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Business cycles in the economy and in economics: an econometric analysis

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Abstract

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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Notes

  1. See Besomi (2011, 55 f.) for more detail on these references.

  2. For example, see Cardoso et al. (2010), Diamond (2009), Geiger (2014) and Kim et al. (2006). Also see Beckmann and Persson (1998) and Cahlik (2000) for general bibliometric work related to economics.

  3. 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.

  4. 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.

  5. This is in order to account for the fact that especially in British literature, ‘trade cycle’ had been the term of choice describing the same concept for a long time, e.g. in Keynes (1936) and Hicks (1950).

  6. Retrieved on April 22nd 2015 from FRED, Federal Reserve Bank of St. Louis, https://research.stlouisfed.org/fred2/series/CFMMI/.

  7. 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.

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Acknowledgments

The authors are grateful for helpful comments by Pedro Garcia Duarte, Harald Hagemann, Johannes Schwarzer and an anonymous referee.

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Authors and Affiliations

  1. Department of Economics, University of Hohenheim, Stuttgart, Germany

    Vadim Kufenko & Niels Geiger

Authors
  1. Vadim Kufenko
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  2. Niels Geiger
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Corresponding author

Correspondence to Niels Geiger.

Appendix

Appendix

See Tables 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 and 16.

Table 2 Granger causality—five variables, category: ‘Business and Economics’
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Table 3 Granger causality—additional variables, category: ‘Business and Economics’
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Table 4 Granger causality—additional variables, category: ‘Economics’
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Table 5 Granger causality—five variables, category: ‘Economics’
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Table 6 Portmanteau Q test of the residuals, category: ‘Business and Economics’
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Table 7 Portmanteau Q test of the residuals, category: ‘Economics’
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Table 8 Johansen cointegration test
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Table 9 Fractional cointegration test
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Table 10 FCVAR long-run exogeneity test—five variables, category: ‘Business and Economics’
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Table 11 FCVAR long-run exogeneity test—two variables, category: ‘Business and Economics’
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Table 12 FCVAR long-run exogeneity test—two variables, category: ‘Economics’
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Table 13 FCVAR long-run exogeneity test—five variables, category: ‘Economics’
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Table 14 FCVAR, Portmanteau Q test of the residuals, category: ‘Business and Economics’
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Table 15 FCVAR, Portmanteau Q test of the residuals, category: ‘Economics’
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Table 16 Diebold–Mariano test, comparing the bibliometric versus bibliometric + economic forecasts
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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

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