It is sometimes pointed out that economic research is prone to move in cycles and react to particular events such as crises and recessions. The present paper analyses this issue through a quantitative analysis by answering the research question of whether or not the economic literature on business cycles is correlated with movements and changes in actual economic activity. To tackle this question, a bibliometric analysis of key terms related to business cycle and crises theory is performed. In a second step, these results are confronted with data on actual economic developments in order to investigate the question of whether or not the theoretical literature follows trends and developments in economic data. To determine the connection between economic activity and developments in the academic literature, a descriptive analysis is scrutinized by econometric tests. In the short run, the VARs with cyclical fluctuations point out multiple cases where economic variables Granger-cause bibliometric ones. In the long run, the fractionally cointegrated VARs suggest that many bibliometric variables respond to economic shocks. In the multivariate framework, the Diebold–Mariano test shows that economic variables significantly improve the quality of the forecast of bibliometric indices. The paper also includes impulse-response function analysis for a quantitative assessment of the effects from economic to bibliometric variables. The results point towards a qualified confirmation of the hypothesis of an effect of business cycles and crises in economic variables on discussions in the literature.
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See Besomi (2011, 55 f.) for more detail on these references.
This also excludes books, whether miscellanies or monographs. If economics became more article- and less book-oriented, especially since the nineteenth century, this could imply the risk of systematically wrong assessments of the earlier decades of the time frame observed here, especially since the sample size for the nineteenth century is still fairly small. However, for a key term analysis (in contrast to a citation analysis, where important individual sources might be left out when not including books), this should pose no major problems, as long as content in books does not systematically employ different key terms from journal articles when discussing the same issues—an assumption which seems plausible. Still, it should be pointed out that in one respect, therefore, the present analysis is less comprehensive than that of Besomi (2011): it includes only research articles, no other items. However, this also makes the sample used here more consistent, since the other items Besomi (2011) uses are not available in a comprehensive manner similar to journal archives.
One of the terms Besomi (2011) lists, namely ‘confidence’, is left out here. This is because results for ‘confidence’ can be expected (and indeed turn out) to be less reliable, since any econometric paper reporting confidence levels on its results will contain the term, no matter which topic it discusses.
Retrieved on April 22nd 2015 from FRED, Federal Reserve Bank of St. Louis, https://research.stlouisfed.org/fred2/series/CFMMI/.
The test on the long-run exogeneity is carried out analogously to Jones et al. (2014, 1100) with a 5 % benchmark. The causal inference in cointegrated systems is not straightforward and a standard Wald test as in the case of Granger causality may be problematic (see Mosconi and Giannini 1992 for the discussion). However, the above-mentioned testing procedure allows us to perform long-run inference regarding the responses of bibliometric variables to economic shocks.
Aftalion, A. (1913). Les crises périodiques de surproduction. Paris: Riviére.
Baum, C. F. (2003). DMARIANO: Stata module to calculate Diebold–Mariano comparison of forecast accuracy. Statistical Software ComponentsS433001, Boston College Department of Economics, revised 26 Apr 2011.
Beckmann, M., & Persson, O. (1998). The thirteen most cited journals in economics. Scientometrics, 42(2), 267–271.
Besomi, D. (2011). Naming crises: A note on semantics and chronology. In D. Besomi (Ed.), Crises and cycles in economic dictionaries and encyclopaedias (pp. 54–132). London: Routledge.
Björk, B.-C., & Solomon, D. (2013). The publishing delay in scholarly peer-reviewed journals. Journal of Informetrics, 7(4), 914–923.
Cahlik, T. (2000). Search for fundamental articles in economics. Scientometrics, 49(3), 389–402.
Cardoso, A. R., Guimarâes, P., & Zimmermann, K. F. (2010). Trends in economic research: An international perspective. Kyklos, 63(4), 479–494.
Diamond, A. M. (2009). Schumpeter vs. Keynes: ‘In the long run, not all of us are dead’. Journal of the History of Economic Thought, 31(4), 531–541.
Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253–263.
Durbin, E. F. M. (1933). Purchasing power and trade depression: A critique of under-consumption theories. London: Jonathan Cape.
Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836.
Fabian, A. (1989). Speculation on distress: The popular discourse of the panics of 1837 and 1857. Yale Journal of Criticism, 3(1), 127–142.
Fisher, I. (1932). Booms and depressions: Some first principles. New York: Adelphi.
Garrett, T. A. (2007). The rise in personal bankruptcies: The eighth federal reserve district and beyond. Federal Reserve Bank of St. Louis Review, 89(1), 15–37.
Geiger, N. (2014). The rise of behavioural economics: A quantitative assessment. Violette Reihe Arbeitspapiere, No. 44/2015, University of Hohenheim.
Granger, C. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424–438.
Granger, C. (1980). Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control, 2(1), 329–352.
Hamilton, J. (1994). Time series analysis. Princeton, NJ: Princeton Univ. Press.
Hicks, J. R. (1950). A contribution to the theory of the trade cycle. Oxford: Clarendon Press.
Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2–3), 231–254.
Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59(6), 1551–1580.
Johansen, S. (2008). A representation theory for a class of vector autoregressive models for fractional processes. Econometric Theory, 24(3), 651–676.
Johansen, S., & Nielsen, M. Ø. (2012). Likelihood inference for a fractionally cointegrated vector autoregressive model. Econometrica, 80(6), 2667–2732.
Jones, M. E. C., Nielsen, M. Ø., & Popiel, M. K. (2014). A fractionally cointegrated VAR analysis of economic voting and political support. Canadian Journal of Economics/Revue canadienne d’économique, 47(4), 1078–1130.
Keynes, J. M. (1936). The general theory of employment, interest, and money, Volume VII of the collected writings of John Maynard Keynes. London: Macmillan (1973).
Kim, E. H., Morse, A., & Zingales, L. (2006). What has mattered to economics since 1970. The Journal of Economic Perspectives, 20(4), 189–202.
Kurz, H. D. (2006). Whither the history of economic thought? Going nowhere rather slowly? The European Journal of the History of Economic Thought, 13(4), 463–488.
Ljung, G. M., & Box, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297–303.
Lucas, R. E. (2003). Macroeconomic priorities. The American Economic Review, 93(1), 1–14.
MacKinnon, J. G., & Nielsen, M. Ø. (2014). Numerical distribution functions offractional unit root and cointegration tests. Journal of Applied Econometrics, 29(1), 161–171.
Mills, J. (1868). On credit cycles and the origin of commercial panics. Transactions of the Manchester Statistical Society (pp. 11–40). Manchester: J. Roberts.
Mosconi, R., & Giannini, C. (1992). Non-causality in cointegrated systems: Representation estimation and testing. Oxford Bulletin of Economics and Statistics, 54(3), 399–417.
Neumark, F. (1975). Zyklen in der Geschichte ökonomischer Ideen. Kyklos, 28(2), 257–285.
Nielsen, B. (2001). Order determination in general vector autoregressions. Economics Papers, No. 2001-W10, Economics Group, Nuffield College, University of Oxford.
Nielsen, M. Ø, & Popiel, M. K. (2014). A Matlab program and user’s guide for thefractionally cointegrated VAR model. QED working paper, No. 1330, Queen’s University.
Officer, L. H., & Williamson, S. H. (2015). The annual consumer price index for the United States, 1774–2014.
Petris, G. (2010). An R package for dynamic linear models. Journal of Statistical Software, 36(12), 1–16.
Petris, G., Petrone, S., & Campagnoli, P. (2009). Dynamic linear models with R. Use R! Springer.
Richter, F. E. (1923). Recent books on business cycles. The Quarterly Journal of Economics, 38(1), 153–168.
Shiller, R. J. (2015). Irrational exuberance. Princeton: Princeton University Press.
Soo, C. K. (2013). Quantifying animal spirits: News media and sentiment in the housing market. Ross School of Business Paper, No. 1200.
The authors are grateful for helpful comments by Pedro Garcia Duarte, Harald Hagemann, Johannes Schwarzer and an anonymous referee.
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Kufenko, V., Geiger, N. Business cycles in the economy and in economics: an econometric analysis. Scientometrics 107, 43–69 (2016). https://doi.org/10.1007/s11192-016-1866-9
- Bibliometric analysis
- Business cycle theory
- Fractional cointegration