Dept of Eco Ets Course Content Mphil Econometrics
Dept of Eco Ets Course Content Mphil Econometrics
Dept of Eco Ets Course Content Mphil Econometrics
PHIL ECONOMETRICS
Academic Program
Total Credit Hours: 39
Course Work: 27
Thesis: 12
Duration: 2 Years
Total Courses: 9
Core Courses: 7 (First Semester: 4, Second Semester: 2, Third Semester, 1)
Note: A student will qualify for thesis if he/she attains minimum 3 CGPA in course work after completion
of 2nd semester (excluding Research Methodology).
Course Outline:
Theory of Consumer Behaviour; Theory of Firm; Market Equilibrium; Uncertainty and Information
Asymmetry. The theory of consumer behavior includes: Direct and Indirect Utility Functions, Derivation of
Marshallian and Hicksian Demand Curves; Consumer Surplus. Theory of Firm includes constrained
optimization of Production, Cost and Profit Functions; Derivation of Input Demand Functions, Returns to
Scale, Perfect and Imperfect Market Competition. Game theoretic concepts are discussed with reference to
Oligopolistic Markets.
ETS- 610: MACROECONOMIC THEORY ........................................................................ (CREDIT HOURS 3)
Course Outline:
Introduction to Classical Economics-Utopian world of Demand and Supply, Quantity theory of money,
Why supply and demand does not work in the labor market – Efficiency Wage Theories, Factors that led
to Great Depression, Nonexistent demand and supply model, Household debt, Bubble in the assets
market, Keynesian Economics,-Wage – Price rigidities and malfunctioning of demand – supply model,
Role of money in economic activity, Government role as a leader in investment activities, Chicago plan
and Islamic Chicago Plan. Post Keynesian Economics and the first Neo-Classical synthesis, Convergence
and stability debate, Monetarists counter revolution, Redefining role of money, New classical economics,
Rational expectations hypothesis, Policy irrelevance proposition, Macroeconomic modeling based on
microeconomic foundations, New Keynesian Economics and the second Neo-Classical synthesis: The Era
of Great Moderation, Rational expectations and micro foundations as norms in macroeconomics, Wrong
perception of the role of money, Interest rate as monetary policy instrument despite its limited role, DSGE
models and neglecting the distributional issues, , Inflation targeting regime, How did economists get it
wrong, Misperception of the role of money: Werner’s theory, Limitations of interest rate targeting,
Misuse of rational expectations hypothesis, Monopoly of private banks and money creation process,
Explaining bubble as great moderation, Financial crisis and Great Recession, Fiscal austerity in Europe,
Role of free trade in European crisis, Ideological battle in Macroeconomics.
Recommended Books:
1. Romer, D. (2011),Advanced Macroeconomics. McGraw-Hill.
2. William M. Scarth (1988) Macro Economics. An Introduction to Advanced Methods.
3. Blanchard and Fischer (1989), Lectures on Macroeconomics. MIT Press.
4. Sargent, Thomas (1987), Dynamic Macroeconomics Theory. Harvard Univ. Press.
Other Books
1. Obstfeldt, M. and K. Rogoff (1996). Foundations of International Economics. MIT Press.
2. Barro, R. and X. Sala-i-Martin (1995). Economic Growth. McGraw-Hill .
3. Sorensen, P.B. and Whitta-Jacobsen, H. J. (2005), Introducing Advanced Macroeconomics:
Growth and Business Cycles. McGraw Hill.
4. Heijdra,B. and Ploeg,F. (2009),The foundations of Modern Macroeconomics. Oxford University
Press.
Reading Material
1. Winning Ideas: Lessons from Free Market Economics. Alkire, Sabina and Ritchie, Angus(2007),
OPHI Working Paper No. 6.
2. Efficiency Wage Hypothesis – the case of Pakistan by Zaman, Asad and Syed Kanwar Abbas,
(2005), Pakistan Development Review, Vol 44 number 4, 1051-1066
3. A Quick Refresher Course in Macroeconomics by N. Gregory Mankiw, Journal of Economic
Literature (Dec 1990).
4. Revolution and Evolution in 20th Century Macroeconomics by Michael Woodford, Princeton,
1999.
5. Gordon, Robert J. "What Is New-Keynesian Economics?" Journal of Economic Literature 28, no.
3 (September 1990): 1115-71.
6. Keynes, John Maynard. The General Theory of Employment, Interest, and Money. 1936.
7. “The Counter-Revolution in Monetary Theory” (1970) by Milton Friedman, IEA Occasional
Paper no. 33.
ETS-620: QUANTITATIVE FOUNDATION FOR ECONOMETRICS: .............................. (CREDIT HOURS 3)
Course Outline:
Arbitrary Ranking ,Sorting, Ranking and Percentiles Concept and importance of Sorting, Ranking and
Percentiles , REPRESENTING: (Mean, Median and Mode), Mean, Median and Mode, Measures of
Spread, and Outliers Various measures of spread are discussed like IQR Standard Deviation, Measures of
Spread and Outliers, Boxplots Histograms Bivariate, Relationships , Comparison of Visual graphics.
Probability, Random Variables and Probability Distribution, Distribution Functions of Random Variables,
General Notion of Expectation, Stochastic Processes, Limit theorems, Statistical Inference, Properties of
Estimators, Estimation Methods, Hypothesis Testing and Confidence Regions, Measures of Spread, and
Outliers various measures of spread are discussed like IQR Standard Deviation, The Introduction to
Econometric Modelling, Statistical Models in Econometrics. The Gauss Linear Model, The Linear
Regression Model- Specification, Estimation and Testing, The Dynamic Linear Regression Model,Random
sampling, Bernoulli sequence, Discrete Random Variable, Continuous RV, Expectations, Moments and
MGF, Central Limit theorem
Recommended Books
Course Outline
Introduction Course Setup : What is data visualization? How can data be visualized, How can Data
Visualization be used? When data visualization is suitable? Econometrics, Data Visualization and Data
Science,Tools for data visualization for this course (R-Studio and R library ggplot2),
Explorative Data Analysis Start tutorials,
R Graphics I, R Graphics II, ggplot2
Which Chart to use When
Visualizing Categorical and Continuous data
Time series visualization
Scatter plots, residuals, regression discontinuity designs, nonlinearities
Geospatial data visualization
Advanced Charts: Treemap, Classification Trees, Heatmapsetc
De-Clutter Graphs for Better Data Insights
Making use of colors , facets, aesthetics, legends
Interactive Data Visualization Using plotly/R shiny
Dashboard Designs
Telling Story With Data Visualization
Note: Additional resources like Excel for graphics can be used but main software to be used is R
(ggplot2) as coding will help students in their data analytics in other courses as well. We shall mainly
follow book Winston Chang and Nathan Yau (both use R language for data visualization).
Books
Course Outline
Estimation Methods; Maximum Likelihood and Generalised Method of Moments; Models of Consumption
and Investment; Models Connecting Asset Market Data to Economic Aggregates and Models of the
Underlying Sources of Economic Fluctuations; The Estimation of Demand and Supply Equations; Estimation
of Production Relationship; Estimation of Pricing Equations in Finance and Labour Economics and
Calibration of General Equilibrium Models, Monte Carlo Simulations and Computer Programming. House
hold demand Models
Optional Courses
Recommended Books:
1. Arellano, M. (2003), Panel Data Econometrics, Oxford University Press.
2. Baltagi, B.H. (2006), Panel Data Econometrics: Theoretical Contributions and Empirical Applications,
Emerald Group Publishing.
3. Hsiao, C. (2003), Analysis of Panel Data, Cambridge University Press.
Other Books
1. Frees (2004), Longitudinal and Panel Data, Cambridge University Press.
2. Wooldridge (2008), Econometric Analysis of Cross Section and Panel Data, 2nd Ed., MIT Press.
Recommended Books:
1. Anselin, L., Florax, R.J.G.M. and Rey, S.J. (2004), Advances in Spatial Econometrics:
Methodology, Tools and Applications, Springer.
2. Arbia, G. and Baltagi, B,H. (2009), Spatial Econometrics: Methods and Applications, Springer.
3. Haining, R. (2003), Spatial Data Analysis: Theory and Practice. Cambridge University Press:
New York.
4. LeSage, J.P. and Pace, R.K. (2009), Introduction to Spatial Econometrics, CRC Press.
Other Books
1. Fischer, M.M. and Getis, A. (2010), Handbook of Applied Spatial Analysis: Software Tools,
Methods and Applications, Springer.
Course Outline:
The Empirical Distribution Function, Likelihood, Influence Functions, Jackknife and Bootstrap
Confidence Intervals and Tests, Permutation Tests, Rank Tests, Bias-Variance Tradeoff, Cross-
Validation, Kernel Density Classification and Estimation, Curse Of Dimensionality, Nonparametric
Regression, Basis Expansions, Splines, and Penalized Regression, Quantile Regression, Nonparametric
Approaches to Multiple Regressions, Generalized Additive Models, Nonparametric Analysis of
Longitudinal Data.
Recommended Books:
Course Outline: Basic mathematical tools, integration and differential equations, Qualitative theory,
Control theory, Ramsey problem of optimal consumption, Dynamic programming, Lucas' model of
endogenous growth, Stochastic models in discrete time, Discrete-time optimization, Overlapping-
generations, Real-business-cycles, Stochastic models in continuous time, Stochastic differential equations
and rules for differentials, Merton's model of growth under uncertainty, Stochastic dynamic control
problems, Optimal saving under uncertainty.
Recommended Books:
1. Chang, Fwu-Ranq, Stochastic optimization in continuous time, Cambridge Univ. Press, 2004.
2. Sydsaeter, Knut, Peter Hammond, AtleSeierstad, and Arne Strom, Further Mathematics for
Economic Analysis, Prentice Hall, 2008.
ETS-880 OPERATIONS RESEARCH ......................................................................... (CREDIT HOURS 3)
Course Outline:
Introduction; Linear programming and modelling; Simplex method; Sensitivity Analysis; Decision analysis;
Game theory; Queuing systems; Optimization theory; Post-optimal analysis; Dynamic programming;
Network Optimization models: Critical Path Method (CPM), PERT; Project Management with PERT/CPM;
Simulation; Planning over Time, Uncertainty and Forecasting; Markov Decision Process; Operations
Research Applications.
Recommended Books:
Course Outline:
Fundamentals of Structural Equation Modeling: Basic concepts, Latent versus observed variables,
Exogenous versus endogenous latent variables, The factor analytic model, The full latent variable model,
General purpose and process of statistical modeling, The general structural equation model, Symbol
notation, The path diagram, Structural equations, Nonvisible components of a model, Basic composition,
The formulation of covariance and mean structures.Path Analysis: Introduction, Path Diagrams, Rules
for Determining Model Parameters, Parameter, Estimation, Parameter and Model Identification, Model-
Testing and -Fit Evaluation, Example Path Analysis Model, Modeling Results, Testing Model
Restrictions in SEM, Model Modifications. AMOS: Getting to Know the AMOSProgram, Structure of
Input Files for SEM Programs, Introduction to the AMOS Notation and Syntax, Introduction to the
AMOS Notation, Introduction to the AMOSNotation and Syntax. Confirmatory Factor Analysis: What
Is Factor Analysis? Factor Analysis Model, Identification, estimation, Model Evaluation, Modeling
Results, and Testing Model Restrictions: True Score Equivalence. Structural Regression Models: What
Is a Structural Regression Model? An Example Structural Regression Model, Modeling Results, Factorial
Invariance across Time in Repeated Measure Studies. Latent Change Analysis: Measuring change in
individual growth over time: The general notion, The hypothesized dual-domain LGC model, Modeling
intra individual change, Modeling inter-individual differences in change, Testing latent growth curve
models: A dual-domain model, The hypothesized model, Selected AMOS output: Hypothesized model,
Testing latent growth curve models: Gender as a time-invariant predictor of change.Mediation :
Introduction, Applications of the Mediation Model, Single Mediator Model, Single Mediator Model
Details, Multiple Mediator Model, Path Analysis Mediation Models, Latent Variable Mediation Models,
Longitudinal Mediation Models, Multilevel Mediation Models, Mediation and Moderation, Mediation in
Categorical Data Analysis, Computer Intensive Methods for Mediation Models, Causal Inference for
Mediation Models. Moderation: Introduction, Applications of the Moderation Model, Estimation,
interpretation. MIMIC Modeling: Multiple Indicators Multiple Causes (MIMIC)model involves using
latent variables that are predicted by observed variables. Bootstrapping as an aid to non-normal data:
Basic principles underlying the bootstrap procedure, Benefits and limitations of the bootstrap, Procedure,
Caveats regarding the use of bootstrapping in SEM Modeling with AMOS Graphics, The hypothesized
model, Characteristics of the sample, Applying the bootstrap procedure, Selected AMOS output,
Parameter summary, Assessment of normality, Statistical evidence of non-normality, Statistical evidence
of outliers, Parameter estimates and standard errors, Sample ML estimates and standard errors, Bootstrap
ML standard errors, Bootstrap bias-corrected confidence intervals
Recommended Books:
1. A First Course in Structural Equation Modeling Second Edition TenkoRaykov, Michigan State
University and George A. MarcoulidesCalifornia State University, Fullerton
2. Introduction to Statistical Mediation Analysis by David P. Mackinnon
3. Structural Equation Modeling with AMOS, SECOND EDITION, Barbara M. Byrne
4. Principles and Practice of Structural Equation Modeling, Third Edition, Rex B. Kline
Course Outline:
Basic Concepts of population, Target Population, A sample, Sampling frame, Probability and non-
probability sampling, Design of the survey, Sampling techniques such as random sampling, stratified
random sampling, systematic sampling, Areas aping etc. Estimation of ratio and regression estimators,
Comparisons of various estimators, Response and non-response errors and imputation.
Recommended Books:
Recommended Books:
1. Christopher, Z. Mooney. (1997), Introduction to Monte Carlo Methods, Sage Publications.
2. Gentle, James E. (2003),Random Number Generation and Monte Carlo Methods, Springer.
3. Morgan, B. J. T. (1984), Elements of Simulation, Chapman and Hall.
Other Books
1. Fishman, G.S. (1996), Monte Carlo: Concepts, Algorithms, and Applications, Springer.
2. Chen, Ming-Hui, Shao, Qi-Man, Ibrahim, Joseph G. (2000),Monte Carlo Methods in Bayesian
Computation,Springer.
3. M. E. J. Newman and G. T. Barkema. (1999),Monte Carlo Methods in Statistical Physics, Oxford
University Press.
4. Neal Madras. (2000),Monte Carlo Methods, AMS Books.
5. Ripley B.D. (1987), Stochastic Simulations, John Wiley & Sons.
6. Ross, S. M.(2002), Simulation, Academic Press.
7. Rubinstein, R.Y. (1981), Simulation and the Monte Carlo Method, John Wiley & Sons.
Recommended Books:
1. Stirzaker, D. (1999), Probility and Random Variables, Cambridge University Press, Cambridge.
2. Stuart, A., and Ord, K. (1998), Advanced Theory of Statistics Vol. I, Charles Griffin and Co.
3. Feller, W. (1968), Introduction to Probability Theory and its Applications Vol. 1, John Wiley and
Sons.
Other Books
1. Khan, M.K. (1994), Pobability Theory with Applications, IlmiKitabKhanna, Lahore, Pakistan .
2. Rohatgi, S. (1976), Introduction to Probability Theory, Mcgraw Hill.
Course Outline:Comparison of Point Estimators:The framework for parametric inference, Mean Square
Error, Unbiased estimators, Sufficiency, Factorisation Theorem, Minimal sufficiency. Distribution Theory:
Conditional distributions and expectations, Central Limit Theorem.Minimum variance unbiased estimation:
Rao-Blackwell Theorem, Exponential Families, Lehmann-Scheffé Theorem.Likelihood, Fisher Information
and the Cramér-Rao Inequality: The Efficient Score, Fisher Information, Cramér-Rao lower bound,
Attainment of the Cramér-Rao lower bound, Multi-dimensional Cramér-Rao inequality.Maximum likelihood
estimators: Elementary properties, Consistency and asymptotic efficiency. Hypothesis Testing: Definitions,
The Neyman-Pearson lemma, Tests of composite hypotheses, Likelihood ratio tests.Confidence Sets:
Relationship with hypothesis tests, pivotal quantities.
Recommended Books:
1. Mood, A.M., Graybill, F.A. and Boes, D.C.: Introduction to the Theory of Statistics, McGraw
Hill,
2. DeGroot, M.H. and Schervish, M. J.: Probability and Statistics, Addison-Wesley, 2002.
3. Casella, G. and Berger, R.L.: Statistical Inference, 2nd ed., Duxbury Press, 2001.
4. Silvey, S.D.: Statistical Inference, Chapman & Hall, 1978.
5. Statistical Theory: B W Lindgren, Collier MacMillan
Course Outline:
What Big Data Means? When Do You Have a Big Data Problem? Characteristics, Sources and
importance of Big Data Analysis. Compare and contrast the roles of: data-at-rest processing, data-in-
motion processing, data-warehouse processing, and contextual search. Tools available for Big Data
Analytics. Ingesting and integrating data, Storage and compute platforms. Presentation and
visualization: Understand the purpose of various types of data visualization, ranging from infographics to
visual analytics, Apply design principles to design visualization techniques, Use visualization tools to
perform visual analysis. Algorithms and analytics: Identify Big Data problems that require Statistical
Techniques, Apply the Statistical Techniques correctly on Big Data Problems, Understand the properties
of these techniques, and the role of assumptions, Interpret the conclusions properly, Programme in “R”,
Neural nets, Support vector machines. Understand methods from machine learning: Classification and
regression trees, decision trees and decision forests, Random forests, clustering and topic modelling,
logistic regression and deep learning, matrix factorization and time series analysis &spatio-temporal event
modelling, Boosting, Bagging, Spike and slab regression? Penalized regression (e.g., the lasso, lars, and
elastic nets). Apply the methods in advanced techniques: text analytics, image and video analytics and
recommendation, Apply the techniques in large scale use-cases. Security and privacy.
Recommended Books:
1. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. The Elements of Statistical Learning: Data
Mining, Inference, and Prediction. Springer- Verlag, 2 edition, 2009.
2. Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. An Introduction to Statistical
Learning with Applications in R. Springer, New York, 2013.
3. Graham Williams. Data Mining with Rattle and R. Springer, New York, 2011.
4. Xindong Wu and Vipin Kumar, editors. The Top Ten Algorithms in Data Mining. CRC Press, 2009.
5. Bill Howe. Introduction to data science. Technical report, University of Washington, 2013.
6. W. N. Venables and B. D. Ripley. Modern Applied Statistics with S. Springer-Verlag, New York, 4
edition, 2002.
7. Varian, Hal R. "Big data: New tricks for econometrics." The Journal of Economic Perspectives (2014): 3-
27.
8. Einav, Liran, and Jonathan D. Levin. The data revolution and economic analysis. No. w19035. National
Bureau of Economic Research, 2013.
Course Outline:
The overall aim of this subject is to familiarize students with essential concepts and techniques used in
Bayesian inference. The course will provide students with the necessary programming skills to implement
Bayesian estimation and inference for econometric model using suitable software. The formal course
description is:
Foundations of Probability, Bernoulli & Binomial RV’s, Conventional Inference for Surveys, From Data
to Densities, Prior to Posterior updating in Beta-Binomial Model, Assessment of Binomial Models,
Bayesian Econometrics Multiple Matches, Match Binomial Model to Real World, Bayesian Econometrics
Testing Independence, Bayesian Econometrics Chi-Square Test, Review of Normal Distribution,
Bivariate Normal Distribution, Bayesian calculations for normal data, normal prior, Bayesian
Calculations with Normal-Gamma Priors, Fundamental Formulas for Bayesian & Conventional Inference
with IID Normal, Principles for Applications of Normal Inference on Real Data, IID Normal Inference
with Real Data, Bayesian inference using IID Normal Models for real Data, Introduction to Empirical
Bayes, Empirical Bayes for Panel IID Normal Data, Empirical Bayes & Stein Estimation, Empirical
Bayes on Recession Probabilities and Fire Alarms, Empirical Bayes Quality Control, Empirical Bayes in
Regression Models, Hierarchical Bayes and Gibbs Sampler.
Recommended Books:
Course Outline:
The purpose of this course is to enable students to understand what asymptotic theory is and how is it
used to design and analyze the statistical tests and estimators. However, asymptotic could fail to perform
for two reasons (a) asymptotic theory is the large sample theory and could not be very good in
small/medium sizes (b) the asymptotic theory could be sometimes too complicated to be analytically
solved. Simulations could serve as a substitute for the asymptotic theory where it fails to perform. The
second half of the course is about simulation, where, the students will be taught how to solve econometric
problems using simulation methods.
Asymptotic Theory
This part will cover selected chapters from William H Greene (Econometric Analysis), Jhonston and
Dinardo (Econometric Methods) and Peter Kennedy (A Guide to Econometrics) including topics:
Maximum likelihood principal and applications, Properties of maximum likelihood estimators, Wald,
Lagrange Multiplier and Likelihood Ratio tests, Large sample theory, Central Limit theorem, Law of
Large Numbers, Convergence in Probability, Convergence in Distribution, Convergence of Function of
Random Variables, Large Sample Properties of Least Square, Instrumental Variables and GLS, More on
Maximum Likelihood, Estimating Asymptotic Variance of Maximum Likelihood Estimator, 2 step
maximum likelihood.
Simulations
This part will cover what is simulation? Simulating mean, median, mode and matching it with theoretical
properties of the random variables, Central Limit Theorem: Verification by simulations. Properties of
OLS using Monte Carlo simulations: Unbiasedness, and Consistency; biasedness in overs pecified and
underspecified models. Testing Correlation: Pearson and Rank Order Correlation, Computing Simulated
Critical Values and Power of correlation tests, comparison of power of the two tests and choice of test.
Introduction to MATLAB: How to write function and program files, loops, matrices, conditions, Using
MATLAB to simulate unit root tests and cointegration tests, Introduction to R programing: comparison of
features of MATLAB and R. Using built in packages of R
Helping Material: Excel Lecture Notes, MATLAB user manual, R-user manual
Recommended Books:
Course Outline:
Foundations of Risk Analysis; Measuring Risk; Application of Risk Analysis; the Portfolio
Selection Problem; the Capital Asset Pricing Model; the Arbitrage Pricing theory; Common
Stocks; Preferred Stocks; Bonds; Capital Structure theories; the goal of the Firm; the Economic
Evaluation of Investment Proposals; the traditional Mundell Fleming Model; the Dynamic-
Optimizing model with Price Flexibility; Intertemporal Model with Price Stickiness; Currency
Crises; External Crises: Fiscal Policies and Taxation in the Open Economy; International Capital
Flows under Asymmetric Information; and International Growth Convergence.
Course Outline:
Course Outline:
This course is designed to understand wide variety of complex adaptive systems using agent based
modelling. During the course, power of ABM in understanding the real world behavior amenable to
complex system analysis will be explored. This course will help students to learn studying economic
and social phenomenon through ABM.NetLogo programming language which is developed at
Northwestern University will be used for building ABM. No programming background/knowledge is
required for student to register the course.
Why do we need to understand agent Based modelling?What Is Agent-Based Modeling?Creating
Simple Agent-Based Models, Complex Adaptive Systems, Introduction, logic and need of Modelling,
Exploring and Extending Agent-Based Models, Creating Agent-Based Models, The Components of
Agent-Based Modeling, Analyzing Agent-Based Models, Verification, Validation, and Replication,
Advanced Topics and Applications, Sensitivity, Uncertainty, and Robustness Analysis, Tragedy of the
Commons, Networks, Diffusion of Innovation, Fads and Fashion, Collective Action, Labor market Job
search and wage distribution, Growth Theories, Stock Market.
Recommend texts:
1. Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: modeling natural,
social, and engineered complex systems with NetLogo. MIT Press.(IABM)
2. Hamill, L., & Gilbert, N. (2015). Agent-Based Modelling in Economics. John Wiley & Sons.
(ABME)
3. Jansen M. A., (2013 )Introduction to Agent Based Modelling eBook (ABMMJ)
Additional Resources:
4. Railsback, S. F., & Grimm, V. (2011). Agent-based and individual-based modeling: a practical
introduction. Princeton university press. (ABIM)
5. Tesfatsion, L. (2002). Agent-based computational economics: Growing economies from the bottom
up. Artificial life, 8(1), 55-82.
6. Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of computational economics: agent-based
computational economics (Vol. 2). Elsevier.
7. Ehrentreich, N. (2007). Agent-based modeling: The Santa Fe Institute artificial stock market model
revisited (Vol. 602). Springer Science & Business Media. (ABMSF)
Online- Resources:
8. NetLogo software package and community models are available https://ccl.northwestern.edu/netlogo/
9. The videos are freely available on YouTube
(https://www.youtube.com/channel/UCCqW98YlsST73jnB3WuUjbw).
10. The Santa Fé institute also offers ABM and related courses
(https://www.complexityexplorer.org/courses).
11. ABM by TU Delft (http://wiki-app1.tudelft.nl/bin/view/Education/SPM955xABMofCAS/Spm4530).
12. Other great resources are: the joural of artificial societies and social
simulation (http://jasss.soc.surrey.ac.uk/JASSS.html),
13. A platform (https://www.openabm.org/)on which free working models are posted.
14. Agent Based Computational Economics: http://www2.econ.iastate.edu/tesfatsi/afinance.htm (ABCE)