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A dynamic factor model to assess the real time state of the Spanish industry using confidence indicators Ángel Cuevas Research and Analysis Unit Ministry.

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Presentation on theme: "A dynamic factor model to assess the real time state of the Spanish industry using confidence indicators Ángel Cuevas Research and Analysis Unit Ministry."— Presentation transcript:

1 A dynamic factor model to assess the real time state of the Spanish industry using confidence indicators Ángel Cuevas Research and Analysis Unit Ministry of Industry, Energy and Tourism of Spain (MINETUR- prevMITYC) SUBSECRETARÍA DE INDUSTRIA, ENERGÍA Y TURISMO SECRETARÍA GENERAL TÉCNICA Subdirección General de Estudios, Análisis y Planes de Actuación EU workshop on business and consumer surveys (BCS) Brussels, 15 th -16 th November 2012

2 BACKGROUND Interest in improving the measurement of the state of the industrial business cycle for purposes of: Anticipation of adverse situations. Evaluation and implementation of economic and industrial policies. Improvement in databases of economic indicators. Advances in econometric methods for time series. Development of computer tools. Stock and Watson (1991, 2002), Gayer and Genet (2006), Angelini et al. (2008) Camacho and Perez-Quirós (2009), Cuevas and Quilis (2011). Objective: Multivariate modeling of a broad and representative set of monthly indicators of industrial activity, with the purpose of prediction, analysis and monitoring and forecasting of macroeconomic aggregates (industrial GVA). 2

3 Inputs Preprocessing Dynamic factor model {Treatment unbalanced panel} 3 Applications

4 Selection of indicators High frequency indicators (monthly). Must provide a synthetic measure of the Spanish industrial activity. They must be available promptly. They must be correlated with the reference series: Industrial Production Index (IPI) 4

5 5 Selection of indicators Prove the correlation with the IPI: Cross-correlation with the growth signal of SAC series. Cyclical Analysis: Butterworth (band-pass) + classification of the turning points (Bry-Boschan).

6 6 Selection of indicators Leading indicators are highlighted in yellow

7 7 Leading indicators Cross-correlation: y-o-y rates/differences Cross-correlation: y-o-y rates/differences

8 8 Leading indicators Cross-correlation: m-o-m rates/differences Cross-correlation: m-o-m rates/differences

9 9 Leading indicators Cross-correlation: q-o-q rates/differences (quarterly frequency) Cross-correlation: q-o-q rates/differences (quarterly frequency)

10 10 Leading indicators Cyclical Analysis: ICI

11 11 Leading indicators Cyclical Analysis: Car registrations

12 12 Leading indicators Cyclical Analysis: PMI

13 Inputs Preprocessing Dynamic factor model {Treatment unbalanced panel} 13 Applications

14 Preprocessing The series are adjusted for seasonal and calendar effects (if such effects are significant). Logarithmically transformed. Regular differences. The above variables are standardized. 14 (Soft)

15 Inputs Preprocessing Dynamic factor model Dynamic factor model {Treatment unbalanced panel} 15 Applications

16 16 ftft z 1,t z 2,t z 3,t u 1,t u 2,t u 3,t Static factor model 1 (B) 2 (B) 3 (B) e 1,t e 2,t e 3,t (B) atat Common dynamic Idiosyncratic dynamics

17 17 Dynamic factor model: complete representation

18 Factor model: dynamics 18

19 19 Factor model: estimation (Nº. factors)

20 20 Dynamic factor model: estimation (f) The common factor and its standard deviation are estimated by Kalman filter, adjusting the dynamic factor model to state space representation.

21 21 Dynamic factor model: loadings

22 Inputs Preprocessing Dynamic factor model {Treatment unbalanced panel} {Treatment unbalanced panel} 22 Applications

23 23 Estimation with an unbalanced panel

24 24 Estimation with an unbalanced panel

25 25 Estimation with an unbalanced panel

26 26 Estimation with an unbalanced panel

27 27 Estimation with an unbalanced panel

28 28 Estimation with an unbalanced panel

29 Inputs Preprocessing Dynamic factor model {Treatment unbalanced panel} 29 Applications

30 30 Industrial GVA and dynamic factor

31 31 Industrial GVA and dynamic factor Cross-correlation: y-o-y rates Cross-correlation: y-o-y rates Cross-correlation: q-o-q rates Cross-correlation: q-o-q rates

32 32 Forecasting and interpolation of industrial GVA Benchmarking method: Chow-Lin, Fernández Forecasting performance, 2003:Q1 – 2012:Q1 RMSE ARIMA2,755 DFM + Bench.1,461 ISI (MEC) + Bench.1,707

33 33 Markov swithching model

34 34 Conclusions It has developed a coincident indicator of Spanish industrial activity, trying to exploit all possible information from various related monthly indicators. The presence of leading indicators is critical in order to project the factor and anticipate the evolution of industrial activity in real time. These leading indicators are ICI, PMI and car registrations and they have a lead of three months. The methodology allows not only to estimate this factor, but also get individual predictions in a multivariate context of all the indicators included in the model. With the estimated factor there are various options for use: Perform a real time prediction of IGVA Translate its variations into probabilities of recession This work can be extended in many directions: transfer functions, more sophisticated Markov switching models, etc.

35 35 Angelini, E., Camba-Méndez, G., Giannone, D., Reichlin, L., Runstler, G (2008) Short-term forecasts of Euro area GDP growth. CEPR Discussion Paper n Camacho M, Pérez-Quirós G (2010) Introducing the Euro-STING: Short Term Indicator of Euro Area Growth. Journal of Applied Econometrics. Cuevas, A. & Quilis, E.M. (2011) A factor analysis for the Spanish economy. SERIEs Journal of the Spanish Economic Association. Gayer, C. & Genet, J.(2006) Using Factor Models to Construct Composite Indicators from BCS Data - A Comparison with European Commission Confidence Indicators. Economic Papers N.240, European Commission. Kim, C.-J. & Nelson, C.R. (1999) State-Space Models with Regime Switching, The MIT Press. 35 References

36 Thanks for your attention Ángel Cuevas Research and Analysis Unit Ministry of Industry, Energy and Tourism of Spain (MINETUR) SUBSECRETARÍA DE INDUSTRIA, ENERGÍA Y TURISMO SECRETARÍA GENERAL TÉCNICA Subdirección General de Estudios, Análisis y Planes de Actuación EU workshop on business and consumer surveys (BCS) Brussels, 15 th -16 th November 2012


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