In a recent speech in Hull, England, Bank of England Deputy Governor for Monetary Policy, Charlie Bean, addressed the topic of central banking in the boom and slump. In so doing, he laid out the reasoning for macroprudential policies going forward, which will be implemented by the new Financial Policy Committee (FPC). These macroprudential policies are in the development stage, leading Mr Bean to call on academia for support in both theory and empirical areas. Fortunately, a recent paper analysing credit booms has done just that.
If one thinks of economics broadly, three areas could be distinguished: theory, empirics and policy. When trying to ensure that the financial crisis of 2008 does not happen again, we need advances in all three of these areas. In terms of policy, this is exactly what Mr Bean laid out in his Hull speech. For example, the FPC will use a countercyclical capital buffer to manage credit across the cycle. Basel III already requires banks to hold more capital against their risk weighted assets, but national regulators will be able to require an additional time-varying buffer that can be increased during the upswing of the credit cycle.
In terms of theory and empirics, things are also progressing, with the theoretical work of Geanakoplos and the fantastic empirical work of Mian and Sufi. More recently, a paper by Mendoza & Terrones (hereafter M&T) has highlighted the role of credit in a boom that Mr Bean was referring to in his speech.
There are three parts of interest in this paper, which will be addressed in turn.
1) How they did it
The method to measure and define a credit boom by M&T was quite interesting. A credit boom is defined as a period in which credit to the private sector grows by more than is typical in a business cycle. The method is summarised as follows:
Denote the deviation from the long-run trend in the logarithm of real credit per capita in country i, date t as lit, and the corresponding standard deviation of this cyclical component as σ (li ) . The long-run trend is calculated using the Hodrick-Prescott (HP) filter with the smoothing parameter set at 100, as is typical for annual data. Country i is defined to have experienced a credit boom when we identify one or more contiguous dates for which the credit boom condition, lit ≥ϕσ(li ) holds, where ϕ is the boom threshold factor. Thus, during a credit boom the deviations from trend in credit exceed the typical expansion of credit over the business cycle by a factor of ϕ or more. The baseline value of ϕ is set at 1.65, because the 5 percent tail of the standardized normal distribution satisfies Prob(lit /σ(li )≥1.65 )=0.05.
This method makes both intuitive and mathematical sense. It not only allows the authors to conduct their analysis, but it also has possible implications for theoretical frameworks of credit cycles.
2) Event analysis
Using IMF IFS data for 61 countries, covering both industrialised and developing countries, M&T identify 70 credit booms in the 50 years up to 2010. To then understand the macroeconomic dynamics of a credit boom, the authors conduct event analysis around 7 year windows of the credit boom.
Output, private consumption and government expenditure all rise during a credit boom. Investment, output of non-tradables and the real exchange rate all show similar patterns, but seem to rise and fall more severely across the cycle.
In line with theories of financial accelerators and balance sheet effects of credit booms and busts, the authors also find that equity prices and house prices also show a clear pattern of upswing in the boom followed by a downswing in the bust.
3) Frequency analysis
M&T then conduct a frequency analysis to examine the associations to financial crises and preconditions to a credit boom. Firstly, credit booms are often associated with banking crises, currency crises and sudden stops in capital inflows. An important point to note here is that not all credit booms end with a crisis as such. This finding has important theoretical implications as any model should help to distinguish why some credit booms end in crisis while other do not. Secondly, regarding preconditions for credit booms, the results showed that surges in capital inflows are a good predictor of credit booms, while total factor productivity gains are also a good predictor in industrialised countries.
Sometimes in economics things go from theory to empirics to policy. The policy presented by Mr Bean and the work of M&T have suggested the opposite flow: from policy and empirics to theory. The challenge now is to model these empirical findings.
Fascinating theoretical questions arise from the work of M&T. Firstly, how do you allow credit to be solved endogenously in a model in which it can have ‘normal’ business cycle fluctuations but, at times, it can move into a boom phase? How can a theoretical model that addressed credit booms then allow for large movements in the equilibrium, such as output crashing from a credit bust? In general, this is what the Cubic IS model is attempting to do. Perhaps we should look at behavioural extensions of New Keynesian models to see if some of the Cubic models’ features can play a role in such a theoretical framework.
 Mendoza, E. G. & Terrones, M. E. (2012). An anatomy of credit booms and their demise. NBER Working Paper series, Working Paper 18379.