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Monetary Business Cycle Accounting Analysis of Indian Economy


In this paper we quantitatively analyze the real and nominal frictions which have shaped up economic fluctuations in the past two decades for the Indian economy. Our results provide guidelines for building future structural models to replicate its business cycle properties, for both real and monetary variables. We use the Monetary Business Cycle Accounting framework (Sustek in Rev Econ Dyn 14(4):592–612, 2011; Chari et al. in Econometrica 75(3):781–836, 2007), and quarterly data from 1999–2017, which is a first for the Indian economy. We find that the distortions in efficiency and investment wedges capture most of the fluctuations in output, investment and hours worked. Up to half of consumption dynamics however are accounted for by the labor wedge. We also find that investment frictions have decreased but labor market frictions have increased over time, indicating reforms in financial sector making an impact and the need for reforms in labor markets. Taylor’s Rule wedge matches up to 50% of inflation, and interest rate is well accounted by the asset market wedge.

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Data Availability Statement

Public sources of data, discussed in text.

Code availability

Available on request.


  1. Note that for aggregate hours worked, our data on it is annual, hence we interpolate it to quarterly series and also do a robustness check using other methods, and our results stay consistent. We discuss this in more detail later.

  2. Unpublished, available at

  3. Available at

  4. Results are similar if we assume 10 working hours in a day.

  5. We carry out some robustness checks in the last section on hours worked.

  6. Unpublished, available at

  7. We borrow the MATLAB codes for Bayesian analysis from Herbst and Schorfheide (2015).

  8. Shah (2015, Unpublished, available at mention that: ”From 2004–2005 to 2007–2008, bank credit rose by roughly 15 percentage points of GDP. “Industrial credit” expanded 2.61 times in this boom. Loans in one field, infrastructure and construction, expanded by 4.07 times, while the remainder of industrial credit expanded by 2.3 times.”

  9. In Table 8 we ignore the nominal wedges in calculating the decomposition results, since these wedges do not explain any part of the real observables as can be seen from the one-wedge on graphs. For example, in Fig. 3 for output, nominal wedges are essentially just straight lines, thus any value in the decomposition for them via the formula is artificial. In another paper we are working on where we include a simple banking sector with the standard monetary business cycle accounting setup, and in that paper the nominal wedges explain real variables.

  10. We also need to include small measurement errors via \(R_{o}=0.000025*ones(6)\).

  11. Standard deviations of inflation and wedge components of inflation: \(\sigma _{\pi }\) = 0.2796 \(\sigma _{\pi A}\) = 0.0215 \(\sigma _{\pi \tau _{l}}\) = 0.0200 \(\sigma _{\pi \tau _{x}}\) = 0.0098 \(\sigma _{\pi \tau _{b}}\) = 0.3612 \(\sigma _{\pi \tilde{R}}\) = 0.4684.

  12. See Viswanathan (2010).

  13. Here we introduce measurement errors by making \(R_{o}(3,3)!=0\), capturing measurement error in hours worked series.

  14. To save space we only show the decomposition results for observables Y, C, X, H with real wedges and with limited cases, results are similar in others.


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Correspondence to Kshitiz Mishra.

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Mishra, K., Chatterjee, P. Monetary Business Cycle Accounting Analysis of Indian Economy. J. Quant. Econ. 19, 471–491 (2021).

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  • Business Cycle Accounting
  • Frictions
  • Indian economy
  • Economic fluctuations
  • Wedges
  • Monetary

JEL Classification

  • E31
  • E32
  • E43