（更新：2007年10月29日）

 

This course provides a mix of theory and applications of macroeconomics. Topics range from the classical economics, Keynesian economics and other neo-classical models such as overlapping generation models, dynamic optimization, real business cycle theory, inter-temporal open economy models and the theory of economic growth. Various theories will be illustrated using examples drawn from local and international policy issues, as appropriate.

The purpose of this course is to teach the basic principles of micro theory which lie at the core of modern argumentation of economics. Hence this is very much like learning a language. By the end of the course, you should have exposed to most standard techniques in partial-equilibrium analysis, including fundamental theories to analyze firm and consumer behaviors. The main textbook used will be Varian, Microeconomic Analysis, 3rd Edition.

This course is an introduction to basic probability and statistical theory. Topics covered in the course include random variable, conditional expectation, modes of convergence, weak law of large numbers, central limit theorems, hypothesis testing and maximum likelihood theory. This course is designed for both M.A. and PhD students whose research area is econometrics or applied econometrics. Calculus and linear algebra are prerequisites. Textbooks include Hong, Y. (2006) Lecture Notes for Probability and Statistics, Bierens, H. (2004) Introduction to the Mathematical and Statistical Foundations of Econometrics, and Gallant, A.R. (1997) An Introduction to Econometric Theory.

 

Intensive study of mathematic methods and applications widely used in economics and related fields is undertaken. This course is designed to equip students with essential tools and techniques in application to the fundamental studies of macroeconomics, microeconomics and econometrics. It serves as a bridge course, which combines contemporary mathematic models with economics theory. The focus is on the training of analytical skills in economics research, enabling students to comprehend academic articles in international top journals. Furthermore, students are expected to master essential mathematics methods and tools and apply them in economics research after the completion of the course.

 

 

 

This course is designed for first-year students in the master program. Topics covered mainly include an introduction to Theory of Rational Expectation, its standing in the development of modern Macroeconomics as well as some practical applications. Prerequisites for well understanding the details involve Multivariate Calculus, Static Optimization and Intermediate Macroeconomics. The very core of modern Macroeconomics is the dynamic stochastic general equilibrium model grounding on the microeconomic foundations; as a consequence Dynamic Optimization is indispensable analysis tool in this area. Therefore, theory of Dynamic Optimization will be firstly introduced, then basing on this footing we will jump into the details of Growth Theory, Real-Business-Cycle Theory, Inflation and Monetary Policy, Budget Deficits and Fiscal Policy, Unemployment Theory, etc.

This is a core course designed to provide students with the current tools of microeconomic analysis, and is a natural continuation of advanced microeconomics (I). While, in that course, students should have learnt about the classical theory of choice and perfectly competitive markets, here students will study more recent developments --- the analysis of strategic interaction, problems involving information and incentives, the functioning of imperfectly competitive markets, etc. The tools students will pick up are now being extensively used in a wide variety of fields, such as labor economics, industrial organization, public finance, development, and even macroeconomics. What students learn here will form much of basic repertoire as a professional economist in the future!

 

This course is the continuation of Probability and Statistic Theory offered last semester. The course begins with an introduction of the classical linear regression (CLR) models, and then relaxes assumptions gradually. Besides CLR models, this course covers linear regression models with I.I.D. observations, linear regression models with dependent observations, linear regression models with HAC disturbances, instrumental variables regression, GMM and MLE. This course also touches several frontier topics, for example, nonparametric econometrics and model selections et al. This course aims to provide solid econometric foundation for both theorists and empirical economists.

 

This course is to provide the economics foundation of modern asset pricing theory. It serves as an introduction to the functioning of financial market as an efficient venue for organizing investment activities. Various issues on risk measurement, risk assessment, managing risk, investors’ psychological attitudes towards risk, and its implications on consumption and portfolio decision making in an uncertain world will be introduced and discussed. The classical Sharpe-Lintner CAPM, Markowitz’s mean-variance analysis, Fama’s efficient market hypotheses, and no-arbitrage asset pricing theory as corner stones of modern finance will be received in-depth treatment.

 

By taking this one semester course, students are expected to learn some basic computer and network knowledge and master SAS programming skills, and they can apply these tools to do some empirical economic research independently. Topics to be introduced are: 1. some most useful computer and network skills can enable students to solve problems that they may face when doing research; 2. SAS base module is very important and basic knowledge to master other modules. 3. SAS statistic module provides many PROCs to do some statistic analysis. 4. SAS macro skills can be used to make your program shorter and easier to read; 5. SAS ETS module focuses on econometrics and time series. 6. PROC SQL helps students to control the data for their uses. 7. SAS IML, the interactive matrix language, by which students can program some new statistic test methods or do some Monte Carlo simulation, especially important to the students whose major is Econometrics. 8. Case I and Case II, after learning these two complicated and synthesized cases, students can reuse all the knowledge they learnt from this course and can also learn some methods of how to do some empirical study.

 

The core material deals with labor supply decisions made by rational households, labor demand decisions made by profit-maximizing firms, and the equilibrium wage differentials and employment patterns implied by these decisions when markets are competitive. Applications include the analysis of industry wage differentials, life-cycle age-earnings profiles, and returns to human capital investments. The last part of the course considers various ways in which labor markets may differ from the competitive ideal. Topics include efficiency wages and other incentive schemes, discrimination, bargaining between workers and employers to divide monopoly rents, and unemployment.

 

This course is an introduction to panel data econometrics. Panel data provides multiple observations over time for a number of cross-section units. Topics range from econometric analysis of fixed effect models, random effect models and dynamic panels. The course is designed for both M.A. and PhD students whose research area is econometrics or applied econometrics. Probability and Statistical Theory, Advanced Econometrics (I) and (II) are prerequisites. Textbooks include Hsiao, C. (2003) Analysis of Panel Data, Baltagi, B. (2004) Econometric Analysis of Panel Data, and Wooldridge, J (2001), Econometric Analysis of Cross Section and Panel Data.

The course is divided into five parts. The long-term investment decision is covered first. Financing decisions and working capital are covered next. Finally a series of special topics are covered. Here are the five parts: Part I describes how investment opportunities are valued in financial markets. The most important concept in Part I is net present value. We develop the net present value rule into a tool for valuing investment alternatives. Part II introduces basic measures of risk. The capital-asset pricing model (CAPM) and the arbitrage pricing theory (APT) are used to devise methods for incorporating risk in valuation. We use the above pricing models to handle capital budgeting under risk. Part III examines two interrelated topics: capital structure and dividend policy. Capital structure is the extent to which the firm relies on debt. It cannot be separated from the amount of cash dividends the firm decides to pay out to its equity shareholders.Part IV concerns long-term financing. We describe the securities that corporations issue to raise cash, as well as the mechanics of offering securities for a public sale. Here we discuss call provisions, warrants, convertibles, and leasing.Part V discusses options.Part VI covers mergers.In addition, students are required to read and present critically some classical papers on corporate finance. These papers are from top journals in finance, such as JOF, JFE and JFQA. You can get them through JSTOR.

 

The course will cover the statistical and econometric techniques needed to conduct quantitative research in finance.  Topics include estimation of CAPM, option pricing, continuous time process, term structure, VaR, CVaR and credit risk.  Emphasis is on understanding and interpreting empirical findings in a range of financial markets, from viewpoints of academics as well as practitioners.

 

The course covers the main topics in financial engineering which include:(1) Introduction of derivatives: Forward, Futures, Options, Swap;(2) Pricing of derivatives: models, closed-form pricing formulas, numerical methods;(3) Hedge and Risk Management: Greek Letters, VAR;(4) Term Structure of Interest Rate and Bond Pricing;(5)Exotic Derivatives.

 

 

 

This course examines parametric time series models for analyzing stationary and non-stationary data. Emphasis is on drawing economic interpretations with time series data. The objective of this course is twofold. One is introducing econometric tools for modeling economic and financial time series. The other is providing solid foundation on the econometric theory of time series models.

 

This course is intended to bring students to the frontier of applied econometrics using labour economics as the main platform. The underlying theoretical issues are mostly microeconomic aspects of the labour market. We will go through a list of important papers in the field of applied labour most of the term (one or two papers each week). Students’ active participation in the discussion is strongly encouraged. Another important part of this course is students’ presentations. Students will be asked to select applied papers in their chosen field and give a presentation on these papers.

 

This course is the second part of Research Methods in Finance sequence for postgraduate students. It focuses on the foundations of the equilibrium models of asset pricing rather than the arbitrage pricing theory, which is the other main pricing approach in finance. Topics include: a review of general equilibrium theory in pure exchange economies and economies with productions; utility theory under uncertainty; portfolio choice under uncertainty; mean-variance analysis; two fund separation theorem; the Sharpe-Lintner CAPM; theory of contingent markets and martingale representation of asset prices. All discussions will be within a two-period economy.

 

This is the advanced level of econometrics with ideas, theory and applications. Here, our focuses are on both the rigorous THEORY and SKILLS of analyzing real data using nonparametric methods and statistical software R.

Nonparametric econometrics is referred to statistical techniques that do not require a researcher to specify a functional form for an object being estimated. Rather than assuming that the functional form of an object is known up to a few unknown parameters, we shall substitute less restrictive assumptions such as existence and smoothness for the assumption that the parametric form of, say, a density function is known and equal to, say, the univariate normal distribution. Of course, if there is some prior knowledge about the functional form of the object of interest up to a few unknown parameters (say, mean and variance), then it would be better to use parametric techniques.

However, in practice these forms are rarely if ever known, and the unforgiving consequences of parametric mis-specification are well known and are not repeated here. Lectures will provide details on ideas, methodologies, theory and applications. In particular, the theoretical results will be derived in a rigorous way and the computer code for applications will be provided as well as all results will derived under both iid setting and time series contexts.

Applications include using nonparametric methods to recover the drift and diffusion functions in Black-Scholes model, to forecast the inflation rate, interest rate and exchange rates, to estimate the frontier production function, and to test if a jump diffusion model is appropriate for a specific financial asset, and so on so forth. There is no a single book serviced as a textbook for this course so that materials will be provided.