correction model (ECM) method, which estimates not only the long-run relationship between the cointegrated variables, but also the potential short-run deviations from this long-run relationship.

We use the pooled mean group (PMG) estimation method, introduced for panel estimates by Pesaran et al. (1999). It, too, is based on this principle of short-run deviations from the longrun trend. This method can be used to estimate the long-run relationship between the credit-toGDP ratio and other variables, which is identical for all countries, whereas the short-run deviations from this relationship can differ across countries. The PMG model, therefore, allows heterogeneity of the estimates for individual countries in the short run. However, the long-run relationship of the cointegrated variables is common to all the countries in the sample.

The data used for the OOS method were obtained from the International Monetary Fund’s International Financial Statistics (IFS) database, which provides the required macroeconomic data with a sufficient history (which is vital for estimating long-run relationships). For this reason, we used data for a 30-year period (1980–2010). The available statistics on bank loans to the private sector were used as the credit indicator. These statistics slightly underestimate the total credit of the private sector, as they do not include nonbank financial intermediaries (for example, leasing) and cross-border loans. Data on aggregate household consumption, government debt, short-term interest rates, unemployment, inflation measured by the GDP deflator, and GDP per capita in dollar terms were also used.

A long-run cointegration relationship between the credit-to-GDP ratio, the household consumption-to-GDP ratio, and GDP per capita in US dollars was identified for the OOS set of countries. The GDP per capita variable in the long-run relationship captures the different degree of wealth of the economy, which therefore also influences the equilibrium private credit level (Terrones and Mendoza 2004).

The following equation gives estimates of the coefficients of the long-run relationship between the co-integrated variables and the values of the coefficients in the short run, which are given as the mean of all the estimates for the relevant countries.

∆ (credit/gdp)t =  credit/gdpt-1 - 0.7cons/gdpt - 0.13 gdp/popt  }long-run relationship     ∆(cons/gdp)t-1  0.07inft-1 + 0.014 }short-run deviations    Note: *, **, and *** denote significance of the estimated coefficients at the 10%, 5%, and 1% levels, respectively.

_Credit/gdp_ represents the ratio of private sector credit to GDP, _cons/gdp_ denotes the ratio of household consumption to GDP, _gdp/pop_ is GDP per capita in dollar terms, and _inf_ is the change in the price level, expressed as the year-on-year change in the GDP deflator.

Besides the aforementioned variables, other factors that might affect the explained _credit/gdp_ ratio were included in the model. For example, the government debt-to-GDP ratio might capture any crowding out of bank lending to the private sector.<sup>18</sup> Also, the real interest rate, or changes therein, should, as the cost of financing, be in a negative relationship with the

> 18 For this reason, we would expect a negative relationship between the government debt ratio and loans to the private sector. The fact that a less indebted government sector would be able to provide more significant support if the banking sector ran into serious problems is relevant for assessing whether the current private sector credit level is excessive with regard to financial stability.

17