17 October 2010 The Information Content of Capacity Utilisation Rates for Output Gap Estimates Michael Graff and Jan-Egbert Sturm

17 October rd International Seminar, Moscow Overview  Introduction and motivation  Data  Output gap data: OECD Economic Outlook  Capacity utilisation: information from Business Tendency Surveys  Empirical analysis  Design  Results  Conclusions

17 October rd International Seminar, Moscow Evaluation Real-time data FinalPartly revisedFirst-released Vintage Final data Economic forecast Political decisions Final dataPartly revisedFirst-released Time

17 October rd International Seminar, Moscow Measurement of the output gap in real time  Output gap = (Y – Y*)/Y* ≈ y – y* Percentage deviation of factual output from potential output  Potential and factual output are unobservable in real time  This is when this information is most needed as a guidance for economic and monetary policy Countercyclical fiscal policy E.g. Swiss “debt brake” Monetary policy in a Taylor rule framework

17 October rd International Seminar, Moscow Measurement of the output gap in real time  Problems with real-time estimates of output gap data  Late availability and revisions to Y All GDP data are either ex-post estimates, or real-time “nowcasts” or ex-ante forecasts  End-point problem when estimating Y*  Orphanides & Van Norden (2002)  Revisions are of similar magnitude as the gap itself  Hence, questionable usefulness of output gap data in real time How can we improve the quality of output gap estimates in real time?  Various remedies suggested Forecasting data points Multivariate filters … This paper: output gap  capacity utilisation from BTS

17 October rd International Seminar, Moscow Some methods to estimate potential output  Smoothing real GDP using a filter  Hodrick-Prescott, Baxter-King, …  The “split time trend” method  calculate average output growth during each cycle, where the cycle is defined as the period between peaks in economic growth  Estimating potential output using a production function approach  ln Y = a + α ln L + (1 – α) ln K + TFP where L is labour, K capital and TFP ‘total factor productivity’  ln Y* = a + α ln L* + (1 – α) ln K* + TFP*, where ‘*’ denotes ‘potential’

17 October rd International Seminar, Moscow Output gap data: OECD Economic Outlook  Production function based approach  Bi-annual vintages with data at annual frequency  First vintage:Jun. 1995(data cover )  Last vintage:Dec. 2009(data cover )  Bi-annual vintages with data at quarterly frequency  First vintage:Dec. 2003(data cover 1970q1-2005q4)  Last vintage:Dec. 2009(data cover 1970q1-2011q4)  The resulting revision data sets are unbalanced  Annual data:22 countries(up to 287 obs.)  Quarterly data:18 countries(up to 338 obs.)  The largest balanced panels thereof are  Annual data:17 countries, (170 obs.)  Quarterly data:14 countries, 2003q3-2005q4(140 obs.)

17 October rd International Seminar, Moscow Capacity utilisation data  Sources: European Commission, OECD MEI, KOF, national sources (in case of Belgium and New Zealand)  Business tendency survey data  Question asks for assessment of current level of capacity utilisation –Refers mainly to means of production (physical capital) –Is consistently asked in the industry sector –Range Minimum: completely idle = 0 % Maximum: full utilisation of present capacity = 100 % -Few surveys allow for “excess” capacity utilisation > 100%  Data are (almost) not revised

17 October rd International Seminar, Moscow BTS: Direct measurement of capacity utilisation

17 October rd International Seminar, Moscow Annual data  Countries in bold are not included in the strictly balanced sample

17 October rd International Seminar, Moscow F4 F2F3F4 F1F2F3F4 F1F2F3F4 F1F2F3 F1F2 F1F2 F1F2 F1F2 F1F2 F1F2 F1F2 F1F2 F1F2 F1F2 F1F2 F4 F3F4 F3F4 F3F4 F3F4 F3F4 F3F4 F3F4 F3F4 F3F4 F3F4 Data setup and revision process: Annual data Source: OECD, calculations KOF JunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDecJunDec 1970 … Fx Rx Reference Period 2002 Forecast number x Release number x Vintages / Release Dates R1R2R3R4R5R6R7R8 ……………………………………………………………………………

17 October rd International Seminar, Moscow Releases of annual output gaps: unbalanced panel

17 October rd International Seminar, Moscow Releases of annual output gaps: averaged bal. panel Source: OECD, calculations KOF rel. 1 rel. 2 rel. 3 rel. 4 rel. 5 rel. 6 rel. 7 rel. 8 CU rate % of potential GDP CU rate (in %)

17 October rd International Seminar, Moscow Average absolute revisions of annual OG, balanced panel

17 October rd International Seminar, Moscow Average revisions of annual OG, balanced panel

17 October rd International Seminar, Moscow Cumulative revisions of annual OG, balanced panel

17 October rd International Seminar, Moscow Descriptive Statistics of the annual releases/vintages ObsMeanSt.D.Min.Max. ObsMeanSt.D.Min.Max. degree (in %) Release Release Release Release Release Release Release Release Strictly balanced panelMaximum panel (22 countries, )(17 countries, ) Capacity utilisation (in % of full capacity) Output gap (in % of potential GDP)

17 October rd International Seminar, Moscow Estimation design  Data revisions contain news  revisions are orthogonal to earlier releases and not predictable y Rx (t) = y R1 (t) +  (t), cov(y R1 (t),  (t)) = 0 Rx = R2, R3, R4, R5, R6, R7, R8  Mincer-Zarnowitz (1969) test for forecast efficiency (in a panel data set-up)  Are real time output gap estimates “informationally efficient” (w.r.t. Capacity Utilisation data)  Are the revisions predictable?   Rx-R1 y(t) =  (i) +  y R1 (i,t) +  CU(i,t) +  (t) +  (i,t) –  Rx-R1 y(t) represent the cumulative revisions 1 to 7 –Hypotheses:  (i) = 0,  = 0,  = 0

17 October rd International Seminar, Moscow Efficiency regressions, increasing revision horizons (1)(2)(3)(4)(5)(6)(7) Dependent variable:R 2 -R 1 R 3 1 R 4 1 R 5 1 R 6 1 R 7 1 R (-3.74)(-6.13)(-8.00)(-6.69)(-7.35)(-6.25)(-5.63) (1.53)(2.50)(2.44)(2.66)(3.29)(1.99)(2.19) Adjusted R Number of observations170 Number of countries17 Number of periods10 p-value LR-test for country effects p-value LR-test for time effects0.00 p-value LR-test for time and country effects0.00 First release (y R1 ) Capacity utilisation rate

17 October rd International Seminar, Moscow Goodness-of-fit across different revisions Revision 1Cumulative Revision 2 Cumulative Revision 3 Cumulative Revision 4 Cumulative Revision 5 Cumulative Revision 6 Cumulative Revision 7 without CU variableCU variable included adj.R2

17 October rd International Seminar, Moscow Additional regression results, annual data Dependent variable: cumulative revision 7 ( Δ R8-R1 y) (1)(2)(3)(4)(5) (-6.09)(-5.63)(-6.00)(-5.98)(-5.91) (2.19)(2.57)(1.90) (2.52)(1.92) Adjusted R Number of observations Number of countries17 Number of periods10 p-value LR-test for country effects0.00 p-value LR-test for time effects0.00 p-value LR-test for time and country effects0.00 First release (y R1 ) Capacity utilisation rate Capacity utilisation rate, lagged one period

17 October rd International Seminar, Moscow ……………………………… …………………………… F4 F3F4 F3F4 F2F3F4 F2F3F4 F1F2F3F4 F1F2F3F4 F1F2 Data setup and revision process: Quarterly data Source: OECD, calculations KOF JunDecJunDecJunDecJunDecJunDecJunDecJunDec 1970I …… 2003I II 2003III 2003IV 2004I II 2004III 2004IV 2005I II 2005III 2005IV …… 2010IV Fx Rx Forecast number x Release number x Based on less information Based on more information Vintages / Release Dates Reference Period R1R2R3R4R5R6R7R8R1R2R3R4R5R6R7R8R2R3R4R5R6R7R8 R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8 R1R2R3R4R5R6R7R8

17 October rd International Seminar, Moscow Releases of quarterly output gaps: unbalanced panel

17 October rd International Seminar, Moscow Average absolute revisions of quarterly OG, balanced panel

17 October rd International Seminar, Moscow Average revisions of quarterly OG, balanced panel

17 October rd International Seminar, Moscow Cumulative revisions of quarterly OG, balanced panel

17 October rd International Seminar, Moscow Efficiency regressions, quarterly data

17 October rd International Seminar, Moscow Conclusions  Revisions to OECD output gap estimates are almost of a similar magnitude as the output gap estimates  During the (short) sample period, output gaps were overall revised upwards  Hence, revisions appear to be predictable without further information  Yet, in addition to this, real time BTS data on capacity add further explanatory power to explain revision process  OECD real-time output gap estimates are not informationally efficient  Referring to the survey data available in real time could have improved output gap estimates  Findings are robust with respect to sample and frequency

17 October rd International Seminar, Moscow To be done …  Carry out real-time forecasting exercise on country level  Do the results hold when using other output gap estimates (e.g. those produced by HP filters)  Do other BTS data, e.g. business climate indicators, add information  … (suggestions are highly appreciated)