A Simple Multivariate Filter For Estimating Potential Output

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 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.

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.

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.

Potential output—in the sense of the GDP level or path an economy can sustain over the medium term—is a crucial benchmark for policymakers. However, it is difficult to estimate when financial “booms and busts” are driving the real economy. This paper uses a simple multivariate filtering approach to illustrate the role financial variables play in driving potential or sustainable output. The results suggest that it moves more steadily during financial “boom and bust” periods than implied by conventional HP filter estimates, which tend to more closely follow actual GDP. A two-region, multisector New Keynesian DSGE model with financial frictions sheds light on the economic forces that could be behind the results obtained from the filter. This has important implications for policymakers.

The gap between potential and actual output—the output gap—is a key variable for policymaking. This paper adapts the methodology developed in Blagrave and others (2015) to estimate the path of output gap in the U.S. economy. The results show that the output gap has considerably shrunk since the Great Recession, but still remains negative. While the results are more robust than other existing methodologies, there is still significant uncertainty surrounding the estimates.