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Estimates of potential output are an important ingredient of structured forecasting and policy analysis. Using information on consensus forecasts, this paper extends the multivariate filter developed by Benes and others (2010). Although the estimates in real time are more robust relative to those of naïve statistical filters, there is still significant uncertainty surrounding the estimates. The paper presents estimates for 16 countries and provides an example of how the filtered estimates at the end of the sample period can be improved with additional information.
This paper develops a simple model for measuring potential output that uses data on inflation, unemployment, and capacity utilization. We apply the model to 10 countries, in addition to the United States and the euro area. While there is a substantial amount of uncertainty around our estimates, we find that the financial crisis has resulted in a reduction in potential output.
Estimates of potential output are an important component of a structured forecasting and policy analysis system. Using information on capacity utilization, this paper extends the multivariate filter developed by Laxton and Tetlow (1992) and modified by Benes and others (2010), Blagrave and others (2015), and Alichi and others (2015). We show that, although still fairly uncertain, the real-time estimates from this approach are more accurate than estimates constructed from naïve univariate statistical filters. The paper presents illustrative estimates for the United States and discusses how the end-of-sample estimates can be improved with additional information.
The estimates of potential output and the output gap presented in this paper are not official IMF estimates. The programs and potential output estimates in this paper can be downloaded from www.douglaslaxton.org.The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy. The authors would like to thank the European Department of the IMF for helpful comments. All errors and omissions are our own.
Estimates of potential output are an important ingredient of structured forecasting and policy analysis. Using information on consensus forecasts, this paper extends the multivariate filter developed by Benes and others (2010). Although the estimates in real time are more robust relative to those of naïve statistical filters, there is still significant uncertainty surrounding the estimates. The paper presents estimates for 16 countries and provides an example of how the filtered estimates at the end of the sample period can be improved with additional information.
This paper extends the multivariate filter approach of estimating potential output developed by Alichi and others (2018) to incorporate labor market hysteresis. This extension captures the idea that long and deep recessions (expansions) cause persistent damage (improvement) to the labor market, thereby reducing (increasing) potential output. Applying the model to U.S. data results in significantly smaller estimates of output gaps, and higher estimates of the NAIRU, after the global financial crisis, compared to estimates without hysteresis. The smaller output gaps partly explain the absence of persistent deflation despite the slow recovery during 2010-2017. Going forward, if strong growth performance continues well beyond 2018, hysteresis is expected to result in a structural improvement in growth and employment.
We study the properties of the IMF-WEO estimates of real-time output gaps for countries in the euro area as well as the determinants of their revisions over 1994-2017. The analysis shows that staff typically saw economies as operating below their potential. In real time, output gaps tend to have large and negative averages that are largely revised away in later vintages. Most of the mis-measurement in real time can be explained by the difficulty in predicting recessions and by overestimation of the economy’s potential capacity. We also find, in line with earlier literature, that real-time output gaps are not useful for predicting inflation. In addition, countries where slack (and potential growth) is overestimated to a larger extent primary fiscal balances tend to be lower and public debt ratios are higher and increase faster than projected. Previous research suggests that national authorities’ real-time output gaps suffer from a similar bias. To the extent these estimates play a role in calibrating fiscal policy, over-optimism about long-term growth could contribute to excessive deficits and debt buildup.
Estimates of potential output are an important ingredient of structured forecasting and policy analysis. Using information on consensus forecasts, this paper extends the multivariate filter developed by Benes and others (2010). Although the estimates in real time are more robust relative to those of naïve statistical filters, there is still significant uncertainty surrounding the estimates. The paper presents estimates for 16 countries and provides an example of how the filtered estimates at the end of the sample period can be improved with additional information. --Abstract.
Using a multivariate filter, we estimate potential growth rates in Chile’s mining and non-mining sectors. Estimates for the mining sector incorporate information on copper prices, whereas estimates for non-mining reflect information on inflation and unemployment rates. To better understand the drivers of potential growth, we decompose estimates into capital, labor (adjusted for human-capital and hours worked), and total-factor productivity using a production-function. Our estimates of potential output in Chile suggest that an important part of the recent growth slowdown has been structural, with potential-output growth slowing to 21⁄2 percent in recent years, although it plausibly could be higher in the medium-term.
This paper discusses some methodologies for estimating potential output and the output gap that have recently been studied at the Bank of Canada. The assumptions and econometric techniques used by the different methodologies are discussed in turn, and applications to Canadian data are presented. The first group of methods considered are those that simply use some implicit or explicit assumptions about the dynamics of real output to identify the output gap, including the Hodrick and Prescott filter for identifying the cyclical component of output. The second group consists of approaches that combine their assumptions with information from assumed or structural relationships between the output gap and other economic variables. The third class of methods uses multivariate rather than univariate dynamic relationships, often in combination with structural relationships from economic theory, to estimate output gap as a particular transitory component of real output.