Are disaggregate data useful for factor analysis in forecasting French GDP?

Journal of Forecasting

Volumn 29, Issue 1-2, 2010, Pages 132-144

  Barhoumi, Karim ^a   Darné, Olivier ^a,b   Ferrara, Laurent ^a  

^a BANQUE DE FRANCE   (France)

^b UNIVERSITÉ DE NANTES   (France)

Author keywords

Data aggregation; Factor models; GDP forecasting

Indexed keywords

FREQUENCY DOMAIN ANALYSIS;

DATA AGGREGATION; FACTOR MODEL; FACTORS ANALYSIS; FORECASTING PERFORMANCE; FRENCH ECONOMY; GDP FORECASTING; MODEL-BASED OPC; PRINCIPAL COMPONENTS; STATICS AND DYNAMICS; TIMES SERIES;

FORECASTING;

EID: 77449148484     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.1162     Document Type: Article

References (34)

1. Angelini E, Camba-Méndez G, Giannone D, Rünstler G, Reichlin L. 2008. Short-term forecasts of Euro area GDP growth. Working paper no. 949, European Central Bank.
2. Bai J, Ng S. 2002. Determining the number of factors in approximate factor models. Econometrica 70: 191-221.
3. Bai J, Ng S. 2007. Determining the number of primitive shocks in factor models. Journal of Business and Economic Statistics 25: 52-60.
4. Boivin J, Ng S. 2005. Understanding and comparing factor-based forecasts. International Journal of Central Banking 1: 117-151.
5. Boivin J, Ng S. 2006. Are more data always better for factor analysis? Journal of Econometrics 132: 169- 194.
6. Breitung J, Eickmeier S. 2006. Dynamic factor models. In Modern Econometric Analysis, Hübler O, Frohn J (eds). Springer: Berlin; ch. 3.
7. Camba-Méndez G, Kapetanios G. 2005. Forecasting euro area inflation using dynamic factor measures of underlying inflation. Journal of Forecasting 25: 491-503.
8. Chevillon G. 2007. Direct multi-step estimation and forecasting. Journal of Economic Surveys 21: 746-785.
9. Clark TE, McCracken MW. 2009. Improving forecast accuracy by combining recursive and rolling forecasts. International Economic Review 50(2): 363-395.
10. D'Agostino A, Giannone D. 2007. Comparing alternative predictors based on large-panel factor models. Discussion paper no. 6564, Centre for Economic Policy Research.
11. Dias F, Pinheiro M, Rua A. 2008. Forecasting using targeted diffusion indexes. Working paper no. 7/2008, Banco de Portugal.
12. Doz C, Giannone D, Reichlin L. 2006. A quasi maximum likelihood approach for large approximate dynamic factor models. Discussion paper no. 5724, Centre for Economic Policy Research.
13. Doz C, Giannone D, Reichlin L. 2007. A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Discussion paper no. 6043, Centre for Economic Policy Research.
14. Eickmeier S, Ziegler C. 2008. How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach. Journal of Forecasting 27(3): 237-265.
15. Forni M, Hallin M, Lippi M, Reichlin L. 2004. The generalized dynamic factor model: consistency and rates. Journal of Econometrics 119: 231-255.
16. Forni M, Hallin M, Lippi M, Reichlin L. 2005. The generalized dynamic factor model: one-sided estimation and forecasting. Journal of the American Statistical Association 100: 830-840.
17. Ghysels E, Sinko A, Valkanov R. 2007. MIDAS regression: further results and new directions. Econometric Reviews 26: 53-90.
18. Giacomini R, White H. 2006. Tests of conditional predictive ability. Econometrica 74: 1545-1578.
19. Giannone D, Reichlin L, Small D. 2008. Nowcasting: the real-time informational content of macroeconomic data. Journal of Monetary Economics 55: 665-676.
20. Harvey D, Leybourne S, Newbold P. 1997. Testing the equality of mean squared errors. International Journal of Forecasting 13: 281-291.
21. Jungbacker B, Koopman SJ. 2008. Likelihood-based analysis for dynamic factor models. Working paper, VU University Amsterdam.
22. Kapetanios G, Marcellino M. 2006. A parametric estimation method for dynamic factor models of large dimensions. Discussion paper no. 5620, Centre for Economic Policy Research.
23. Krolzig H-M, Hendry DF. 2001. Computer automation of general-to-specific model selection procedures. Journal of Economic Dynamics and Control 25: 831-866.
24. Marcellino M, Schumacher C. 2008. Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP. Discussion paper no. 6708, Centre for Economic Policy Research.
25. Marcellino M, Stock J, Watson M. 2003. Macroeconomic forecasting in the euro area: country specific versus euro wide information. European Economic Review 47: 1-18.
26. Marcellino M, Stock J, Watson M. 2006. A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. Journal of Econometrics 135: 499-526.
27. Matheson TD. 2006. Factor model forecasts for New Zealand. International Journal of Central Banking 2: 169-237.
28. Rünstler G, Barhoumi K, Benk S, Cristadoro R, Den Reijer A, Jakaitiene A, Jelonek P, Ruth K, Van Nieuwenhuyze C. 2009. Short-term forecasting of GDP using large data sets: a pseudo real-time forecast evaluation exercise. Journal of Forecasting (forthcoming).
29. Schumacher C. 2007. Forecasting German GDP using alternative factor models based on large datasets. Journal of Forecasting 26: 271-302.
30. Stock J, Watson M. 2002a. Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics 20: 147-162.
31. Stock J, Watson M. 2002b. Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97: 1167-1179.
32. Stock J, Watson M. 2006. Forecasting with many predictors. In Handbook of Economic Forecasting, Vol. 1, Elliott G, Granger CWJ, Timmermann A (eds). North-Holland: Amsterdam; 515-554.
33. Watson M. 2003. Macroeconomic forecasting using many predictors. In Advances in Economics and Econometrics, Theory and Applications: Eighth World Congress of the Econometric Society, Vol. III, Dewatripont M, Hansen L, Turnovsky S (eds). 81-115.
34. White H. 1980. Maximum likelihood estimation of misspecified models. Econometrica 50: 1-25.