Introduction To Quantitative Methods For Economists
- Module Code:
- Module Withdrawn
- Unit value:
- Year of study:
- Year 2
This course offers a survey of basic economic statistical techniques and an introduction to the mathematical analysis useful for economic theory. The course complements the two other courses taught in the first year, and is an essential prerequisite for the second-year courses in economic theory and econometrics.
This is a compulsory course for students taking a single-subject economics degree. It is a core course for BSc Economics (not for BSc Development Economics). It is a prerequisite for Econometrics.
Objectives and learning outcomes of the module
On successful completion of the course, you should be able to:
- discuss the role of quantitative methods in economics
- explain basic arithmetic and algebraic concepts including, but not restricted to, real numbers, rational and irrational numbers, integers and fractions, variables, constants, monomials, polynomials and terms, equations and functions
- demonstrate ability to simplify and manipulate mathematical expressions
- factorise polynomials
- demonstrate understanding of and ability to solve equations and system of equations
- explain and use the exponential and logarithmic functions
- demonstrate understanding of, derivatives, rules of differentiation, partial derivatives, total differential, higher order derivatives, their uses and applications
- demonstrate understanding of and ability to use the first- and second-order partial derivatives and Young's theorem
- solve unconstrained and constrained optimisation problems
- explain indefinite and definite integrals and use them to solve problems
- use the mathematical methods covered in the course to solve problems in economics.
- explain basic statistical concepts including, but not restricted to, descriptive and inferential statistics, and levels of measurement
- demonstrate understanding and ability to use basic descriptive statistic techniques including, but not restricted to, frequency tables, histogram, and ogive
- use the summation operator
- demonstrate understanding of and ability to use measures of central tendency, dispersion, skewness and kurtosis
- calculate and interpret covariance and correlation coefficient
- demonstrate understanding of indices including, but not restricted to Laspeyres and Paasche indices, their uses and applications
- demonstrate understanding of and ability to use statistical techniques to analyse inequality including, but not restricted to, Lorentz curve and Gini coefficient
- explain basic concepts in probability theory including, but not restricted to, experiment, population or sample space, sample point, event, mutually exclusive, equally likely and collectively exhaustive events, random variable, discrete and continuous random variable, probability, probability distribution or probability density function (PDF), statistical independence
- use characteristics or moments of PDF including, but not restricted to expected value, variance and standard deviation, skewness and kurtosis, covariance, coefficient of correlation, conditional and unconditional expectation
- use population parameters and sample estimators including, but not restricted to sample mean, sample variance, sample standard deviation, sample covariance, sample correlation, sample skewness and sample kurtosis
- demonstrate understanding of and ability to use normal distribution, chi-square, t and F distributions
- explain the sampling distribution of an estimator (e.g. the sample mean)
- demonstrate understanding of and ability to use point and interval estimation and hypothesis testing
- explain the method of ordinary least squares (OLS) and use it to estimate regression coefficients
- interpret and critically evaluate econometric results
- demonstrate understanding of measures of goodness of fit including their uses and limitations
- explain the assumptions of the classical linear regression model
- use t test on regression coefficients
Method of assessmentAssessment weighting: Exam 100%
Mathematics: term 1
- Dinwiddy, C. Elementary Mathematics for Economists (Nairobi: Oxford U.P., 1967).
- Dowling, E. T. Introduction to Mathematical Economics, Schaum's Outline series (New York: McGraw-Hill, 1980).
- Thomas, R. L. Using Mathematics in Economics (Second Edition, Addison-Wesley, 1999).
- Chiang, A.C. Fundamental Methods of Mathematical Economics (McGraw-Hill, 1984).
Statistics: term 2
- Gujarati, D. Essentials of Econometrics (2nd ed., McGraw Hill, 1999).
- Kmenta, J. Elements of Econometrics (Second Edition, New York: Macmillan, 1990).
- Barrow, M. Statistics for Economics Accounting and Business Studies (London: Longman, 1996).