Monash University’s Department of Econometrics and Business Statistics, where I did my PhD and worked from 2007-2012, defines econometrics as “a set of quantitative techniques that are useful for making ‘economic decisions’”. That’s about as good a definition as any. Literally, ‘metrics’ has to do with measurement. Hence, econo-metrics literally means ‘measuring stuff to do with the economy’. So the focus is on the appropriate use of quantitative techniques, but their application of course, goes way beyond just economics.
Many students are intimidated by econometrics, but as I used to tell my first years – there are many good reasons for studying statistics and econometrics, and one of them is that with a decent knowledge of statistics, you’ll have a much better chance of recognising when some government, corporation or individual is trying to deceive you! More positively, a good grounding in statistics helps you develop and engage with an evidence-based approach to research and policy.
There are loads of good books and resources of course. Below I’ve listed a small sample, with an emphasis on R, “a free software environment for statistical computing and graphics”.
Books
Books on R
Papers & Chapters
Papers on R
Links
R Links
Belsley, D.A., Kuh, E. and Welsch, R.E., (1980) Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, John Wiley & Sons, New York & Chichester, 292 pp.
Durlauf, S.N. and Blume, L.E. (Eds.), (2010) Macroeconometrics and Time Series Analysis, The New Palgrave Economics Collection; Palgrave Macmillan, Houndmills, Basingstoke & New York, x + 406 pp.
Durlauf, S.N. and Blume, L.E. (Eds.), (2010) Microeconometrics, The New Palgrave Economics Collection; Palgrave Macmillan, Houndmills, Basingstoke & New York, x + 354 pp.
Hsiao, C., (2002) Analysis of Panel Data, 2nd Edition; Cambridge University Press, Cambridge, 368 pp.
Hyndman, R.J., Koehler, A.B., Ord, J.K. and Snyder, R.D., (2008) Forecasting with Exponential Smoothing: The State Space Approach, Springer-Verlag, Berlin & Heidelberg, xiii + 359 pp.
Kanji, G.K., (2006) 100 Statistical Tests, 3rd Edition; Sage Publications, Los Angeles & London, viii + 242 pp.
Kennedy, P., (2008) A Guide to Econometrics, 6th Edition; Blackwell Publishing, Malden MA, Oxford UK & Carlton VIC, xii + 585 pp.
Mukherjee, C., White, H. and Wuyts, M., (1998) Econometrics and Data Analysis for Developing Countries, Routledge, London and New York, xviii + 496 pp.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M. and Tarantola, S., (2008) Global Sensitivity Analysis: The Primer, John Wiley & Sons, Chichester, x + 292 pp.
Wooldridge, J.M., (2010) Econometric Analysis of Cross Section and Panel Data, 2nd Edition; The MIT Press, Cambridge MA & London, xxvii + 1064 pp.
Ziliak, S.T. and McCloskey, D.N., (2007) The Cult of Statistical Significance: How the Standard Error is Costing Jobs, Justice, and Lives, University of Michigan Press, Ann Arbor, MI, 480 pp.
Adler, J., (2010) R in a Nutshell: A Quick Desktop Reference, O’Reilly, Sebastopol, CA, xx + 611 pp.
Bivand, R.S., Pebesma, E.J. and Gómez-Rubio, V., (2008) Applied Spatial Data Analysis with R, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer Science+Business Media, New York, xiv + 374 pp.
Cowpertwait, P.S.P. and Metcalfe, A., (2009) Introductory Times Series with R, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer Science+Business Media, New York, xv + 254 pp.
Dalgaard, P., (2008) Introductory Statistics with R, 2nd Edition; Springer, Dordrecht, Heidelberg, London & New York, xvi + 363 pp.
Everitt, B.S. and Hothorn, T., (2010) A Handbook of Statistical Analyses Using R, 2nd Edition; CRC Press, Boca Raton FL, London & New York, 355 pp.
Jones, O., Maillardet, R. and Robinson, A., (2009) Introduction to Scientific Programming and Simulation Using R, Chapman & Hall / CRC Press, Boca Raton FL, London & New York, xix + 453 pp.
Kleiber, C. and Zeileis, A., (2008) Applied Econometrics with R, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer, Dordrecht, Heidelberg, London & New York, x + 221 pp.
Maindonald, J. and Braun, W.J., (2010) Data Analysis and Graphics Using R: An Example-Based Approach, 3rd Edition; Cambridge Series in Statistical and Probabilistic Mathematics; Cambridge University Press, Cambridge, xxvi + 525 pp.
Mittal, H.V., (2011) R Graphs Cookbook, Packt Publishing, Birmingham & Mumbai, iv + 255 pp.
Pfaff, B., (2008) Analysis of Integrated and Cointegrated Time Series with R, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer Science+Business Media, New York, xx + 188 pp.
Sarkar, D., (2008) Lattice: Multivariate Data Visualization with R, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer Science+Business Media, New York, xvii + 273 pp.
Shumway, R.H. and Stoffer, D.S., (2011) Time Series Analysis and Its Applications: With R Examples, 3rd Edition; Springer Science+Business Media, New York, Dordrecht, Heidelberg & London, viii + 596 pp.
Spector, P., (2008) Data Manipulation with R, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer Science+Business Media, New York, ix + 152 pp.
Teetor, P., (2011) R Cookbook, O’Reilly, Sebastopol CA, xviii + 413 pp.
Torgo, L., (2010) Data Mining with R: Learning with Case Studies, Chapman & Hall / CRC Data Mining and Knowledge Discovery Series; Chapman & Hall / CRC Press, Boca Raton FL, London & New York, xv + 289 pp.
Wickham, H., (2009) ggplot2: Elegant Graphics for Data Analysis, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer Science+Business Media, New York, viii + 212 pp.
Zuur, A.F., Ieno, E.N. and Meesters, E.H.W.G., (2010) A Beginner’s Guide to R, Series ed. Gentleman, R., Hornik, K. and Parmigiani, G.; Use R! series; Springer, Dordrecht, Heidelberg, London & New York, xv + 218 pp.
Arellano, M. and Honoré, B., (2001) “Panel Data Models: Some Recent Developments”, In Handbook of Econometrics ed. Heckman, J.J. and Leamer, E.; Elsevier, Amsterdam, Vol. 5 pp. 3229-3296.
Backhouse, R.E. and Morgan, M.S., (2000) “Introduction: Is Data Mining a Methodological Problem?” Journal of Economic Methodology, Vol. 7, No. 2, June, pp. 171-181.
Banks, D.L., (2010) “Statistical Data Mining”, WIREs Computational Statistics, Vol. 2, January – February, pp. 9-25.
Barthélemy, M., Nadal, J.-P. and Berestycki, H., (2010) “Disentangling Collective Trends from Local Dynamics“, Proceedings of the National Academy of Sciences of the United States of America, Vol. 107, No. 17, 27 April, pp. 7629-7634.
Bollen, K.A. and Jackman, R.W., (1990) “Regression Diagnostics: An Expository Treatment of Outliers and Influential Cases”, In Modern Methods of Data Analysis ed. Fox, J. and Long, J.S.; Sage Publications, London, Newbury Park, New Delhi, pp. 257-291.
Crowley, P.M., (2007) “A Guide to Wavelets for Economists”, Journal of Economic Surveys, Vol. 21, No. 2, April, pp. 207-267.
Diewert, W.E., (1987) “Index Numbers”, In The New Palgrave: A Dictionary of Economics ed. Eatwell, J., Milgate, M. and Newman, P.; MacMillan Press, London; Stockton Press, New York & Maruzen Company, Tokyo, Vol. 2, pp. 767-780.
Elhorst, J.P., (2010) “Applied Spatial Econometrics: Raising the Bar“, Spatial Economic Analysis, Vol. 5, No. 1, March, pp. 9-28.
Frondel, M. and Vance, C., (2010) “Fixed, Random, or Something in Between? A Variant of Hausman’s Specification Test for Panel Data Estimators”, Economics Letters, Vol. 107, No. 3, June, pp. 327-329.
Fu, T.-c., (2011) “A Review on Time Series Data Mining”, Engineering Applications of Artificial Intelligence, Vol. 24, No. 1, February, pp. 164-181.
Heckman, J.J., (2008) “Econometric Causality”, International Statistical Review, Vol. 76, No. 1, April, pp. 1-27.
Heckman, J.J. and Urzúa, S., (2010) “Comparing IV with Structural Models: What Simple IV Can and Cannot Identify”, Journal of Econometrics, Vol. 156, No. 1, May, pp. 27-37.
Hurlin, C., (2010) “What Would Nelson and Plosser Find had they Used Panel Unit Root Tests?” Applied Economics, Vol. 42, No. 12, pp. 1515 – 1531.
Hyndman, R.J. and Koehler, A.B., (2006) “Another Look at Measures of Forecast Accuracy”, International Journal of Forecasting, Vol. 22, No. 4, October – December, pp. 679-688.
Inder, B., (1993) “Estimating Long-Run Relationships in Economics: A Comparison of Different Approaches”, Journal of Econometrics, Vol. 57, No. 1/2/3, May – June, pp. 53-68.
Kalton, G., (2000) “Developments in Survey Research in the Last 25 Years”, Survey Methodology, Vol. 26, No. 1, June, pp. 3-10.
Kennedy, P.E., (1998) “Teaching Undergraduate Econometrics: A Suggestion for Fundamental Change”, American Economic Review, Vol. 88, No. 2, May, pp. 487-491.
Kennedy, P.E., (2002) “Sinning in the Basement: What are the Rules? The Ten Commandments of Applied Econometrics.” Journal of Economic Surveys, Vol. 16, No. 4, September, pp. 569-589.
Leamer, E.E., (1983) “Let’s Take the Con Out of Econometrics”, American Economic Review, Vol. 73, No. 1, March, pp. 31-43.
McCloskey, D.N., (1985) “The Loss Function Has Been Mislaid: The Rhetoric of Significance Tests”, American Economic Review, Vol. 75, No. 2, May, pp. 201-05.
McCloskey, D.N., (1992) “The Bankruptcy of Statistical Significance”, Eastern Economic Journal, Vol. 18, No. 3, Summer, pp. 359-361.
McCloskey, D.N., (1995) “The Insignificance of Statistical Significance”, Scientific American, Vol. 272, No. 4, April, pp. 32-33.
McCloskey, D.N. and Ziliak, S.T., (1996) “The Standard Error of Regressions”, Journal of Economic Literature, Vol. 34, No. 1, March, pp. 97-114.
Oxley, L., (2002) “Making and Breaking Rules in Applied Econometrics”, Journal of Economic Surveys, Vol. 16, No. 4, September, pp. 565-567.
Reed, W.R. and Ye, H., (2009) “Which Panel Data Estimator Should I Use?” Applied Economics, published early online 16 July, pp. 1-16.
Rodrik, D., (2005) “Why We Learn Nothing from Regressing Economic Growth on Policies“, Working Paper, March, 14 pp.
Trapani, L. and Urga, G., (2010) “Micro versus Macro Cointegration in Heterogeneous Panels”, Journal of Econometrics, Vol. 155, No. 1, March, pp. 1-18.
White, H., (2002) “Combining Quantitative and Qualitative Approaches in Poverty Analysis”, World Development, Vol. 30, No. 3, March, pp. 511-522.
Wilson, S.E. and Butler, D.M., (2007) “A Lot More to Do: The Sensitivity of Time-Series Cross-Section Analyses to Simple Alternative Specifications”, Political Analysis, Vol. 15, No. 2, Spring, pp. 101-123.
Zeileis, A., (2005) “A Unified Approach to Structural Change Tests Based on ML Scores, F Statistics, and OLS Residuals”, Econometric Reviews, Vol. 24, No. 4, pp. 445-466.
Zellner, A., (2007) “Philosophy and Objectives of Econometrics”, Journal of Econometrics, Vol. 136, No. 2, February, pp. 331-339.
Croissant, Y. and Millo, G., (2008) “Panel Data Econometrics in R: The plm Package“, Journal of Statistical Software, Vol. 27, No. 2, July. For longer version see here.
Goodreau, S.M., Handcock, M.S., Hunter, D.R., Butts, C.T. and Morris, M., (2008) “A statnet Tutorial“, Journal of Statistical Software, Vol. 24, No. 9, May, pp. 1-26.
Hyndman, R.J. and Khandakar, Y., (2008) “Automatic Time Series Forecasting: The forecast Package for R“, Journal of Statistical Software, Vol. 27, No. 3, July, pp. 1-22.
Jara, A., Hanson, T., Quintana, F.A., Müller, P. and Rosner, G.L., (2011) “DPpackage: Bayesian Semi- and Nonparametric Modeling in R“, Journal of Statistical Software, Vol. 40, No. 5, April, pp. 1-30.
Petris, G. and Petrone, S., (2011) “State Space Models in R“, Journal of Statistical Software, Vol. 41, No. 4, May, pp. 1-25.
Zeileis, A., (2004) “Econometric Computing with HC and HAC Covariance Matrix Estimators“, Journal of Statistical Software, Vol. 11, No. 10, November, pp. 1-17.
Zeileis, A., Kleiber, C. and Jackman, S., (2008) “Regression Models for Count Data in R“, Journal of Statistical Software, Vol. 27, No. 8, November, pp. 1-25.
Wickham, H., Cook, D., Hofmann, H. and Buja, A., (2011) “tourr: An R Package for Exploring Multivariate Data with Projections“, Journal of Statistical Software, Vol. 40, No. 2, April, pp. 1-18.
EconometricsLinks – Econometric Links of the Econometrics Journal
Monash University – Department of Econometrics & Business Statistics
Statistical Analysis – Stack Exchange – A ‘Q&A for statisticians, data analysts, data miners and data visualization experts’
STATA – A sophisticated package, but you have to pay for it.
The R-Project for Statistical Computing – R is excellent, powerful and free!
The Comprehensive R Archive Network (CRAN)
CRAN Task Views: Computational Econometrics
Statistics for the Social Sciences
The R Journal (free)
R Tools
Rattle – Data mining
R Studio – My preferred IDE.
rJava – Low-level R to Java interface
R Tutorials, Tips & Resources
The Comprehensive R Archive Network
Last updated: 6 July 2017