Methods Classes in Spring 06 

 With the end of the Fall semester comes the happy time of shopping for (applied) quantitative methods courses for the Spring. Here's a partial list for currently planned offerings around Cambridge, and their descriptions. 


   Bio 503 Introduction to Programming and Statistical Modeling in R (Harezlak, Paciorek and Houseman)   

 An introduction into R in 5 3-hour sessions combining demonstration, lecture, and laboratory components. It will be graded pass/fail on the basis of homework assignments. Taught in the Winter session at HSPH. 

   Gov 2001 Advanced Quantitative Research Methodology (King) 
   

 Introduces theories of inference underlying most statistical methods and how new approaches are developed. Examples include discrete choice, event counts, durations, missing data, ecological inference, time-series cross sectional analysis, compositional data, causal inference, and others. Main assignment is a research paper to be written alongside the class. 

   Econ 2120. Introduction to Applied Econometrics (Jorgenson) 
   

 Introduction to methods employed in applied econometrics, including linear regression, instrumental variables, panel data techniques, generalized method of moments, and maximum likelihood. Includes detailed discussion of papers in applied econometrics and computer exercises using standard econometric packages. Note: Enrollment limited to certain PhD candidates, check the website. 

  MIT 14.387 Topics in Applied Econometrics (Angrist and Chernozhukov) 
   Click here for 2004 website  

 Covers topics in econometrics and empirical modeling that are likely to be useful to applied researchers working on cross-section and panel data applications.  
[It's not clear whether this class will be offered in Spring 06. Check the MIT class pages for updates. 

  KSG API-208 Program Evaluation: Estimating Program Effectiveness with Empirical Analysis (Abadie) 
   Accessible from here (click on Spring Schedule)  

 Deals with a variety of evaluation designs (from random assignment to quasi-experimental evaluation methods) and teaches analysis of data from actual evaluations, such as the national Job Training Partnership Act Study. The course evaluates the strengths and weaknesses of alternative evaluation methods. 

  KSG PED-327 The Economic Analysis of Poverty in Poor Countries (Jensen) 
   Accessible from here (click on Spring Schedule)  

 Emphasizes modeling behavior, testing economic theories, and evaluating the success of policy. Topic areas include: conceptualizing and measuring poverty, inequality, and well-being; models of the household and intra-household allocation; risk, savings, credit, and insurance; gender and gender inequality; fertility; health and nutrition; and education and child labor. 

   Stat 221 Statistical Computing Methods (Goswami) 
   

 Advanced methods of fitting frequentists and Bayesian models. Generation of random numbers, Monte Carlo methods, optimization methods, numerical integration, and advanced Bayesian computational tools such as the Gibbs sampler, Metropolis Hastings, the method of auxiliary variables, marginal and conditional data augmentation, slice sampling, exact sampling, and reversible jump MCMC. 

   Stat 232 Incomplete Multivariate Data (Rubin) 
   

 Methods for handling incomplete data sets with general patterns of missing data, emphasizing the likelihood-based and Bayesian approaches. Focus on the application and theory of iterative maximization methods, iterative simulation methods, and multiple imputation. 

   Stat 245 Quantitative Social Science, Law, Expert Witnesses, and Litigation (Stephenson and Rubin) 
   

 Explores the relationship between quantitative methods and the law via simulation of litigation and a short joint (law student and quantitative student) research project. Cross-listed with Harvard Law School. 

   Stat 249 Generalized Linear Models (Izem) 
   

 Methods for analyzing categorical data. Visualizing categorial data, analysis of contingency tables, odds ratios, log-linear models, generalized linear models, logistic regression, and model diagnostics.