经济学精品课

课程视频简介

This course is for all the students who are pursuing master degree in School of Economics, Nankai University.

主讲人: Hongmei Zhao

Associate Professor, Institute of Statistics and Econometrics, School of Economics, Nankai University, No94, Weijin Road, Tianjin city, P.R. China.

Tel: 0086-22-23503232

Mobile: 18902031012            

Email: hongmeizhao@nankai.edu.cn 


Education:

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.

Marks: 80%

 

Work Experience: 

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

Curve> 

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 

Approach> 

Recent Publications: 

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.

Prerequisites

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.



chaptercontentsClass time
1IntroductionIntroducing the definition and basic     knowledge of econometrics and the reference textbooks, Eviews   software, etc
2Introduction to multiple regression modelUsing 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
3Explanations and comparison of different     regression modelsLearning 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
4Asymptotic propertiesStudying 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
5Computer 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
6Maximum likelihood methodUnderstanding 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
7Heteroscedasticity and autocorrelationIntroducing 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
8Limited 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
9Limited 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
9Single equation GMMIntroducing endogeneity bias and some     examples, the general formulation and identification method, method of   moment   and generalized method of moment.3 hours
10Computer lab (2)Eviews conduction of limited dependent     models and GMM method.3 hours
11Simultaneous equation modelDefinitions, 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
12Time series modelDefinition 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
13Review and examCourse 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.

9.  by Kennedy, The MIT Press, Cambridge

10.by Maddala, Prentice Hall.


Computer exercises: 30%

Final exam: 70%