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

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Presentation on theme: "17 October 2010 The Information Content of Capacity Utilisation Rates for Output Gap Estimates Michael Graff and Jan-Egbert Sturm."— Presentation transcript:

1 17 October 2010 The Information Content of Capacity Utilisation Rates for Output Gap Estimates Michael Graff and Jan-Egbert Sturm

2 17 October 201023rd 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

3 17 October 201033rd International Seminar, Moscow Evaluation Real-time data FinalPartly revisedFirst-released Vintage Final data Economic forecast Political decisions Final dataPartly revisedFirst-released Time

4 17 October 201043rd 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

5 17 October 201053rd 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

6 17 October 201063rd 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’

7 17 October 201073rd 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 1970-1996)  Last vintage:Dec. 2009(data cover 1970-2011)  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, 1996-2005 (170 obs.)  Quarterly data:14 countries, 2003q3-2005q4(140 obs.)

8 17 October 201083rd 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

9 17 October 201093rd International Seminar, Moscow BTS: Direct measurement of capacity utilisation

10 17 October 2010103rd International Seminar, Moscow Annual data  Countries in bold are not included in the strictly balanced sample

11 17 October 2010113rd 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 … 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Fx Rx Reference Period 2002 Forecast number x Release number x Vintages / Release Dates 20072008200919951996199719981999200020012003200420052006 R1R2R3R4R5R6R7R8 ……………………………………………………………………………

12 17 October 2010123rd International Seminar, Moscow Releases of annual output gaps: unbalanced panel

13 17 October 2010133rd International Seminar, Moscow Releases of annual output gaps: averaged bal. panel Source: OECD, calculations KOF -2.0 -1.5 -0.5 0.0 0.5 1.0 1.5 2.0 1996199719981999200020012002200320042005 rel. 1 rel. 2 rel. 3 rel. 4 rel. 5 rel. 6 rel. 7 rel. 8 CU rate % of potential GDP 79.5 80.0 80.5 81.0 81.5 82.0 82.5 83.0 83.5 CU rate (in %)

14 17 October 2010143rd International Seminar, Moscow Average absolute revisions of annual OG, balanced panel

15 17 October 2010153rd International Seminar, Moscow Average revisions of annual OG, balanced panel

16 17 October 2010163rd International Seminar, Moscow Cumulative revisions of annual OG, balanced panel

17 17 October 2010173rd International Seminar, Moscow Descriptive Statistics of the annual releases/vintages ObsMeanSt.D.Min.Max. ObsMeanSt.D.Min.Max. degree (in %)35381.54.4064.592.317081.42.9774.487.5 Release 1287-0.931.98-8.795.50170-0.791.58-4.865.50 Release 2283-0.551.65-5.735.68170-0.641.58-4.275.68 Release 3287-0.401.75-5.506.39170-0.531.64-4.316.39 Release 4283-0.461.90-7.316.41170-0.461.62-4.066.41 Release 5287-0.381.97-7.327.66170-0.381.65-4.537.66 Release 6283-0.511.90-9.546.77170-0.281.59-3.166.77 Release 7287-0.481.96-9.546.84170-0.241.64-5.116.84 Release 8283-0.522.02-9.666.83170-0.131.67-4.176.83 Strictly balanced panelMaximum panel (22 countries, 1995-2009)(17 countries, 1996-2005) Capacity utilisation (in % of full capacity) Output gap (in % of potential GDP)

18 17 October 2010183rd 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

19 17 October 2010193rd 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 8 1 -0.22-0.35-0.48-0.55-0.59-0.54-0.47 (-3.74)(-6.13)(-8.00)(-6.69)(-7.35)(-6.25)(-5.63) 0.060.130.170.190.230.16 (1.53)(2.50)(2.44)(2.66)(3.29)(1.99)(2.19) Adjusted R 2 0.190.260.320.370.470.450.49 Number of observations170 Number of countries17 Number of periods10 p-value LR-test for country effects0.050.090.00 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

20 17 October 2010203rd International Seminar, Moscow Goodness-of-fit across different revisions 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 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

21 17 October 2010213rd International Seminar, Moscow Additional regression results, annual data Dependent variable: cumulative revision 7 ( Δ R8-R1 y) (1)(2)(3)(4)(5) -0.39-0.47-0.49-0.42-0.48 (-6.09)(-5.63)(-6.00)(-5.98)(-5.91) 0.160.180.12 (2.19)(2.57)(1.90) 0.170.12 (2.52)(1.92) Adjusted R 2 0.470.49 0.50 Number of observations170 167 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

22 17 October 2010223rd 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 2009200420032005200620072008 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

23 17 October 2010233rd International Seminar, Moscow Releases of quarterly output gaps: unbalanced panel

24 17 October 2010243rd International Seminar, Moscow Average absolute revisions of quarterly OG, balanced panel

25 17 October 2010253rd International Seminar, Moscow Average revisions of quarterly OG, balanced panel

26 17 October 2010263rd International Seminar, Moscow Cumulative revisions of quarterly OG, balanced panel

27 17 October 2010273rd International Seminar, Moscow Efficiency regressions, quarterly data

28 17 October 2010283rd 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

29 17 October 2010293rd 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)

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