This course is for all the students who are pursuing master degree in School of Economics, Nankai University.
About Course 教学内容
Course Outline 参考资料
Textbook & Reference 评价考核方式
Associate Professor, Institute of Statistics and Econometrics, School of Economics, Nankai University, No94, Weijin Road, Tianjin city, P.R. China.
University College Dublin, Dublin, Ireland, January 2001- November 2004
Degree: PhD in Economics
Thesis: “The Effect of Productivity on Inflation and Unemployment”
Research keywords: Monetary Policy, Productivity, Wage Aspiration, Phillips Curve, Panel Data, Structural VAR Model, Co-integrated VAR.
Supervisor: Dr. Vincent Hogan
University College Dublin, Dublin, Ireland, 1999 – 2000
Degree: Master of Arts in Economic Science
Thesis: “Possibility and Limitations of Monetary Policy in a Low Inflation Environment”
Supervisor: Prof. Brendan Walsh
Main Course of Study: Macro, Micro, Econometrics, Labor Economics.
Marks: Second Class Honor, grade I
University College Dublin, Dublin, Ireland, 1998-1999
Degree: Postgraduate Diploma in Economic Science
Main Course of Study: Macro, Micro, Econometrics, International Trade.
Marks: First Class Honor
Hebei University, Baoding City, Hebei Province, P.R.China, 1993-1997
Degree: Bachelor of Arts in International Economics
Main Course of Study: Economics, International Economic, International Trade, Finance, Marketing.
Teaching Assistant in Department of Economics, University College Dublin from 2001 to 2004. Duties include giving econometric tutorials for third year and master class, supervising econometric labs, teaching statistical computer software TSP and STATA, correcting exam papers.
Associate Professor in School of Economics, Nankai University from 2005 up to now, teaching Applied Statistics for second year students, Basic Econometric for third year students, Intermediate Econometric I for Master students and Applied Time Series Econometrics for PhD students.
Recent Working Papers:
l < Productivity Growth and Inflation: A Multi-Country Study>
l < Dynamic Effect of Productivity and Wage Aspirations in the Phillips
l < Productivity and the Stability of the AS-AD Structural VAR Model>
l < The Threshold Effects of Productivity on the Phillips Curve>
l <Measuring the Potential GDP Growth rate of China Using Structural VAR
l 《Measuring the NAIRU — A structural VAR approach》published at Frontiers of Economics in China, first author，Volume 6, issue 1 (2011) P76-91.
l 《The Medium and Long Term Forecast of China’s Vehicle Stock per 1000 Person Based on the Gompertz Model》 published at Journal of Industrial technological Economics, first author, issue 225 (2012) P7-24.
l 《The Study of Influencing Factors for the Demand of Property Insurance in China -- Based on the Regional-weighted And Time-weighted Panel Data》published at Insurance Studies, first author, issue 298 (Feb, 2013) P38 – 45.
l 《Vehicle Ownership Analysis Based on GDP per Capita in China: 1963–2050》，Sustainability, the second author， number 6, 2014, P4877-489.
l 《The Collaborative Effect Between Wage Level, Wage Structure and Firms Profit: An Empirical Study Based on the Survey Data of 923 Enterprises in Tianjin from 2008 to 2010》publised at Studies in Labor Economics, the second author, number 6, volume 2, 2014, P126-151.
Recent research projects:
l 《The Application of Structural VAR Model in the Study of GDP Growth Rate in China 》, supported by the Fundamental Research Funds for the Central Universities of People’s Republic of China.
l 《Forecasting China’s Vehicle Stock》，funded by China Automotive Technology Research Centre.
l 《Research on the Relationship Between Economic Variables and Vehicle Sales 》, funded by China Automotive Technology Research Centre.
This course is for all the students who are pursuing master degree in School of Economics, Nankai University. It is a required course and it aims to introduce students the linear regression models, the limited dependent variable models, the generalized moment method and some time series models. The students should, at the end of the course, understand the principles underlying these econometric models and be familiar with the practical application of the models using statistical packages. The students finally should be able to extend these basic econometric models and select the suitable econometric models to analyze the specified regional economic phenomena.
This course will cover classical econometric theories, both theoretically and practically. For example, linear and nonlinear regression models, ordinary least square and maximum likelihood method, generalized moment method, simultaneous equations model, probit model, logit model and Tobit model and time series models.
This course takes 48 hours and students who can finish it obtain 3 credits.
Students should be confident that they have a working understanding of basic statistical knowledge such as hypothesis testing, probability. This course will have amount of computer exercises. Some experiences of implementing the computation packages will be of great advantage.
|1||Introduction||Introducing the definition and basic knowledge of econometrics and the reference textbooks, Eviews software, etc|
|2||Introduction to multiple regression model||Using matrix form to introduce the assumptions of OLS methods, OLS criterion, OLS estimator, the properties of OLS estimator, R2, significance test, t test, F test, single test for the restrictions of coefficient, joint test for the overall significance of coefficients, confidence interval, forecast. Eviews case study.||3 hours|
|3||Explanations and comparison of different regression models||Learning how to explain regression models and learning model misspecification (irrelevant variable model and omitted variable model), regressors selection (AIC criterion, BIC criterion), log form models, the functional form test (RESET test), structural break test (chow test). Case study.||3 hours|
|4||Asymptotic properties||Studying how to derive consistency, asymptotic normality of the OLS estimator. Understanding the assumptions behind the deriving process. Knowing how to use asymptotic properties to do the hypothesis test.||3 hours|
|5||Computer lab (1)||Setting up a data file, understanding the functions of dealing with the sample data, multiple regression model, knowing how to investigate residual, draw scatter plot, do economic forecast and transform the data.||3 hours|
|6||Maximum likelihood method||Understanding the basic definition of the ML method. Knowing the statistical properties of the likelihood function. Being able to do Wald test, Likelihood Ratio test and LM test.||4 hours|
|7||Heteroscedasticity and autocorrelation||Introducing robust standard errors, generalized least square method, correction methods for heteroscedasticity and autocorrelation, feasible generalized least square, comparison of FGLS and OLS, Eviews case study.||2 hours|
|8||Limited dependent variables model(1)||Learning the definition of binary choice models, how to estimate the models, how to explain the coefficients and how to justify the estimation results.||3 hours|
|9||Limited dependent variables model (II)||Introducing standard Tobit model and the extension of Tobit model. Giving the examples about how to set up Tobit model and how to explain the model.||3 hours|
|9||Single equation GMM||Introducing endogeneity bias and some examples, the general formulation and identification method, method of moment and generalized method of moment.||3 hours|
|10||Computer lab (2)||Eviews conduction of limited dependent models and GMM method.||3 hours|
|11||Simultaneous equation model||Definitions, simultaneous equation bias, examples of simultaneous model, identification (order condition and rank condition), estimation (instrument variable, two stage least square), forecast. Eviews case study.||6 hours|
|12||Time series model||Definition of time series, two basic random processes, four types of ARIMA(p,d,q) model, autocorrelation function, partial autocorrelation function, three steps of setting up a time series model (identifying, estimating, diagnostic checking), selecting the model using information criterion. Eview case study.||6 hours|
|13||Review and exam||Course review, answering the questions (2 hours), exam (3hours)||6 hours|
1.by Xiaotong Zhang，China Machine Press.
2.by Wooldridge. Thomson south-western
3.by Gujarati. McGraw-Hill
4.by Walter and Enders. Wiley.
5. by Verbeek. Wiley.
6.by Hayashi. Prenceton University Press.
7.by Cameron. Cambridge.
8.by Greene. Prentice-Hall.