Limits of Econometrics

Year 2009, Volume: 1 Issue: 1, 5 - 17, 01.04.2009

David A. Freedman

References

• Abbott, A. (1997). Of time and space: The contemporary relevance of the Chicago school. Social Forces, 75, 1149–82.
• Abbott, A. (1998). The causal devolution. Sociological Methods and Research, 27, 148–81.
• Abelson, R.P. (1995). Statistics as Principled Argument. Hillsdale, NJ: Lawrence Erlbaum Associates.
• Achen, C.H. (1982). Interpreting and Using Regression. Sage Publications.
• Achen, C.H. (1986). The Statistical Analysis of Quasi-Experiments. Berkeley: University of California Press.
• Achen, C.H. (2002). Toward a new political methodology: Microfoundations and ART. Annual Review of Political Science, 5, 423–50.
• Angrist, J.D. and A.B. Krueger (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15, 69–85.
• Arceneaux, K., A.S. Gerber and D.P. Green (2006). Comparing experimental and matching methods using a large-scale voter mobilization experiment. Political Analysis, 14, 37– 62.
• Beck, N. (2001). Time-series cross-section data: What have we learned in the past few years? Annual Review of Political Science, 4, 271–93.
• Berk, R.A. (2004). Regression Analysis: A Constructive Critique. Sage Publications.
• Berk, R.A. and D.A. Freedman (2008). On weighting regressions by propensity scores. Evaluation Review, 32, 392–409.
• Bernert, C. (1983). The career of causal analysis in American sociology. British Journal of Sociology, 34, 230–54.
• Brady, H.E., D. Collier and J. Seawright (2004). Refocusing the discussion of methodology. In Rethinking Social Inquiry: Diverse Tools, Shared Standards, ed. H.E. Brady and D. Collier. Lanham, MD: Rowman & Littlefield Publishers, Inc, 3–20.
• Brady, H.E. and D. Collier (2004). Rethinking Social Inquiry: Diverse Tools, Shared Standards. Lanham, MD: Rowman & Littlefield Publishers, Inc, 3–20.
• Clogg, C.C. and A. Haritou (1997). The regression method of causal inference and a dilemma confronting this method. In Causality in Crisis? ed. V.R. McKim and S.P. Turner, University of Notre Dame Press, 83–112.
• Cook, T.D. and D.T. Campbell (1979). Quasi-Experimentation: Design & Analysis Issues for Field Settings. Chicago: Rand McNally.
• Dewald, W.G., J.G. Thursby and R.G. Anderson (1986). Replication in empirical economics: The Journal of Money, Credit and Banking Project. American Economic Review, 76, 587–603.
• Diaconis, P. (1998). A place for philosophy? The rise of modeling in statistics. Quarterly Journal of Applied Mathematics, 56, 797–805.
• Diaconis, P. and D.A. Freedman (1998). Consistency of Bayes estimates for nonparametric regression: Normal theory. Bernoulli Journal, 4, 411–44.
• Eaton, M.L. and D.A. Freedman (2004). Dutch book against some ―objective‖ priors. Bernoulli Journal, 10, 861–72.
• Engle, R.F., D. F. Hendry and J. F. Richard (1983). Exogeneity. Econometrica, 51, 277–304.
• Evans, W.N. and R.M. Schwab (1995). Finishing high school and starting college: Do Catholic schools make a difference? Quarterly Journal of Economics, 110, 941–74.
• Fearon, J. (1991). Counterfactuals and hypothesis testing in political science. World Politics, 43, 169–95.
• Freedman, D.A. (1985). Statistics and the scientific method. In Cohort Analysis in Social Research: Beyond the Identification Problem, ed. W.M. Mason and S.E. Fienberg (with discussion). Springer, 343–90.
• Freedman, D.A. (1987). As others see us: A case study in path analysis. Journal of Educational Statistics, 12, 101–223. Reprinted in The Role of Models in Nonexperimental Social Science, ed. J. Shaffer (1992), Washington, D.C.: American Educational Research Association and American Statistical Association, 3–125.
• Freedman, D.A. (1991). Statistical models and shoe leather. In Sociological Methodology, ed. P. Marsden, chapter 10 (with discussion). Washington, D.C.: American Sociological Association.
• Freedman, D.A. (1995). Some issues in the foundation of statistics. Foundations of Science, 1, 19–83 (with discussion). Reprinted in Topics in the Foundation of Statistics, ed. B.C. van Fraassen (1997). Dordrecht: Kluwer.
• Freedman, D.A. (1997). From association to causation via regression. In Causality in Crisis? ed. V.R. McKim and S.P. Turner (with discussion). University of Notre Dame Press, 113–82. Reprinted in (1997) Advances in Applied Mathematics, 18, 59–110.
• Freedman, D.A. (1999). From association to causation: Some remarks on the history of statistics. Statistical Science, 14, 243–58. Reprinted in (1999) Journal de la Société Française de Statistique, 140, 5–32 and in (2003) Stochastic Musings: Perspectives from the Pioneers of the Late 20th Century, ed. J. Panaretos. Hillsdale, NJ: Lawrence Erlbaum Associates, 45–71.
• Freedman, D.A. (2004). Graphical models for causation, and the identification problem. Evaluation Review, 28, 267–93. Reprinted in (2005) Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg, ed. D.W.K. Andrews and J.H. Stock, Cambridge University Press.
• Freedman, D.A. (2005). Statistical Models: Theory and Practice. Cambridge University Press.
• Freedman, D.A. (2008). Diagnostics cannot have much power against general alternatives. Forthcoming in Journal of Forecasting. http://www.stat.berkeley.edu/users/census /nopower.pdf.
• George, A.L. and A. Bennett (2005). Case Studies and Theory Development in the Social Sciences. MIT Press.
• Gibson, J.L. (1988). Political intolerance and political repression during the McCarthy red scare. Heinz Eulau Award from the American Political Science Association, as best paper published in 1988 in the American Political Science Review. American Political Science Review, 82, 511–29.
• Gigerenzer, G. (1996). On narrow norms and vague heuristics. Psychological Review, 103, 592–96.
• Glazerman, S., D.M. Levy and D. Myers (2003). Nonexperimental versus experimental estimates of earnings impacts. Annals of the American Academy of Political and Social Science, 589, 63–93.
• Goldthorpe, J.H. (1999). Causation, Statistics and Sociology. Twenty-ninth Geary Lecture, Nuffield College, Oxford. The Economic and Social Research Institute: Dublin, Ireland.
• Goldthorpe, J.H. (2000). On Sociology: Numbers, Narratives, and Integration of Research and Theory. Oxford University Press.
• Goldthorpe, J.H. (2001). Causation, statistics, and sociology. European Sociological Review, 17, 1–20.
• Green, D.P. and I. Shapiro (1994). Pathologies of Rational Choice Theory: A Critique of Applications in Political Science. Yale University Press.
• Heckman, J.J. (2000). Causal parameters and policy analysis in economics: A twentieth century retrospective. The Quarterly Journal of Economics, 115, 45–97.
• Hedström, P. and R. Swedberg (1998). Social Mechanisms. Cambridge University Press.
• Hendry, D.F. (1980). Econometrics—Alchemy or Science? Economica, 47, 387–406. Reprinted in D.F. Hendry (2000) chapter 1. Oxford: Blackwell.
• Hodges, J.L.Jr. and E. Lehmann (1964). Basic Concepts of Probability and Statistics. Holden- Day: San Francisco. 2nd ed. reprinted by (2005) SIAM: Philadelphia.
• Holland, P.W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 8, 945–70.
• Holland, P.W. (1988). Causal inference, path analysis, and recursive structural equation models. In Sociological Methodology, ed. C. Clogg (1988), chapter 13. Washington, D.C.: American Sociological Association.
• Hoover, K.D. (2008). Causality in economics and econometrics. In The New Palgrave Dictionary of Economics, ed. S. Durlauf and L.E. Blume. 2nd ed. Macmillan.
• Hubbard, R., D.E. Vetter and E.L. Little (1998). Replication in strategic management: Scientific testing for validity, generalizability, and usefulness. Strategic Management Journal, 19, 243–54.
• Kahneman, D., P. Slovic and A. Tversky (1982). Judgment under Uncertainty: Heuristics and Biases. Cambridge University Press.
• Kahneman, D. and A. Tversky (1974). Judgment under uncertainty: Heuristics and bias. Science, 185, 1124–31.
• Kahneman, D. and A. Tversky (1996). On the reality of cognitive illusions. Psychological Review, 103, 582–91.
• Kahneman, D. and A. Tversky (2000). Choices, Values, and Frames. Cambridge University Press.
• King, G., R.O. Keohane and S. Verba (1994). Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton University Press.
• Keynes, J.M. (1939). Professor Tinbergen‘s method. The Economic Journal, 49, 558–68.
• Keynes, J.M. (1940). Comment [on Tinbergen‘s reply]. The Economic Journal, 50, 154–56.
• Larzalere, R.E., B.R. Kuhn and B. Johnson (2004). The intervention selection bias: An underrecognized confound in intervention research. Psychological Bulletin, 130, 289– 303.
• Leamer, E.E. (1978). Specification Searches. Wiley.
• Lieberson, S. (1985). Making it Count. Berkeley: University of California Press.
• Lieberson, S. and F.B. Lynn (2002). Barking up the wrong branch: Alternatives to the current model of sociological science. Annual Review of Sociology, 28, 1–19.
• Liu, T.C. (1960). Under-identification, structural estimation, and forecasting. Econometrica, 28, 855–65.
• Lomborg, B. (2001). The Skeptical Environmentalist: Measuring the Real State of the World. Cambridge University Press.
• Lucas, R.E.Jr. (1976). Econometric policy evaluation: A critique. In The Phillips Curve and Labor Markets, ed. K. Brunner and A. Meltzer. The Carnegie-Rochester Conferences on Public Policy, supplementary series to the Journal of Monetary Economics (with discussion). Amsterdam: North-Holland, 1, 19–64.
• Mahoney, J. and D. Rueschemeyer (2003). Comparative Historical Analysis in the Social Sciences. Cambridge University Press.
• Manski, C.F. (1995). Identification Problems in the Social Sciences. Harvard University Press.
• McKim, V.R. and S.P. Turner (1997). Causality in Crisis? Proceedings of the Notre Dame Conference on Causality. University of Notre Dame Press.
• Meehl, P.E. (1954). Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence. Minneapolis: University of Minnesota Press.
• Meehl, P.E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46, 806– 34.
• Meehl, P.E. and N.G. Waller (2002). The path analysis controversy: A new statistical approach to strong appraisal of verisimilitude. Psychological Methods (with discussion), 7, 283–337.
• Nelson, R.R. and S.G. Winter (1982). An Evolutionary Theory of Economic Change. Harvard University Press.
• Neyman, J. (1923). Sur les applications de la théorie des probabilités aux experiences agricoles: Essai des principes. Roczniki Nauk Rolniczych (in Polish), 10, 1–51. English translation by D.M. Dabrowska and T.P. Speed (1990). Statistical Science (with discussion), 5, 465–80.
• Ní Bhrolcháin, M. (2001). ―Divorce effects‖ and causality in the social sciences. European Sociological Review, 17, 33–57.
• Oakes, M.W. (1990). Statistical Inference. Chestnut Hill, MA: Epidemiology Resources, Inc.
• Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
• Pilkey, O.H. and L. Pilkey-Jarvis (2006). Useless Arithmetic. Columbia University Press.
• Platt, J. (1996). A History of Sociological Research Methods in America. Cambridge University Press.
• Pratt, J.W. and R. Schlaifer (1984). On the nature and discovery of structure. Journal of the American Statistical Association (with discussion), 79, 9–33.
• Pratt, J.W. and R. Schlaifer (1988). On the interpretation and observation of laws. Journal of Econometrics, 39, 23–52.
• Rindfuss, R.R., L. Bumpass and C.St. John (1980). Education and fertility: Implications for the roles women occupy. American Sociological Review, 45, 431–47.
• Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.
• Scheffé, H. (1956). Alternative models for the analysis of variance. Annals of Mathematical Statistics, 27, 251–71.
• Schneider, M., P. Teske and M. Marschall (1997). Institutional arrangements and the creation of social capital: The effects of public school choice. American Political Science Review, 91, 82–93.
• Sen, A.K. (2002). Rationality and Freedom. Harvard University Press.
• Shadish, W.R., T.D. Cook and D.T. Campbell (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Boston: Houghton Mifflin.
• Sims, C.A. (1980). Macroeconomics and reality. Econometrica, 48, 1–47.
• Sobel, M.E. (1998). Causal Inference in Statistical Models of the Process of Socioeconomic Achievement: A Case Study. Sociological Methods Research, November 1, 27(2), 318– 348.
• Sobel, M.E. (2000). Causal inference in the social sciences. Journal of the American Statistical Association, 95, 647–51.
• Spirtes, P., C. Glymour and R. Scheines (1993). Causation, Prediction, and Search. Springer Lecture Notes in Statistics, 81. 2nd ed. (2000). MIT Press.
• Steiger, J.H. (2001). Driving fast in reverse. Journal of the American Statistical Association, 96, 331–38.
• Stone, R. (1993). The assumptions on which causal inferences rest. Journal of the Royal Statistical Society, Series B, 55, 455–66.
• Tinbergen, J. (1940). On a method of statistical business-cycle research. A reply [to Keynes]. The Economic Journal, 50, 141–54.
• Wilde, E.T. and R. Hollister (2007). How close is close enough? Evaluating propensity score matching using data from a class size reduction experiment. Journal of Policy Analysis and Management, 26, 455–77.