UNIVERSITY OF MACAU FACULTY OF BUSINESS ADMINISTRATION BACHELOR'S DEGREE PROGRAMME
ECIF311 ECONOMETRICS II
Second Semester 20102011
Instructor Contacts P. S. Tam Office: L430 (Thursday 4:00 p.m.  7:00 p.m. Or By appointment.) Phone: 83974756 Email: pstam@umac.mo Friday 1:00 p.m.  4:00 p.m. J207 http://webcourse.umac.mo
Class Website
Description:
This course focuses on basic econometric techniques, emphasizing both technical derivations and practical applications. The linear regression model will be reviewed using matrix algebra, and its limitations addressed. Topics on dynamic models, random regressors, simultaneous equations models, and time series econometrics will be covered. If time permits, panel data models and qualitative and limited dependent variable models will also be discussed. Upon completing this course, students are expected to be able to undertake their own econometric analysis.
Prerequisite:
ECIF310 or equivalent. In general, knowledge in basic economic theory, calculus, probability and statistics is required.
Textbook:
Hill, R.C., Griffiths, W.E., and Lim, G.C. Principles of Econometrics, Third Edition. John Wiley & Sons, Inc. 2008. Griffiths, W.E., Hill, R. C., and Lim, G.C. Using EViews for Principles of Econometrics, Third Edition. John Wiley & Sons, Inc. 2008. (They are available from the university bookstore as a bundle with student discount.) Textbook website: http://as.wiley.com/WileyCDA/WileyTitle/productCdEHEP001750.html?filter= TEXTBOOK.
1
References:
Gujarati, D.N., and Porter, D.C. Basic Econometrics. McGrawHill. Stock, J.H. and Wason, M.W. Introduction to Econometrics, Pearson Higher Education. Wooldridge, J. Introductory Econometrics: A Modern Approach, SouthWestern Cengage Learning.
Software:
EViews. EViews 6 Student Version comes with the textbook purchase. EViews 7 is available in the university computing network.
Content:
Topic Review of Matrix Algebra Matrix Approach to Linear...
...1220
1420
Now
Therefore the regression equation is:
What The regression Equation Tells:
a. Y and X are positively associated, if X increases, Y will also increase. This is visible from sign of slope term
b. If x increases by 1 unit, Y increases by 0.924 units, it is visible from the slope coefficient (verify)
c. If X is zero, than , put x=0 in the regression equation, you will get this value, assuming error term is sero. This is only systematic part of y, actual value of y is sumk of systematic and random part and random part cannot be predicted, so actual y also cannot be predicted. However we hope it will be closer to the estimate. [In fact we can also tell range where actual value of y should lie, but we will learn this technique later.
Exercise: take a small data set and calculate regression equation.
The matrix approach to the OLS
Consider the data set given by the fig. As before we can write the relationship as:
Here the RHS consist of systematic part (regression line, the straight line for predictions) and stochastic part.
More explicitly, we can write as:
…..
……
……
This system of equation can be written as:
Or
Where
Or
Now
=RSS
So minimizing is equivalent to minimizing RSS which we have done in lecture one. However here we have to take derivative of matrices w.r.t. a matrix in order to apply first order condition. I skip the intermediate detail and write the solution of the OLS parameters:...
...EEC is equal to zero and replace by 1 with the given list of countries for each groups. The procedure is repeated for the other group EFTA, EEC_EFTA and Kennedy.
.
is the standard deviation of the natural logarithm. In order to calculate of each group, I have to generate the variable call ln(y) which represent natural logarithm of real per worker income and label it as log of y. The group EEC will be use to illustrate as an example of how to create sigma (). The command that we use in stata is egen because it is a built in command. I restricted the year in the stata command as Sigma of EEC group under restricted period is between 1950 and 1969. Finally, the sigma of EEC is label as per worker dispersion for EEC countries as a description for future reference.
Since, is defined as . I used stata to generate new variable that equal to mean lny, by year. In addition, I have to restrict the year for each of the cases within the restricted year according to years of liberalization period. For example, The restricted year for EEC cases is between 1950 to 1968.
The value of for EEC country is the subtraction of mean of lny by Lny. The process of creating and is repeated four times for four cases.
Finally, I sum the mean and standard deviation variables and and present the four separate means and standard deviation in the table.
Question4
The stata command that is used to plot the evolution of sigma over the pre and liberalisation...
...British and American Newspapers (2012)
Aims of This Course
This course has been a traditional course in the curriculum for English majors in China. One of the underpinning ideas for its enduring presence in the curriculum is the belief that putting learners into touch with “original” news texts found in Western newspapers would (A) enrich language input in learning, which, at least theoretically, could maximize the chance of successful language learning; and (B) provide a chance for learners to “learn about” cultures shaping the news texts. Thus, this course has traditionally been regarded as a course that prepares learners for further development in language and intercultural communication skills.
However, this skill orientated positioning of the course is not congruent to current understanding of university education. If university courses like this only focus on language and communication skills, then what is the core competency of graduates? How do they distinguish themselves from trainees in nonuniversity institutions? What is their distinct competitive advantage compared with an experienced practitioner? As an attempt to answer these questions, this course is designed around the assumption that ‘thinking’ is the core competency of the graduate, more specifically, ‘applied critique’. Applied critique is knowledge that stands the test of time by...
...
ECONOMETRICS



First of all, I would like to apologize for showing the results in Spanish, but I couldn’t find the way to change Gretl’s language. However, all the explanations are in English, so I hope there is no problem to understand the results.
Secondly, I would just inform you that the timeseries data that I have used is “U.S. macro data, 19502000” from Greene Sample folder in Gretl.
Before building the model…
I would try to explain the variable “Real GDP” using the variables “Real consumption expenditures”, “Real PrivateSector Investment”, “Real government expenditure”, “Unemployment rate” and “Inflation rate”. To do so, the first thing we should do is to check if there is correlation risk between the independent variables. We will use the Correlation Matrix to figure out this:
Since the coefficient of correlation between the variables Real Consumption, Real PrivateSector Investment and Real Government expenditure is very close to 1, it means that those variables are providing almost the same information, so I would delete some of them, and check the coefficient of correlation again.
Once Real PrivateSector investment and Real Government Expenditure have been deleted from the correlation matrix, the coefficients of correlations between variables are acceptable now, and we can be sure that every variable gives different information about the model.
However, I could have also used another statistic to check is...
...Journal
of Econometrics
41 (1989) 205235.
NorthHolland
TESTING INEQUALITY CONSTRAINTS IN LINEAR
ECONOMETRIC MODELS
Frank A. WOLAK*
Stanford
Received
lJniversi[v,
February
Stunford,
CA 94305, tiSA
1986, final version received July 1988
This paper develops three asymptotically
equivalent tests for examining the validity of imposing
linear inequality restrictions on the parameters of linear econometric models. First we consider the
model .v = X/3 + e. where r is N(O,8), and the hypothesis test H: R/l 1 r versus K: p E R”. Later
we generalize this testing framework to the linear simultaneous equations model. We show that the
Joint asymptotic
distribution
of these test statistics and the test statistics from the hypothesis test
H: RP = I versus K: R/3 2 r is a weighted sum of two sets of independent
X’distributions.
We
also derive a useful duality relation between the multivariate inequality constraints
test developed
here and the multivariate
onesided hypothesis
test. In small samples, these three test statistics
satisfy inequalities
similar to those derived by Berndt and Savin (1977) for the case of equality
constraints.
The paper also contains an illustrative application of this testing technique.
1. Introduction
Estimation
under inequality
restrictions
has a long history in regression
analysis and its application
has become more widespread with the...
...Econometrics is the application of mathematics and statistical methods to economic data and described as the branch of economics that aims to give empirical content to economic relations. [1] More precisely, it is "the quantitative analysis of actual economic phenomena based on the concurrent development of theory and observation, related by appropriate methods of inference."[2] An influential introductory economics textbook describes econometrics as allowing economists "to sift through mountains of data to extract simple relationships."[3] The first known use of the term "econometrics" (in cognate form) was by Paweł Ciompa in 1910. Ragnar Frisch is credited with coining the term in the sense that it is used today.[4]
Econometrics is the unification of economics, mathematics, and statistics. This unification produces more than the sum of its parts.[5] Econometrics adds empirical content to economic theory allowing theories to be tested and used for forecasting and policy evaluation
Basiceconometric models: linear regression
The basic tool for econometrics is the linear regression model. In modern econometrics, other statistical tools are frequently used, but linear regression is still the most frequently used starting point for an analysis.[7] Estimating a linear regression on two variables can be visualized as fitting a line...
...a variety of reasons. Some studies have
focused on the returns to education, others on discrimination, union nonunion dierentials, etc.
For all these studies, a major concern has been the fact that ability should enter as a determinant of
earnings, but that it is close to impossible to measure and therefore represents an omitted variable.
Assume that the coecient on years of education is the parameter of interest. Given that education
is positively correlated to ability, since, for example, more able students attract scholarships and
hence receive more years of education, the OLS estimator for the returns to education could be
upward biased. To overcome this problem, various authors have used instrumental variable estimation techniques. For each of the instruments potential instruments listed below briey discuss
instrument validity.
(a) The individual's postal zip code.
(b) The individual's IQ or testscore on a work related exam.
(c) Years of education for the individual's mother or father.
(d) Number of siblings the individual has.
4
ECON 140
Section 13, November 28, 2013
Solutions
1. (a) Substitution of the rst equation into the identity shows that Xi is correlated with the error
term. Hence estimation with OLS results in an inconsistent estimator.
ˆT
(b) The instrumental variable estimator is consistent and in this case is β1 SLS =
SZY
SZX
.
(c)
Yi = β0 + β1 (Yi + Zi ) + ui
Xi = (β0 + β1 Xi + ui ) + Zi
or...
...Introduction to Econometrics coursework
For the assignment I will examine whether or not a linear regression model is suitable for estimating the relationship between Human development index (HDI) and its components. Linear Regression is a statistical technique that correlates the change in a variable to other variable/s, the representation of the relationship is called the linear regression model.
Variables are measurements of occurrences of a recurring event taken at regular intervals or measurements of different instances of similar events that can take on different possible values. A dependent variable is a variable whose value depends on the value of other variables in a model. Hence, an independent variable is a variable whose value is not dependent on other variables in a model.
The dependent variable here is HDI and this will be regressed against the independent variables which include Life expectancy at birth, Mean years of schooling, expected years of schooling and Gross National Income per capita Hence we can model this into Yi = b0 + b1 xi + b2 xi + b3 xi + b4 xi + where Y is HDI, β0 is a constant, β1 β2 β3 β4 are the coefficients and denotes for random/error term.
R2 is how much your response variable (y) is explained by your explanatory variable (x). The value of R2 ranges between 0 and 1, and the value will determine how much of the independent variable impacts on the dependent variable. The R2 value will show...