The next milestone is dates of turning points of the business cycle. As an example, in the US, the NBER produces a set of dates. These dates are extremely valuable in myriad applications. As an example, the standard operating procedure when drawing the chart of a macroeconomic time-series is to show a shaded background for the period which was a contraction. Here is one example: y-o-y CPI inflation in the US, with recessions shown as shaded bars. In the Indian setting, several papers have worked on the problem of identifying dates of turning points of the business cycle (Dua and Banerji, 2000, Chitre, 2001, Patnaik and Sharma, 2002, Mohanty et.al, 2003).
In a new paper (Pandey et. al., 2016) we bring three new perspectives to this question:
- In the older period, India was an agricultural economy, and the ups and downs of GDP growth were largely monsoon shocks. It is only in the recent period that we have got structural transformation, and the market process of cyclical behaviour of corporate investment and inventory, which add up to a business cycle phenomenon that is recognisably related to the mainstream conception of business cycles (Shah, 2008). This motivates a focus on the post-1991 period.
- We are able to shift from annual data to quarterly data by starting in the mid 1990s.
- We have the laid the groundwork for this to be a system, with regular updation of the dates, rather than a one-off paper.
One approach to business cycle measurement focuses on “growth cycles”, and relies on detrending procedures to extract the cyclical component of output. The cycle is defined to be in the boom phase when actual output is above the estimated trend, and in recession when the actual output is below the estimated trend. This identifies expansion and contraction based on the level of output. In contrast, the “growth rate cycle” identifies turning points based on the growth rate of output. For the post-reform period in India, this is more appropriate.
At an intuitive level, the procedure works as follows. First, we remove the trend and focus on fluctuations away from the trend. Second, we remove the high frequency fluctuations (below two years) and the low frequency fluctuations (above eight years). What’s left is in the range of frequencies which are considered `the business cycle’. Third, we identify turning points in this series.
In terms of tools and techniques, we use the filter by Christiano and Fitzgerald. The Christiano Fitzgerald filter belongs to the category of band-pass filters. This is used to extract the NBER-suggested frequencies from two to eight years. To this filtered cyclical component, we apply the dating algorithm developed by Bry and Boschan, 1971.
Our analysis is focused on seasonally adjusted quarterly GDP series (Base year 2004-05). This series is available from 1996 Q2 (Apr-Jun) to 2014 Q3 (Jul-Sep). The CSO revised the GDP series with a new base year of 2011-12. The revised series is available only from 2011 Q2. Hence we stick to the series with old base year for our analysis.
|De-trended, filtered, seasonally adjusted real GDP growth|
As an example, look at the period of the Lehman crisis. It is well known that the economy was weakening well before the Lehman bankruptcy in September 2008. As an example, INR started depreciating sharply from January 2008 onwards. The evidence above shows that the economy peaked at Q2 2007, and started weakening thereafter.
Each turning point is a fascinating moment. In Q2 2007, i.e. Apr-May-Jun 2007, growth was good but the business cycle was about to turn. It is interesting to go back into history to each of these turning points and think about what was going then, and what we were thinking then.
Our findings on business cycle chronology are robust to the choice of filter and to the choice of the measure of business cycle indicator. We conduct this analysis using different measures of business cycle indicators such as IIP, GDP excluding agriculture and excluding government, and Firms’ net sales, and find broadly similar turning points. Details about these explorations are in the paper.
A system, not just a paper
This is not a one off paper. We will review these dates regularly and update the files, while avoiding changes in URLs. When the methods run into trouble with future data, we will address these problems in the methods. This work would thus become a part of the public goods of the Indian statistical system.
All key materials have been released into the public domain. In addition to a paper web page, we have a system web page which gives a .csv file with dates at a fixed URL and can be used e.g. in your R programs.
An example of an application
|An example of placing recession bars on a graph, of
growth in (non-finance, non-oil) firms net sales
The graph above shows the familiar series of seasonally adjusted annualised growth, of the net sales of non-financial non-oil firms, with shaded bars showing downturns. This series only starts after 2000 as quarterly disclosure by firms only started then. Placing this series (net sales of firms) into the context of the business cycle events gives us fresh insight into both: we learn something about the sales of firms respond to business cycle fluctuations, and we learn something about business cycle fluctuations.
Facts about the Indian business cycle
It is useful to know summary statistics about the Indian business cycle: the average duration and amplitude of expansion and recession and the coefficient of variation (CV) in duration and amplitude across expansions and recessions.
|Exp/Rec||Average amplitude (in per cent)||Average duration (in quarters)||Measure of diversity in duration (CVD)||Measure of diversity in amplitude (CVA)|
The average amplitude of expansion is seen to be 2.5% while the average amplitude of recession is 2.2%. The average duration of expansion is seen to be 12 quarters while the average duration of recession is seen to be 9.3 quarters. These are fascinating new facts in India. There is more heterogeneity in the amplitude of a downturn when compared with expansions.
Changing nature of the Indian business cycle
In recent decades a number of emerging economies have undergone structural transformation and introduced reforms aimed at greater market orientation. There is an emerging strand of literature that studies the changes in business cycle stylised facts in response to these changes. Studies find that business cycle stylised facts have changed over time (Ghate et.al, Alp. et.al, 2012). In the paper, we explore some of these changes.
In the post-reform period, both expansions and recessions have become diverse in terms of duration and amplitude. Some episodes of recession are relatively more deeper and severe relative to others in the post-reform period. Similarly there is considerable variation in the duration of expansion and recession across specific cycles in the post-reform period. Some are short-lived while others are relatively more persistent.
Rudrani Bhattacharya, Radhika Pandey, Ila Patnaik and Ajay Shah. Seasonal adjustment of Indian macroeconomic time-series, NIPFP Working Paper 160, January 2016.
Radhika Pandey, Ila Patnaik and Ajay Shah. Dating business cycles in India. NIPFP Working Paper 175, September 2016.
Ajay Shah (2008). New issues in macroeconomic policy. In: Business Standard India. Ed. by T. N. Ninan. Business Standard Books. Chap. 2, pp.26–54.
Ajay Shah and Ila Patnaik (2010). Stabilising the Indian business cycle. In: India on the growth turnpike: Essays in honour of Vijay L. Kelkar. Ed. by Sameer Kochhar. Academic Foundation. Chap. 6, pp.137–154.