We therefore suggest to alternatively consider the use of predicted growth rates in the persistence term. Based on equation (2), and suppressing the country subscript _c_ , we can define predicted growth as: ∆lny� 1 , (4) such that any changes in cc _x_ will be reflected in this predicted growth rate and hence in subsequent cc−1 1,cc kk kk,cc = θθ∆lny� + ββ ∆xx + ⋯+ ββ ∆xx _x_ persistence terms. Note that the presence of a lagged predicted growth rate in equation (4) requires some approximation (e.g., with actual growth) in the starting period.

# **6.3 What If the Model Does Not Predict Actual Growth Rates Well?**

In the above example for Bangladesh, our model explained actual growth in the 2005-2009 period very well: the residual of 0.27 percentage points (=4.35% - 4.08%) is less than 10% of the actual growth rate. More generally, discrepancies of 1/3 are not exceptional, and they can easily amount to 1/2 in some cases. Such large residuals may result from various sources that may be informative in its own and require country-specific expert knowledge and additional analysis and tools to detect the underlying reasons of such deviations.

One possibility is that the model is not adequate for the country under consideration. For example, small-island states, resource-abundant economies, or transition countries feature specific peculiarities that may not be adequately captured by the model – either because relevant model variables are missing, or because the growth correlation with existing model variables is different for those countries. In the former case, extending the model with those relevant variables can be considered. In the latter case, one can investigate parameter heterogeneity for country groups.

Another possibility is that national GDP numbers are inflated – due to GDP rebasing or creative national accounting – or otherwise mismeasured. Comparing GDP data to alternative proxies of economic activity (e.g., nightlight) can be informative to substantiate this possibility.

A third possibility is that growth dynamics during periods of large residuals are driven by cyclical factors. Neoclassical growth models focus on the role of fundamental long-run growth correlates, as opposed to cyclical factors. The next subsection will give an example of such cyclical deviations.

# **6.4 Crises and Growth Underperformance**

The example of Ghana in Geiger et al. (2019) is instructive to see how deviations between model and actually observed growth rates reflect cyclical effects and can be used to assess potential growth vulnerabilities. Their model only predicts a growth rate of 1.1% for the early-2010s but actual growth was almost 10%. As is noted in their paper, actual growth mainly reflected cyclical factors (oil discoveries, commodity prices, loose fiscal stance) and was not sufficiently backed up by relevant long-run growth correlates that feature in the model. This can be a warning sign for growth to run out of steam – as actually happened in the case of Ghana, which defaulted on its external debt in 2023. Using filtering techniques that separate growth into a trend and cyclical component can be useful to detect such cyclical deviations from trend. Moreover, it could be a warning sign for vulnerabilities to build up if developments in credit/GDP are unusually large, particularly if paired with large government consumption and foreign financing (e.g., fast increases in FDI or other capital inflows, or an increasing current account deficit).

A separate case concerns longer-lasting periods when growth considerably underperforms in a country, with growth rates hovering around or below 0. On top of the steps suggested under 6.3, one could look at levels of main growth correlates (see 5.2) in comparison to faster-growing but structurally similar peer countries and check if the country is considerably falling back in a typical growth correlate. Moreover, one could investigate in which period actual growth started to

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