1. Estimating potential output using business survey data in a SVAR framework 3° annual WORKSHOP on Macroeconomic Forecasting Montreal 5-6 october 2007 Tatiana Cesaroni ISAE-ITALY

2. Motivation  Potential output and the related concept of output gap represent important concepts for economic policy evaluation and analysis  Most macroeconomic models include estimates of potential output  Potential output plays a key role in business cycle research

3. Contribution  Provide potential output and output gap estimates for Italy using information coming from business survey data  Compare the estimated output gap obtained with different methods (univariate vs multivariate decompositions)  Evaluate the reliability of the estimates at the end of sample  Compare peaks and troughs of the estimated output gap with turning points of the Italian official cyclical chronology

4. Definitions Potential output It is defined as the maximum capacity of a given economy It is defined as the maximum capacity of a given economy Output gap  It is defined as the difference between actual level of output and its potential.  It is used as indicator of the cyclical position of the economy

5. Measurement problems  Potential output represents a theoretical concept it is not observed and for this reason need to be estimated  The empirical evidence shows a significant sensibility of the estimates with respect to the method used (see Orphanides, and Van Norden, 2001)  The choice of the methodology is not unique since depends on more factors like the aim of the research, the statistical properties of data used etc.

6. Univariate detrending methods  Deterministic trend (quadratic trend)  Filters (Hodrick Prescott, Band Pass)  Unobserved components models Drawback Drawback We cannot use information coming from external data

7. Structural VAR decomposition The multivariate trend cycle decomposition method used is based on SVAR models with long run restrictions (Blanchard and Quah,1989)  Advantages  Possibility to give an economic interpretation to the shocks  Absence of a priori restrictions on the dynamics of trend/cycle components  Absence of end of sample problems (VAR is a backward method)

8. The model MA Representation of the structural form model is given by where vt indicate vector of the aggregate shocks such that and Xt= [Dyt, bst]. The AR representation of the reduced form (R.F) where represents the residuals vector and is the VCV matrix The associated MA representation of the Reduced Form The structural shocks can be derived from the innovations of the reduced form model: Knowledge of S(0) allows to obtain structural shocks from the innovations

9. Identification scheme Bivariate model : Restriction: Only supply shocks can produce a long run impact on GDP

10. SVAR Trend/Cycle decomposition  Trend is a measure of potential output  The cyclical component is a measure of output gap  Case of bivariate model Considering only the first equation we have:

11. Business survey data

12. t-4t-3t-2t-1tt+1t+2t+3t+4 Plant utilizatio n -0.060.170.390.590.710.740.680.580.46 Inventori es 0.580.470.300.08-0.14-0.29-0.37-0.4-0.38 Prod. level -0.42-0.170.110.380.570.660.640.570.48 Order book -0.46-0.210.080.340.570.680.690.620.53 Prod. exp -0.42-0.26-0.030.230.450.570.600.540.44 Climate-0.52-0.32-0.040.260.500.630.660.610.52 Cross correlations with GDP (period 1986q1-2003q4)

13. Empirical results  Data  Output: Italian GDP quarterly data seasonally adjusted (at constant prices and base 1995) 1980Q1-2005Q1 Source:ISTAT  Degree of plants utilization: quarterly frequency 1985Q1-2005Q1  Source:ISAE  Trend/cycle decompositions used  Quadratic trend  Hodrick Prescott filter  Baxter and King filter  Bivariate SVAR model (GDP and degree of capacity utilization)

14. SVAR model

15.  Reliability of real time estimates  For short term analysis purposes is important to obtain reliable estimates at the end of sample  Impact of revisions  The availability of new information allow to identify more precisely the cyclical position of the economy  Revisions formula  where indicates the estimates at time t, obtained using information available in t+T and indicates the estimates at period t, made using the informative set available in t+i con i<T.

16. Impact of revisions t=2002:4 Pt/t+9- Pt/t+1 Pt/t+9- Pt/t+2 Pt/t+9- Pt/t+3 Pt/t+9- Pt/t+4 Pt/t+9- Pt/t+5 Pt/t+9- Pt/t+6 Pt/t+9- Pt/t+7 Pt/t+9- Pt/t+8 Pt/t+9- Pt/t+9 SAMPLE1980:12003:11980:12003:21980:12003:31980:12003:41980:12004:11980:12003:21980:12004:31980:12004:4 1980: 1 2005: 1 TL1.030.890.780.650.520.400.30.160 TQ0.540.450.380.30.250.210.190.110 HP0.840.550.390.230.150.120.110.060 SVAR0.0150.0160.0100.0080.0080.0100.0080.0070

17. Analysis of turning points of output gap indicators  It is important to evaluate of the ability of the cyclical components obtained through the different methods to indicate the turning points of the cyclical official chronology for Italy.  Cyclical official dating chronology  Turning points are determined on the basis of the dynamics of the series included in the coincident indicator for the italian economy ( information on GDP, ind. Production, imports of investment goods, share of over time hours, railway transport of goods,  (see Altissimo et al., 1999)

18. Detecting turning points (quadratic trend)

19. Detecting turning points (Hodrick Prescott)

20. Detecting turning points (Baxter and King)

21. Detecting turning points (SVAR)

22. Conclusions  The output gap estimates are sensitive to the different trend cycle estimation techniques  The use of business survey data into multivariate models allow to capture information on business cycle activity.  The analyisis of the impact of revisions due to the availability of new information, highligths an high degree of reliability of real time estimates obtained with SVAR models  The output gap estimates obtained with VAR models are able to detect quite precisely the turning points of the cyclical official chronology as well as traditional unvariate decompositions