# II.2 Measures of potential output growth

The literature has largely focused on three methods of estimating potential growth: a production function method, time-series filters, and analysts’ growth forecasts .

Production function method. The production function approach represents potential output as a function of the fully utilized capital stock, fully employed labor force, and technology as measured by TFP. For analytical convenience, the production function is often assumed to have a particular form, known as Cobb-Douglas. Potential TFP growth is estimated as the predicted value of a parsimonious panel regression of five-year averages of trend TFP growth on lagged per capita income relative to the advanced-economy average (to proxy convergence-related productivity catchup), education and demographic indicators, and trend investment (annex A). Potential labor supply is estimated as the population-weighted aggregate of predicted values of age- and gender-specific labor force participation rates from regressions on policy outcomes and cohort characteristics, business cycles, and country effects. The potential capital stock is assumed to match the actual capital stock.

Time-series filtering methods. These methods employ univariate or multivariate filters. Univariate filters involve estimates of trend output using only GDP data series (annex B). Multivariate filters use the empirical relationship between GDP and other variables (such as inflation, unemployment rates, commodity prices or financial variables) to help distinguish short-run deviations of output from trends (annex C). The database in this study employs the following five univariate filters: the Hodrick-Prescott filter, the BaxterKing filter, the Christiano-Fitzgerald filter, the Butterworth filter, and a filter based on an unobserved components model. An additional multivariate filter uses financial variables and commodity prices, a Phillips curve relationship, a Taylor rule, and Okun’s law.

Growth forecasts. This method is applied using two sets of long-term (five-years-ahead) growth forecasts, from Consensus Economics and the IMF’s World Economic Outlook database (annex D). These forecasts are based partly on models used by the analysts and partly on the analysts’ judgement. Judgment can play an important role during periods of major structural change, which models may not be well-equipped to capture.

Each approach comes with advantages and disadvantages (table 2). Even in data-poor environments, univariate filters are straightforward to implement. Multivariate filters utilize additional information that can ensure that the measure of potential output is better aligned with its determinants, as suggested by economic theory. In particular, the multivariate filter-based estimates can ensure that estimated output gaps in the short term are consistent with indicators of domestic demand pressures (such as inflation, unemployment, current account balances, and capacity utilization). All statistical filters, however, have drawbacks: in particular, they suffer from well-known “end-point” problems that tend to lead to large revisions as new data become available. The approach employed here includes forecasts of real GDP growth to minimize this problem. Since they capture high-frequency movements, measures of potential growth based on filtering techniques correlate strongly with actual output growth and with each other.

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