Abstract
Since the late 1940s, statistical analysis has been increasingly applied to economics discipline. As a result, today’s macroeconometrics is essentially a synonym for macroeconomics. Is it sound to apply probability laws to macroeconomics? What is the consequence of econometricalization? What is the scientific way to conduct macroeconomic research? This chapter attempts to answer these questions.
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Notes
- 1.
Granger differentiated Granger causality from causality, i.e. A Granger-causes B does not mean A causes B. This just causes more confusion. If Granger causality is not causality, the Granger causality test has no ability to solve the spurious regression issue, thus the test itself is pointless.
References
Beall, A. T., & Tracy, J. L. (2013). Women Are More Likely to Wear Red or Pink at Peak Fertility. Psychological Science, 24, 1837–1841.
Brown, P. (1972). The Underdevelopment of Economics. Economic Journal, 82, 1–10.
Bunting, D. (1989). The Consumption Function “Paradox”. Journal of Post Keynesian Economics, 11(3), 347–359.
Campbell, W. W., Barton, M. L., Jr., Cyr-Campbell, D., Davey, S. L., Beard, J. L., Parise, G., & Evans, W. J. (1999). Effects of an Omnivorous Diet Compared with a Lactoovovegetarian Diet on Resistance-Training-Induced Changes in Body Composition and Skeletal Muscle in Older Men. American Journal of Clinical Nutrition, 70(6), 1032–1039.
Chatterjee, S. K. (2002). Statistical Thought: A Perspective and History. Oxford: Oxford University Press.
Cooley, T., & Leroy, S. (1985). Atheoretical Macroeconometrics: A Critique. Journal of Monetary Economics, 16, 283–368.
Creighton, C. (1894). A History of Epidemics in Britain. Cambridge: Cambridge University Press.
Donohue, J. J., III, & Levitt, S. D. (2001). The Impact of Legalized Abortion on Crime. Quarterly Journal of Economics, 116(2), 379–420.
Duesenberry, J. (1949). Income, Saving, and the Theory of Consumer Behaviour. Cambridge, MA: Harvard University Press.
Durante, K. M., Rae, A., & Griskevicius, V. (2013). The Fluctuating Female Vote: Politics, Religion, and the Ovulatory Cycle. Psychological Science, 24(6), 1007–1016.
Eyler, J. (2001). The Changing Assessments of John Snow’s and William Farr’s Cholera Studies. History of Epidemiology, 46(4), 225–232.
Eyler, J. (2013). Commentary: Confronting Unexpected Results: Edmund Parkes Reviews John Snow. International Journal of Epidemiology, 42, 1562–1565.
Farr, W. (1852). Influence of Elevation on the Fatality of Cholera. Journal of the Statistics Society of London, 15(2), 155–183.
Fisher, R. A. (1947). Design of Experiments (4th ed.). London: Oliver and Boyd.
Foote, C., & Goetz, C. (2008). The Impact of Legalized Abortion on Crime: Comment. The Quarterly Journal of Economics, 123(1), 407–423.
Freedman, D. (1995). Some Issues in the Foundation of Statistics. Foundations of Science, 1, 19–83.
Freedman, D. (1999). From Association to Causation: Some Remarks on the History of Statistics. Statistical Science, 14, 243–258.
Freedman, D. (2005). Statistical Models: Theory and Practice. Cambridge: Cambridge University Press.
Friedman, M. (1948). Memorandum About the Possible Value of the CC’s Approach Toward the Study of Economic Fluctuations, Rockefeller Archive.
Friedman, M. (1951). Comment, in Conference on Business Cycles (pp. 107–114). New York: Naitonal Bureau of Economic Research.
Friedman, M. (1953). The Methodology of Positive Economics. In M. Friedman (Ed.), Essays in Positive Economics (pp. 3–43). Chicago, IL: University of Chicago Press.
Friedman, M. (1957). A Theory of Consumption Function. Princeton, NJ: Princeton University Press.
Frisch, R. (1934). Statistical Confluence Analysis by Means of Complete Regression Systems. Oslo: Institute of Economics.
Gelman, A., & Loken, E. (2014). The Statistical Crisis in Science. American Scientist, 102, 460–465.
Granger, C. (1980). Testing for Causality: A Personal Viewpoint. Journal of Economic Dynamic and Control, 2(4), 329–352.
Haavelmo, T. (1943). Statistical Testing of Business Cycle Theories. Review of Economics and Statistics, 25, 13–18.
Haavelmo, T. (1944). The Probability Approach in Econometrics. Econometrica, 12(Supplement), iii–115.
Hacking, I. (1975). The Emergence of Probability. Cambridge: Cambridge University Press.
Hald, A. (1990). A History of Probability and Statistics and Their Applications Before 1750. New York: Wiley.
Hald, A. (1998). A History of Mathematical Statistics from 1750 to 1930. New York: Wiley.
Hansen, L. P., & Singleton, K. J. (1982). Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models. Econometrica, 50(5), 1269–1286.
Hansen, L. P., & Singleton, K. J. (1983). Stochastic Consumption, Risk Aversion, and the Temporal Behavior of Asset Returns. Journal of Political Economy, 91(2), 249–265.
Heckman, J. (2000). Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective. The Quarterly Journal of Economics, 115, 45–97.
Hendry, D. (1980). Econometrics: Alchemy or Science? Economica, 47, 387–406.
Hendry, D. (1993). Econometrics: Alchemy or Science? Essays in Econometric Methodology. Oxford: Blackwell.
Hendry, D., & Mizon, G. (2014). Unpredictability in Economic Analysis, Econometric Modelling and Forecasting. Journal of Econometrics, 182, 186–195.
Hendry, D. F., & von Ungern-Sternberg, T. (1980). Liquidity and Inflation Effects on Consumer’s Expenditure. In A. S. Deaton (Ed.), Essays in the Theory and Measurement of Demand. Cambridge: Cambridge University Press.
Hume, D. (1739 [1888]). Treatise of Human Nature (L. A. Selby-Bigge, Ed.). Oxford: Clarendon Press.
Hume, D. (1748). An Enquiry Concerning Human Understanding. Harvard Classics Volume 37. New York: P. F. Collier & Son.
Joyce, T. (2004). Did Legalized Abortion Lower Crime? Journal of Human Resources, 39(1), 1–28.
Joyce, T. (2009). A Simple Test of Abortion and Crime. The Review of Economics and Statistics, 91, 112–123.
Keuzenkamp, H. (1995). The Econometrics of the Holy Grail. Journal of Economic Surveys, 9, 233–248.
Keynes, J. (1939). Professor Tinbergen’s Method. Economic Journal, 49, 558–568.
Keynes, J. (1940). Comment. Economic Journal, 50, 154–156.
Kling, A. (2011). Macroeconometrics: The Science of Hubris. Critical Review, 23, 123–133.
Krugman, P. (2009, September 6). How Did Economists Get It So Wrong? The New York Times.
Kuhn, T. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press.
Leamer, E. (1978). Specification Searches: Ad Hoc Inference with Non-experimental Data. New York: Wiley.
Leamer, E. (1983). Let’s Take the Con Out of Econometrics. American Economic Review, 73, 31–43.
Leontief, W. (1971). Theoretical Assumptions and Non-observed Facts. American Economic Review, 61, 1–7.
Liu, T. (1960). Under-Identification, Structural Estimation, and Forecasting. Econometrica, 28, 855–865.
Lott, J., & Whitley, J. (2007). Abortion and Crime: Unwanted Children and Out-of-Wedlock Births. Economic Inquiry, 45(2), 304–324.
Lucas, R. (1976). Econometric Policy Evaluation: A Critique. In K. Brunner & A. Meltzer (Eds.), Carnegie Rochester Conference Series on Public Policy (Vol. 1, pp. 19–46). Amsterdam: North-Holland.
Mason, S. (1962). A History of the Sciences. New York: Collier Books.
Modigliani, F. (1986). Life Cycle, Individual Thrift, and the Wealth of Nations. American Economic Review, 76, 297–313.
Moore, H. (1911). Laws of Wages: An Essay in Statistical Economics. New York: The Macmillan Company.
Moore, H. (1914). Economic Cycles: Their Law and Causes. New York, NY: MacMillan.
Moore, H. (1917). Forecasting the Yield and Price of Cotton. New York: The Macmillan Company.
Moosa, I. A. (2017). Econometrics as a Con Art. UK: Edward Elgar Publishing.
Morgenstern, O. (1950). On the Accuracy of Economic Observations. Princeton: Princeton University Press.
Nell, E., & Errouaki, K. (2013). Rational Econometric Man, Transforming Structural Econometrics. Aldershot: Edward Elgar.
Parkes, E. (1855). Mode of Communication of Cholera, by John Snow, M.D. (2nd ed.). British and Foreign Medico-Chirurgical Review, 15, 456.
Pearson, K. (1892). The Grammar of Science. London: Walter Scott.
Pearson, K. (1910). The Grammar of Science (3rd ed.). Edinburgh: Black.
Pearson, K., & Lee, A. (1897). On the Distribution of the Frequency (Variation and Correlation) of the Barometric Heights at Divers Stations. Philosophical Transactions of the Royal Society of London, 190, 423–469.
Pearson, K., Lee, A., & Bramley-Moore, L. (1899). Genetic (Reproductive) Selection: Inheritance of Fertility in Man, and of Fecundity in Thoroughbred Racehorses. Philosophical Transactions of the Royal Society of London, Series A, 192, 257–330.
Pesaran, M. (1987). The Limits to Rational Expectations. Oxford: Basil Blackwell.
Petersen, M. B., Sznycer, D., Sell, A., Tooby, J., & Cosmides, L. (2013). The Ancestral Logic of Politics: Upper Body Strength Regulates Men’s Assertion of Self-Interest Over Income Redistribution. Psychological Science, 24(7), 1098–1103.
Popper, K. (1963). Conjectures and Refutations. London: Routledge.
Reid, D. (1977, May). Public Sector Debt, Economic Trends, pp. 100–107.
Sailer, S. (2005). Abortion and Crime: Sailer Responds to Steven “Freakonomics” Levitt’s Response. http://www.unz.com/isteve/abortion-and-crime-sailer-responds-to/.
Sell, A., Cosmides, L., Tooby, J., Sznycer, D., von Rueden, C., & Gurven, M. (2009). Human Adaptations for the Visual Assessment of Strength and Fighting Ability from the Body and Face. Proceedings of the Royal Society, 276, 575–584.
Shapter, T. (1849). The History of the Cholera in Exeter in 1832. London: John Churchill.
Simmons, J., Nelson, L., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allow Presenting Anything as Significant. Psychological Science, 22, 1359–1366.
Sims, C. (1980). Macroeconomics and Reality. Econometrica, 48, 1–48.
Skidelski, R. (2009, August 6). How to Rebuild a Shamed Subject. Financial Times.
Snow, J. (1849). On the Mode of Communication of Cholera. London: John Churchill.
Snow, J. (1855). On the Mode of Communication of Cholera (2nd ed.). London: John Churchill.
Solow, R. (2010). Statement of Robert M. Solow. In Building a Science of Economics for the Real World (pp. 12–15). U.S Government Printing Office. http://www.gpo.gov/fdsys/pkg/CHRG-111hhrg57604/pdf/CHRG-111hhrg57604.pdf.
Spanos, A. (2011). Foundational Issues in Statistical Modelling: Statistical Model Specification and Validation. Rationality, Markets and Morals, 2, 146–178.
Summers, L. (1991). The Science Illusion in Empirical Macroeconomics. The Scandinavian Journal of Economics, 93, 19–148.
Suppes, P. (1970). A Probabilistic Theory of Causality (Acta Philosophica Fennica). Amsterdam: North-Holland.
Tinbergen, J. (1939). Statistical Testing of Business Cycle Theories: Part I: A Method and Its Application to Investment Activity. New Work: Agaton Press.
Tinbergen, J. (1940). On a Method of Statistical Business Cycle Research, A Reply. Economic Journal, 50, 141–154.
Wallis, K. (1977). Multiple Time Series Analysis and the Final Form of Econometric Models. Econometrica, 45, 1481–1497.
Wickens, M. (1982). The Efficient Estimation of Econometric Models with Rational Expectations. Review of Economic Studies, 49, 55–68.
Worswick, G. (1972). Is Progress in Economic Science Possible? Economic Journal, 82, 73–86.
Yule, G. U. (1926). Why Do We Sometimes Get Nonsense Correlations Between Time Series?—A Study in Sampling and the Nature of Time Series. Journal of the Royal Statistical Society, 89(1), 1–64.
Ziliak, S. T., & McCloskey, D. N. (2008). The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. Ann Arbor, MI: The University of Michigan Press.
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Appendix (for Section 3.6.2): Why Econometric Models Fail: An Illustration
Appendix (for Section 3.6.2): Why Econometric Models Fail: An Illustration
To demonstrate the performance of macroeconometric models, the author uses the US macroeconomic time series data 1969–2013 (see Table 3.2 at the end of appendix) to estimate the GDP identity, i.e. the expenditure and income sides of GDP. Since there are some statistical discrepancies in the two sides of the GDP, one must choose one side as the GDP value. The author chooses the income side of the GDP. Based on the 1-lagged GDP and the variables on both sides of the GDP, the author uses the OLS method to estimate 7 models. Here, the author has an upper hand over even the most experienced macroeconometricians and can dismiss any criticism on modelling techniques because we have a complete list of all factors and know the true mechanism (the correct function of the GDP equation). The estimated results for various models are shown in Table 3.1. For the benefit of non-econometricians, the author has not only listed the coefficient and standard error for each variable but also listed the p-value.
Model 1 use income-side data to estimate the GDP identity. The estimated results for Model 1 are perfect: the adjusted R-squared is 1, all variables on the income side of the GDP are extremely significant (p=0.000) and the coefficients are extremely close to 1. The coefficient for the constant is close to zero with a very high p-value (p=0.732), indicating there is no constant term in the model. These are exactly what is predicted by the GDP identity:
GDP=Wage +Tax + Profit + Capital Formation.
One may hail that the macroeconometric model works! However, this is not the usual case in macroeconometric modelling and the model has worked because the assumptions for estimating an econometric model held. With perfect theoretic knowledge we know that all variables are included in the model so the conditions for random experiments hold. We also know perfectly well that we have the right function for the model. More importantly, the data perfectly fit in with the GDP identity equation except for very tiny rounding errors (about US$0.1 billion for a magnitude of US$1018–16980 billion GDP) for some years, so the OLS method can find the best fit.
Model 2 uses the income-side GDP data to estimate the GDP identity on the expenditure side:
GDP= Consumption + Investment + Net Export + Government Spending.
With perfect knowledge, this model also includes all variables and uses the right function. However, the data do not fit the equation closely because of the statistic discrepancy (measurement error) on both sides of the GDP. The measurement error causes much damage to the estimation. Although R-squared is still very high (0.9999) and most explanatory variables are significant, the results are quite far from the truth: all coefficients are not close to 1. The marginal contribution of consumption is overestimated while the marginal contribution of investment and net export is underestimated; the marginal contribution of government spending to the GDP is only about 16%. Compared with the true marginal contribution of 100% based on our perfect knowledge, the estimated marginal contribution of government spending discounts the true value by more than 80%. Effectively, the marginal contribution of government spending is insignificant even if one uses the 10% p-value as a benchmark of rejection of significance of government spending. Moreover, the constant should be zero but modelling results show it is very significant.
Model 3 estimates the impact of a lagged GDP on current GDP to illustrate the common practice of using lagged variables in macroeconometric modelling. The estimation shows a very high R-squared (0.9984) and a very significant impact of past GDP. In fact, the coefficient of 1-lagged GDP is close to (or slightly greater than) 1. This confirms the view that most macroeconomic variables are non-stationary. However, the unit root tests on GDP and other variables are mixed, depending on what type of test is employed. If one believes these variables are non-stationary and thus he/she employs a first-differenced model or a cointegrated VAR model, the results may interest a macroeconometrician but this approach is definitely a step further on the wrong way to finding the truth because there are no dynamics in the GDP identity equation.
Model 4 includes all variables from both sides of the GDP. This exercise assumes that we have no knowledge of what variable is relevant or important so we have to include all possible variables. The estimated results show that the coefficients on the income-side variables are very close to 1 while those on the expenditure-side variables are very close to zero. Since the coefficients on the income-side variables are quite close to the results in Model 1, one may conclude that the irrelevant variables added to the model will not change the modelling results. However, here the expenditure-side variables are not irrelevant variables—they are components of the GDP! Their coefficients are zero simply because the model has already found the best fit, so they become redundant variables. This reasoning is confirmed by the fact that when the expenditure side of the GDP values are used as the values for dependent variables, the expenditure side of the GDP components become very significant (with coefficients close to 1), while the income side of the GDP components is insignificant. Hence, these results demonstrate that an econometric model cannot find which variables are relevant or important but can only suggest which variables can fit the data better.
Models 5–7 show different combinations of variable selections. Model 5 includes the 1-lagged GDP and expenditure-side variables. The results show that the coefficients for the expenditure-side variables are very similar to the results from Model 2, while the lagged GDP becomes insignificant. Again, this result does not indicate that the expenditure-side variables are more important than the lagged GDP, but only shows that the expenditure-side variable can fit the data better than the lagged GDP. Model 6 keeps the relatively more important variables on the expenditure side—wages and profits—but excludes the relatively less important variables—taxes and fixed capital formations. The results show the significant overstatement of the contribution of wages and profits. This is simply the consequence of omitting variables in macroeconometric models, but this model represents a likely case in macroeconometric modelling because in real econometric modelling practice no one has perfect knowledge to include all variables. Model 7 includes the most important variables from both sides of the GDP, namely wages, profits, consumption and investment. The estimation results do not make sense in economics: wages, profits and consumption make a discounted contribution to GDP (the coefficients for these variables are significantly less than 1) while investment contributes negatively (albeit insignificantly) to the GDP.
From this exercise of illustrative estimation, it is seen that, if the conditions for statistical theory hold, a statistical model works well (e.g. Model 1). However, this is an unlikely case in macroeconometrics because we have neither perfect knowledge about the factors involved nor the correct functions to be used, and also because the macroeconomic data are not accurate. From the performance of Models 5–7, we can see how misleading a macroeconometric model can be. Considering the possibility of misspecification of function forms in real econometric modelling practice, the estimation results can be even worse than the example displayed. In short, a macroeconometric model is most likely to be unable to find the truth due to measurement errors in data (e.g. Model 2), the inability to include all possible factors (e.g. Models 5, 6, 7), interference between explanatory variables (Models 5, 7) and misspecification of function form.
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Meng, S. (2019). Statistical Sophistry. In: Patentism Replacing Capitalism. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-12247-8_3
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