The Hong Kong Polytechnic University
School of Accounting and Finance
Applied Econometric Methods in Accounting and Finance Research (AF5908)
Fall 2020
Instructor
Dr. Steven X. Wei
Office: Room M708, 7th Floor, Li Ka Shing Tower, PolyU Campus
Tel:
2766-7056
Email: afweix@inet.polyu.edu.hk [Please always send your e-mails to this account. I do not
check my teaching web E-mail account due to time limitation!]
Consultation Hours: TBD (in the first class)
Making an appointment for any other time
Class Time and Venue
Day and Time: Thursday 18:30 – 21:20
Venue: Online teaching with Zoom
Description of the Course
This course covers basic concepts of econometrics, simple and multiple linear regression models,
various issues related to relaxing the assumptions of these models, dummy variables, etc. Least
square estimation method and maximum likelihood estimation method will be taught and
hypotheses testing techniques will be covered. Asymptotic theory will be briefly introduced.
Some advanced regression models, such as Probit/Logit models and censored models will be
discussed as well. We focus on those econometric issues encountered in doing empirical research
and practical reports. Some theoretical discussions are indispensable though I will certainly try
hard to teach the course in an intuitive way.
Textbook
Wooldridge, Jeffrey M., Introductory Econometrics: A Modern Approach, 7th ed., SouthWestern.
Some references and other hands-outs will be distributed in class.
To learn and work effectively, you are required to form a study group (with 3 - 5 members) to
handle the two homework assignments and discuss some “tough issues” you may encounter in
class. Please bring a calculator with you to each class [This would be useful to prepare for your
mid-term and final exams too].
Course Grading
AF5908
Class Participation
Two Homework Assignments
Take-home exam
10%
40%
50%
Overall
100%
1
Note
To pass the course, you need to pass both the Continuous Assessment (CA) and the Take-home
examination. The passing grade for each of the CA and taking-home exam is 40 (out of 100). If
you worry about passing the final exam, please talk to me before mid-October.
Tentative Topics
Part I. Regression Analysis with Cross-Sectional Data
1. Review of Basic Concepts in Econometrics (Appendices A – C)
Descriptive statistics (mean, median, variance, standard deviation, covariance, correlation
coefficient), random variable and its distribution (discrete and continuous random variables,
probability mass or density function (pf, pmf and pdf), cumulative distribution function
(cdf)), commonly-used distributions (uniform, Bernoulli, normal, t-, 2-, and Fdistributions), joint and conditional distributions, independence, estimator and estimate,
unbiasedness, consistency and efficiency; law of large numbers (LLN) and central limit
theorem (CLT), null (alternative) hypothesis, Type I (Type II) error, significance level, tstatistic, p-value; linear function (intercept and slope), quadratic function, exponential
function, (partial) derivative of a function, logarithm, marginal effect, diminishing marginal
effect, elasticity. [Please take the first two weeks to study the concepts carefully, if you do
not have the background.]
2. The Linear Regression Model (Ch. 2 – Ch.4 )
Dependent and independent variables, disturbance (error term), intercept and slope
parameters, population regression function (PRF), sample regression function (SRF),
ordinary least squares (OLS), fitted value, residual, R-squared (coefficient of determination),
unbiasedness, heteroskedasticity, standard error of regression.
Partial effect, multi-collinearity, exogenous and endogenous explanatory variables, misspecification, omitted variable bias, standard error of the regression (SER), the GaussMarkov theorem (BLUE).
Null and alternative hypothesis, t-ratio, significancelevel, rejection rule critical value, onetailed test, two-tailed test, statistically significant, economically significant, p-value,
confidence interval, F-test.
3. Further Topics with the Linear Regression Model (Ch. 5 – Ch.9 )
Consistency, asymptotic variance, asymptotic t statistics, Largrange multiplier (LM) test,
asymptotic efficiency, asymptotic normality, Adjusted R 2, dummy variable, the Chow test,
linear probability model, slef-selection problem, heteroskedasticity-robust standard error,
Breusch-Pagan test for heteroskedasticity, White test for heteroskedasticity, weighted least
squares estimators, feasible GLS, measurement error, Davidson-MacKinnon test, lagged
dependent variable, missing data, non-random sample, outlier.
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Part II. Regression Analysis with Time Series Data
* only talk about when there’s time.
4. Time Series Models (Ch. 10 – Ch.12, Ch.18 )*
(How much to cover the topic will depend on the progress of my teaching.)
Stochastic process (time series process), finite distributed lag (FDL) model, lag distribution,
long-run propensity (LRP) or long-run multiplier, auto-correlation, time trend, seasonality,
spurious regression, detrending, stationary process, non-stationary process, moving average
MA(q), autoregressive process AR(p), random walk, unit root process, integrated of order d
(I(d)), Dickey-Fuller test, first difference, co-integration, error correction model, stochastic
trend, Durbin-Watson (DW) statistic, ARCH model
Part III. Advanced Topics
5. Instrumental Variables Estimation and Two Stage Least Squares (Ch. 15)
Endogeneity problem (omitted variables, errors-in-variables, etc.), Instrumental variables,
identification, over-identification, under-identification, reduced form equation, two stage
least squares estimator, testing for endogeneity
6. Qualitative and Limited Dependent Variable Models (Ch. 17)
Logit/Probit models, specification, latent variable models, ML estimation of Logit/Probit
models, hypothesis testing with Logit/Probit models, interpretation of the estimates of the
models, pseudo R-squared, Tobit models, interpreting the Tobit estimates, inverse Mills ratio,
censored and truncated regression models, self-selection models, sample selection correction
7. Panel Data Models (Ch. 13 – Ch.14 )*
(How much to cover the topic will depend on the progress of my teaching.)
Two-period panel data analysis, difference-in-differences, fixed effects model, random
effects model
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