Applied Econometric Techniques for Microeconomic Analysis
- Kredity: 10
- Ukončenie: Skúška
- Rozsah: 32sP
- Semester: letný
- Národohospodárska fakulta
Vyučujúci
Zaradený v študijných programoch
Výsledky vzdelávania
Teaching results:
The course is designed to cover econometric techniques suitable for the study of data for microeconomic applications. Quantitative analysis of microeconomic issues is usually based on individual- and firm-level data available from cross-section and longitudinal sample surveys and census data. Data collection at the individual level has seen a tremendous growth over the years. Processing and econometric analysis of large microdatabasis, with the objective of uncovering patterns of economic behaviour, constitutes the core of microeconomic analysis. In this course, regression methods will be applied to cross-section and panel data that have both, cross sectional and time series dimensions. Problems arising from the use of such data (heterogeneity, endogeneity, sample selection bias, etc.) will be addressed and many applications will be presented.
Knowledge
The course will provide students with applied insights on selected topics and research methods in the field of microeconomics. Students will be able to understand the application of microeconomic problems to particular data and to brings solutions or propose different solutions to microeconomic problems.
Abilities
Students will be able applying knowledge from the field of microeconomics to bring solutions to microeconomic problems by the use of different methods and techniques of economic research. Students will be able to decide, which method is suitable to be applied under different conditions to different problems.
Skills
The course will develop analytical skills in the field of microeconometrics in order to analyze microeconomic data and interpret the results of the analysis. Students will also be able to decide and to choose the appropriate method of micro-data analysis.
Stručná osnova predmetu
Indicative content:
1. Cross-sectional data in microeconomics and regression models used to analyse them (Regression with fixed regressors, Ordinary Least squares (OLS)and other estimation methods. Goodness of fit and hypothesis testing.)
2. Comparison among OLS, GLS, and IV methods for the analysis of micro-data. Heteroskedasticity, generalized least squares (GLS), the endogeneity problem, method of Instrumental Variables (IV), statistical inference.
3. Sample selection corrections of micro-data. Nonlinear regression modelling and Limited Dependent Variables models, Logit and Probit models.
4. Microeconomic time-series and related models. Autoregressive and moving average representations of time series. Stationarity, vector autoregressions and causality.
5. Pooling cross section micro data across time, examining causal effects, the applications of the difference-in-differences estimator:
6. The use of Simple Panel Data models in microeconomic analysis. Variation types (overall, within, and between variation). Panel data models (pooled model, fixed effects model, and random effects model)
7. The use of Panel Data in microeconomic analysis. Panel data estimators (pooled OLS, between, fixed effects, first differences, random effects). Tests for choosing between models (Breusch-Pagan LM test, Hausman test)
8. The use of Dynamic Panel Data models in microeconomic analysis (:Generalized Method of Moments (GMM), specification tests, System GMM method.)
Odporúčaná literatúra
Literature:
Angrist, J.D. and A.B. Keueger (1991). Does Compulsory School Attendance Affect Schooling and Earnings? The Quarterly Journal of Economics, Volume 106, Issue 4, Pages 979–1014, https://doi.org/10.2307/2937954
Arellano, M. and S. Bond. (April 1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58. pp. 277 – 297.
Baum, C.F. (2006). An Introduction to Modern Econometrics Using Stata, Stata Press Publication (https://www.stata-press.com/data/imeus.html)
Budd, J.W and B.P. McCall (1997). The Effect of Unions on the Receipt of Unemployment Insurance Benefits, Industrial and Labor Relations Review, Vol. 50, No. 3.
Bruderl, J. (2005). Panel Data Analysis, University of Mannheim, March 2005
Cameron, A.C. (2007). Panel data methods for microeconometrics using Stata, Stata Press.
Cameron A.C. and P.K. Trivedi (2010). Microeconometrics Using Stata, Revised Edition, Stata Press Publication (https://www.stata-press.com/data/imeus.html)
Card, D. and A.B. Krueger (1994). Minimum wages and Employment: A case study of the fast-food industry in New Jersey and Pennsylvania, American Economic Review, Vol. 84, No 4.
Dinardo, J.E. and J. Pischke (1999). The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too? The Quarterly Journal of Economics, Feb., 1997, Vol. 112, No. 1
Djankov, S. and B. Hoekman (2000). Foreign investment and productivity growth in Czech enterprises, World Bank Economic Review, 14(1), 49-64.
Nichols, Austin (2007). Causal inference with observational data. Stata Journal 7(4): 507-541.
Papaioannou, S. and S. Dimelis (2018). Does FDI increase productivity? The role of regulation in upstream industries, World Economy, 1-20. DOI: 10.1111/twec12749.
Roodman, D. (2006). How to Do xtabond2: An Introduction to “Difference” and “System” GMM in Stata, Center for Global Development, Working Paper No103.
Stef, N. and S. Dimelis (2020). Bankruptcy regime and the banking system, Economic Modelling, Vol. 87, 480-495
Torres-Reyna, Oscar (2007). Panel Data Analysis, Fixed and Random Effects using Stata (ver. 4.2), Princeton University, https://dss.princeton.edu/training/Panel101.pdf
Torres-Reyna, Oscar (2015). Differences-in-Differences (using Stata), Princeton University, https://dss.princeton.edu/training/DID101.pdf
Torres-Reyna, Oscar. Getting Started in Logit and Ordered Logit Regression (ver. 3.1 beta), Princeton University, https://dss.princeton.edu/training/Logit.pdf
Torres-Reyna, Oscar. Linear Regression (ver. 6.0), Princeton University, https://dss.princeton.edu/training/Regression101.pdf
Torres-Reyna, Oscar. Time Series (ver. 1.5), Princeton University, https://dss.princeton.edu/training/TS101.pdf
Waldinger, Fabian, Instrumental Variables, https://www.fabianwaldinger.com/applied-econometrics
Wooldridge, J. (2007). What’s New in Econometrics? Difference-in-Differences Estimation, NBER Summer Institute. https://www.nber.org/lecture/difference-differences-estimation
Podmienky na absolvovanie predmetu
Requirements to complete the course:
10% - course attendance and activity
40% - four practical problem sets (4 x 10%)
50% - empirical application with micro-data
Time allocation: 260 hours
32 hours– contact hours
112 hours – preparation of four practical problem sets
116 hours – preparation of empirical application with micro-data
Dátum schválenia: 13.02.2023
Dátum poslednej zmeny: 22.09.2021
Dátum schválenia: 13.02.2023
Dátum poslednej zmeny: 22.09.2021