Abstract
We present a model that incorporates the information contained in diverse variables when estimating sustainable output growth. For this purpose, we specify a state-space model representing a multivariate HP filter that links cyclical fluctuation in GDP with several indicators of macroeconomic imbalances. We obtain the parameterizetion of the model by estimating it over a cross-section of emerging market economies. We show that the trend output growth rates estimated by using this model are more stable than those obtained with a univariate version of the filter and thus are more consistent with the notion of sustainable output.
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Notes
Unlike Borio et al. (2013, 2014), we do not use a dynamic version of the HP filter, which involves the addition of a lagged output gap term on the right-hand side of (2). The unrestricted estimation of this term’s coefficient yields a value close to unity, which is economically implausible. Arguably, this may be due to insufficient variability in the output gap for the relatively short time sample in emerging markets. In addition, as shown in Borio et al. (2014), when using the dynamic HP filter, the smoothing parameter λ should be recalibrated for each specific case in order to make the results comparable with the static version. That would seriously complicate our analysis, which is based on pooled estimation.
We tested s from 0 to 4 and found that in most cases s=1 yields the best results.
Although not necessarily. See for example Senhadji (1999) and Duffy and Papageorgiou (2000) for panel estimates of the production function.
In particular, we found it extremely challenging to obtain satisfactory results with country-specific models that included more than one or two explanatory variables, as the filter tended to favour only few indicators with the highest correlation with output growth. In the pooled estimates, we obtained more balanced results, as the performance of the explanatory variables was averaged across countries. Admittedly, this approach may be misleading if one expects to find substantial systematic differences in the relationship between the imbalances indicators and output in different countries. The reported results should therefore be regarded as evidence of the general relevance of the imbalances indicators for output gap diagnostics rather than the optimal parameterization of the model.
We tested a broad range of indicators before making this selection. Most notably, indicators of external imbalances (trade balance, external debt, real effective exchange rate), although not included in the final model, worked well in other specifications. In addition, admittedly, the availability of financial indicators for emerging markets is severely limited, making their compilation for the whole cross-section quite difficult. We therefore were unable to test some indicators that could be useful (eg housing prices).
This transformation is different from that of Borio et al. (2014), who use de-meaned growth rates. Such a transformation seems less applicable to emerging markets for which sample means are rarely associated with equilibrium values (eg CPI mean growth in the case of gradual disinflation). Admittedly, de-trending the data exacerbates the end-point problem and thus worsens the real-time performance of the model. We experimented with de-trending the imbalances variables jointly with GDP; however, while the model became computationally heavier, the results were not significantly different.
Conducting the estimates on the shorter balanced time sample did not change the results dramatically.
For extrapolation, we used the average growth rate of trend GDP in 2005–2007.
We report the resultant output evolution values after the banking crises estimated in Abiad et al. (2009) and output as a percentage of the pre-crisis trend after deep and long recessions in emerging economies reported in Howard et al. (2011), the estimated impact of severe financial crisis reported in Furceri and Mourougane (2012) and the average cumulative level change in potential output in emerging markets after stand-alone recessions reported in Haltmaier (2012).
We assume that parameterization γ is known and that it does not conduct recursive estimates for the pooled data set. We are not able to fully replicate the real-time analysis because in our sample most of the information on boom/bust occurrence comes in one batch.
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Krupkina, A., Deryugina, E. & Ponomarenko, A. Estimating Sustainable Output Growth in Emerging Market Economies. Comp Econ Stud 57, 168–182 (2015). https://doi.org/10.1057/ces.2014.39
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DOI: https://doi.org/10.1057/ces.2014.39