Economics and Management, Tsinghua University. Since 2005, I have been helping running Wang Yanan Institute for Studies in Economics (WISE) at XMU. My involvement in Tsinghua University and particularly in XMU has crowded out much of my academic research time, but it is a very interesting and valuable experience as I can get a better understanding of Chinese economics education and research. I’d be happy to share with you my observations and ideas on Chinese economics education and research, particularly econometrics education and research.

II. Keynote Address by Professor Yongmiao Hong

Yongmiao Hong:   Good afternoon, everyone!

It is a great honor to be invited to attend this online forum on RenDa BBS, sharing views on econometrics and economics education in China. First of all, I’d like to thank RenDa BBS and the managers of this forum to provide such an excellent opportunity. In what follows, I will talk about some recent developments in econometrics, particularly time series econometrics. Then I will introduce financial econometrics, a relatively new field emerging from the application of econometrics to finance. I will discuss econometrics education and research in China, also and internationalization of Chinese economics.

Before I discuss the aforementioned issues, let me first introduce my academic career and research. I was born in a village near Xiamen Island where I received my primary and high school education. Because my physics exam earned the highest score among all subjects in my national college entrance examination, I chose Physics as a major at Xiamen University (XMU). This choice was not rational from today’s point of view (except for maximizing the chance to be admitted), because I had no idea about my research and career interest in the future. I received a BA in Physics in 1985, and then was admitted to the MS program in Physics at XMU. I completed all required core MS courses in Physics, I found that Physics was not my interest, and so transferred in 1986 to Economics Training Center at RenDa, which was initiated by Professor Gregory Chow and sponsored by Ford Foundation. I have been most grateful to Professor Chow and Professor Gejia Shu, the first dean of School of Economics at XMU, who allowed XMU students in science majors to apply for the Ford program. I’m also most grateful to RenDa, where I started my economic career.

During one year study at RenDa, I learnt intermediate macroeconomics, intermediate microeconomics, public economics, law and economics, development economics, decision making under uncertainty, taught by American professors, and socialist economy, taught by RenDa professors. One year later, I went back to XMU to write my MA thesis, which compared Konai’s Economics of Shortage (a theory for a socialist economy) and the disequilibrium model in a capitalist market economy (e.g., Benassy). Upon earning an MA in Economics in 1988, I was admitted to the PHD program in Department of Economics, University of California at San Diego (UCSD), majoring in econometric theory. During this period, I also did an empirical panel data project on economic reforms of Chinese state owned enterprises with professors at UCSD. I learnt a great deal about the application of econometrics and Chinese economy by dealing with real data. After receiving a PHD in 1993, I became an assistant professor in Department of Economics at Cornell, a tenured associate professor in 1998, and a full professor in 2001.

My doctoral dissertation was on model specification testing using nonparametric series regressions in a cross-sectional context. The basic idea is to compare the sums of squared residuals between a parametric regression model and a nonparametric series regression model. The sum of squared residuals of the former is close to that of the latter when the former is correctly specified, but becomes larger when it is misspecified. This is essentially a generalized F-test with a large number of degrees of freedom. I derived the asymptotic distribution of the proposed test statistics.

At Cornell, I attempted to apply the nonparametric method to a time series context, and I found that it has been used to estimate the spectral density function of a time series. The spectral density function and the autocovariance function are two basic analytic tools in time series analysis. I developed tests for serial correlation of unknown form in the residuals of a time series regression model. The idea is to use the nonparametric method to estimate the spectral density function of an estimated residual series and checks whether it looks like a “flat spectrum”. If there exists no serial correlation (i.e., the residuals are a white noise process), the spectral density function is a constant (so is flat) as a function of frequency.

In working on testing serial correlation, I received a critism that the spectral density function cannot capture nonlinear serial dependence such as ARCH effects (i.e., positive autocorrelations in squared residuals). Nonlinear dependence is an important feature in macroeconomies and financial markets. In response to this critism, I came up with a solution. The basic idea is to first transform the original time series data via a nonlinear function and then inspect the spectral density of the transformed time series. This new approach can capture nonlinear serial dependence such as ARCH effects, and it is termed as the generalized spectral analysis.

Because subtle nonlinear dependent features often occur in financial markets, a natural application of nonparametric methods and generalized spectrum is to financial markets and financial time series data. Thus, I became interested in financial econometrics. I developed econometric tests for market efficiency, Granger causality in risk, and continuous-time models. I also did some work in empirical finance, including return predictability in stock markets and foreign exchange markets, and interest rate term structure dynamics.

In 2002, I was appointed as a special-term professor in Department of Economics, School of Economics and Management, Tsinghua University. Since 2005, I have been helping running Wang Yanan Institute for Studies in Economics (WISE) at XMU. My involvement in Tsinghua University and particularly in XMU has crowded out much of my academic research time, but it is a very interesting and valuable experience as I can get a better understanding of Chinese economics education and research. I’d be happy to share with you my observations and ideas on Chinese economics education and research, particularly econometrics education and research.
    

[此贴子已经被cymbidium于2008-1-30 0:47:34编辑过]

III. Frontier Issues on Econometrics(Time Series)

Anny: Many of us who are participating today’s academic activity are econometrics faculty and students or those who are interested in econometrics. Could you talk about the history and frontier research in econometrics, especially in time series econometrics which you have been working on?

[此贴子已经被作者于2008-1-30 0:51:21编辑过]

Yongmiao Hong: Simply put, econometrics is on methodologies for rigorous empirical studies in economics. It provides a bridge between economic theories or modeling and economic reality (economic data). Econometrics has been widely used in almost all fields in economics, testing validity of economic theory and hypotheses, explaining historical economic phenomena, forecasting future economic trends, and making recommendations for decision-makers.

Econometrics consists of many fields, such as time series econometrics (sometime but not often called macroeconometrics), microeconometrics, panel data econometrics, nonparametric econometrics, financial econometrics, spatial econometrics, and etc. Over the past several decades, econometrics, both in theory and practice, has been advancing rapidly, due to the demand of methodologies for empirical studies, increasing availability of economic data, and progress in computing technology. Due to my limited knowledge, it is impossible for me to introduce recent developments in every field of econometrics. Instead, I would like to focus on some recent developments in time series econometrics which I’m more familiar with.

Time series analysis in statistics has had a very long history, dating back at least to 1920s. Earlier developments in time series analysis were marked with theory and applications of Autoregressive Moving-Average (ARMA) Modeling, often with the assumption of identically and independently distributed (i.i.d.) or i.i.d. normal innovations, which is suitable for modeling the dynamics of a weakly stationary time series. A multivariate version, Vector ARMA or VARMA, can be used to investigate the dynamics of multivariate stationary time series, including lead and lag relationships across individual time series, which are closely related to the well-known notion of Granger causality. Statistical theory for ARMA or VARMA, which is essential for making statistical inference for time series data, has been available for long time. Applied econometricians often prefer AR or VAR modeling, perhaps in combined with some exogenous variables, due to their simplicity in estimation and inference.

However, most macroeconomic time series appears to be trending over time, which violates the stationarity assumption. Statistical theory for stationary ARMA or VARMA models is not applicable to trending macroeconomic time series. It is important to distinguish whether a trending time series has a deterministic trend or stochastically trend, which has important implications on econometric theory and economic interpretations. Nelson and Plosser (1982) documented that most trending macroeconomic time series are unit root processes. Motivated by this study, econometricians, particularly time series econometricians, started to develop statistical theory for unit root processes, and their multivariate versions, cointegrated systems, among other things. The statistical theory for nonstationary time series is quite different from that for stationary time series. This was an exciting and important development in time series econometrics in 1980s and 1990s.

ARMA and VARMA are linear time series models. They are unable to capture nonlinear features in economic time series, such as asymmetric business cycles in U.S. GDP growth rates (i.e., the expansion period lasted longer than the recession period). To account for such features, nonlinear time series models have been introduced to time series econometrics over the last two decades. Among them, the most successful applications are Markov Chain Regime-Switching Models and threshold autoregressive models. Nonlinear analytic tools, including nonparametric methods, have been also increasingly used and developed. An example along this avenue is testing and measuring for nonlinear serial dependence using various methods, including, for example, chaos theory, nonparametric methods, and higher order spectra or generalized spectra.

In the early stage, most time series analysis has focused on modeling and forecasting for the conditional mean dynamics of a time series. Often, the innovations are assumed to be i.i.d., but this appears inconsistent with the empirical styled fact that most economic and financial time series or their residuals are serially uncorrelated but not serially independent. There may exist some dynamic structure in the higher order conditional moments of economic time series. In the early 1980s, a new class of time series models emerged to model and forecast the dynamics of the conditional variance (or volatility) of a time series. This class of models is called Autoregressive Conditional Heteroskedastic (ARCH) models, and its various linear and nonlinear generalizations (e.g., GARCH models). The emergence of this class of models is closely associated with the remarkably increased uncertainty in economic reality since 1970s, particularly after the oil crises in 1973, the shift to floating exchange rate systems, and the U.S. high interest rate policy during 1979-1982. The increased uncertainty in economy calls for the need to quantify the magnitude of uncertainty and its impact on decision-making of economic agents.

Various multivariate versions of ARCH or GARCH models have received attention for a long time, but their estimation often encountered some difficulty in practice. Recently there has been a renewed interest in multivariate volatility modeling, relaxing the constant conditional correlation assumption and allowing for various forms of dynamic conditional correlations between various individual economic time series.

Forecasts for the conditional mean of a time series are usually called point forecasts. When economic agents make a decision under uncertainty, point forecasts are often insufficient. Instead, there is a need for modeling and forecasting the entire probability distribution of an economic time series. In fact, probability density forecasts have existed for a long time in practice. An example is the Professional Forecasts Survey by Fed, U.S.A., which asks some professional forecasters to predict the probability distributions of GDP growth and inflation. Research along this line has been a major development in time series econometrics over the past several years.

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IV. Application of Econometrics on other discipline (Financial econometrics)

Anny: We know that econometrics has been widely used in almost every field in economics. Can you talk how econometrics can be applied to other disciplines in economics? For example, how can time series econometrics be applied to finance?

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Yongmiao Hong:  Financial econometrics is a new field developed over the past two and half decades. It is a hybrid between (time series) econometrics and finance, providing suitable econometric tools to analyze financial data, which help deepen understanding of modern financial markets.

A central issue in finance is to measure financial risk (due to existence of uncertainty and risk aversion of economic agents) and its impact on the decision-making of economic agents. The best mathematical tool to characterize uncertainty is probability theory, and so it is very natural to use the statistical tools to model and forecast uncertainty and financial risk. However, financial econometrics is not a simple application of time series econometrics to financial data. It has its unique individual character for two main reasons. First, financial time series data, particularly high-frequency financial time series data, have distinguished features different from those of time series data in other fields. For example, high-frequency financial returns are often serially uncorrelated but its squares are significantly positively correlated (volatility clustering). They are non-normally distributed because of the existence of heavy tails in their distribution. Second, finance often addresses problems which require modeling or forecasting not only conditional mean dynamics but also other features of the return distribution or the distribution itself. For example, model risk management essentially involves various forms of modeling and forecasting asset return distributions.

Financial econometrics at the early stage mainly involves least-squares regression and maximum likelihood estimation (MLE) techniques. Return predictability, efficient market tests, and tests of portfolio models such as CAPM and APT were essentially implemented with least squares on carefully manipulated data.

Over the past more than two decades, financial econometrics has advanced very rapidly, both in theory and empirical studies. Model asset pricing theories, as characterized by the Euler equation, have implications on a nonlinear conditional moment of the underlying asset price. Least squares and MLE are not suitable here. Generalized method of moments (GMM) has been proposed to estimate various asset pricing models.

Uncertainty is a key financial instrument. Since early 1980s, ARCH models and its various generalizations or extensions, such as GARCH and nonlinear ARCH models, have been proposed to characterize and forecast volatility dynamics of asset prices. At the same time, stochastic volatility (SV) models, which unlike ARCH models, assume that volatility is a latent (i.e., unobservable) process, have been also proposed. These volatility models are widely used to study persistent volatility clustering, spillovers and linkages across assets and markets, asymmetries and leverage effects in volatilities, and derivatives pricing. A popular method called Quasi-MLE, which does not require a correctly specified likelihood function, is proposed to estimate various ARCH models with correct robust standard error formula. SV models are rather challenging to estimate; various estimation methods such as MCMC methods have been proposed.
 
Higher conditional moments, tail distribution or even the entire conditional distribution modeling have received increasing attention. These models are very useful tools for financial risk management, hedging, and derivatives pricing. For example, Value at Risk calculation requires the knowledge of the tail probability distribution of asset returns or portfolios. Distribution modeling includes physical probability distribution and risk neutral probability distribution modeling, which contain risk information about the stochastic discount factor of market participants.

Because of the mathematical elegance of stochastic calculus and the continuous flow of information into financial markets, continuous-time models have been widely used to characterize the dynamics of interest rates, stock prices, and foreign exchange rates. These models characterize the conditional distributions of underlying economic time series but their conditional probability distributions usually do not have a closed form expression. The estimation of these continuous-time models with discretely sampled data are thus challenging. As an important development, various estimation methods for continuous-time models have been invented over the past decade or so.

High-frequency or tick-by-tick data have recently become available for a range of different financial instruments and markets. To analyze these data, new econometric tools have emerged. Examples include mode-free realized volatility estimation and forecasts, and Autoregressive Conditional Duration (ACD) models for irregularly spaced data, which can be used to investigate market microstructure and trading behaviors among other things.

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