Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts. (English) Zbl 07788753
Summary: In this paper, we discuss the most recent developments in temporal disaggregation techniques carried out at the Istituto Nazionale di Statistica (ISTAT). They concern the extension from static to dynamic autoregressive distributed lag regressions and the adoption of the state-space framework for the statistical treatment of temporal disaggregation. Beyond the development of a unified procedure for both static and dynamic methods from one side and the treatment of the logarithmic transformation from the other, we provide short guidelines for model selection. The inclusion of stochastic trends in the regressions is also discussed. We evaluate the new dynamic methods by implementing a large-scale exercise using the ISTAT annual value added data jointly with quarterly industrial production over the 1995–2013 period. The main finding is that autoregressive distributed lag disaggregations reduce forecast errors in comparison to static variants, at a price of lower correlations with related high-frequency indicators. Moreover, problematic outcomes are limited to few cases.
{© 2018 The Authors. Statistica Neerlandica © 2018 VVS.}
{© 2018 The Authors. Statistica Neerlandica © 2018 VVS.}
MSC:
| 91Bxx | Mathematical economics |
| 62Mxx | Inference from stochastic processes |
| 62Pxx | Applications of statistics |
Keywords:
ADL models; Kalman filter; linear Gaussian approximating model; quarterly national accounts; state-space form; temporal disaggregationSoftware:
OxReferences:
| [1] | Barbone, L., Bodo, G., & Visco, I. (1981). Costi e profitti in senso stretto: Un’analisi su serie trimestrali, 1970-1980. Bollettino della Banca d’Italia, 36, numero unico. |
| [2] | Bisio, L., & Moauro, F. (2017). Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts. MPRA Paper. Munich, Germany: University Library of Munich. Retrieved from https://mpra.ub.uni-muenchen.de/87518/ |
| [3] | Castle, J. L., Doornik, J. A., & Hendry, D. F. (2011). Evaluating automatic model selection. Journal of Time Series Econometrics, 3. Retrieved from https://doi.org/10.2202/1941-1928.1097. · Zbl 1266.91057 · doi:10.2202/1941-1928.1097 |
| [4] | Chow, G. C., & Lin, A. (1971). Best linear unbiased interpolation, distribution and extrapolation of time series by related series. The Review of Economics and Statistics, 53, 372-375. |
| [5] | Denton, F. T. (1971). Adjustment of monthly or quarterly series to annual totals: An approach based on quadratic minimization. Journal of the American Statistical Association, 66, 99-102. · Zbl 0216.22801 |
| [6] | Di FonzoT. (2005). Rapporto finale della Commissione di studio sul trattamento dei dati ai fini dell’analisi congiunturale. Il lavoro svolto e i risultati ottenuti. Rome, Italy: Istituto Nazionale di Statistica. |
| [7] | Doornik, J. A. (2013). Ox 7.01: An object‐oriented matrix programming language. London, UK: Timberlake Consultants. |
| [8] | Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37, 409-428. · Zbl 0039.35803 |
| [9] | Durbin, J., & Watson, G. S. (1951). Testing for serial correlation in least squares regression. II. Biometrika, 38, 159-179. · Zbl 0042.38201 |
| [10] | Econometric Modeling and Computing Corporation (2009a). The Modeleasy+ reference manual. Palm Harbor, FL. |
| [11] | Econometric Modeling and Computing Corporation (2009b). The speakeasy reference manual. Palm Harbor, FL. |
| [12] | Farebrother, R. W. (1980). The Durbin‐Watson test for serial correlation when there is no intercept in the regression. Econometrica, 48, 1553-1563. · Zbl 0469.62100 |
| [13] | Fernàndez, R. B. (1981). A methodological note on the estimation of time series. The Review of Economics and Statistics, 63, 471-476. |
| [14] | Frale, C., Marcellino, M., Mazzi, G. L., & Proietti, T. (2010). Survey data as coincident or leading indicators. Journal of Forecasting, 29, 109-131. · Zbl 1205.91128 |
| [15] | Frale, C., Marcellino, M., Mazzi, G. L., & Proietti, T. (2011). Euromind: A monthly indicator of the Euro area economic conditions. Journal of the Royal Statistical Society, 174, 439-470. |
| [16] | Grassi, S., Proietti, T., Frale, C., Marcellino, M., & Mazzi, G. (2014). EuroMInd‐C: A disaggregate monthly indicator of economic activity for the Euro area and member countries. International Journal of Forecasting. Accepted for publication. |
| [17] | Harvey, A., & Chung, C.‐H. (2000). Estimating the underlying change in unemployment in the UK. Journal of the Royal Statistical Society Series A, 163, 303-309. |
| [18] | Harvey, A. C. (1989). Forecasting, structural time series models and the Kalman filter. Cambridge, UK: Cambridge University Press. |
| [19] | Harvey, A. C., & Koopman, S. J. M. (1997). Multivariate structural time series models (with comments). In C.Heij (ed.), J. M.Shumacher (ed.), B.Hanzon (ed.), & C.Praagman (ed.) (Eds.), System dynamics in economic and financial models (pp. 269-298). New York, NY: Wiley. |
| [20] | Harvey, A. C., & Pierce, R. G. (1984). Estimating missing observations in economic time series. Journal of the American Statistical Association, 79, 125-131. |
| [21] | Hendry, D. F., & Mizon, G. E. (1978). Serial correlation as a convenient simplification, not a nuisance: A comment on a study of the demand for money by the Bank of England. Economic Journal, 88, 549-563. |
| [22] | Jarque, C. M., & Bera, A. K. (1987). A test for normality of observations and regression residuals. International Statistical Review, 55, 163-172. · Zbl 0616.62092 |
| [23] | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(Series D), 35-45. |
| [24] | Litterman, R. B. (1983). A random walk, Markov model for the distribution of time series. Journal of Business and Economic Statistics, 1, 169-173. |
| [25] | Ljung, G. M., & Box, G. E. P. (1978). On a measure of a lack of fit in time series models. Biometrika, 65, 297-303. · Zbl 0386.62079 |
| [26] | Lupi, C., & Parigi, G. (1996). La disaggregazione temporale di serie economiche: Un approccio econometrico. Quaderni di Ricerca 3. Rome, Italy: Istituto Nazionale di Statistica. |
| [27] | Moauro, F. (2014). Monthly employment indicators of the Euro area and larger member states: Real‐time analysis of indirect estimates. Journal of Forecasting, 33, 339-349. · Zbl 1397.91510 |
| [28] | Moauro, F., & Savio, G. (2005). Temporal disaggregation using multivariate structural time series models. The Econometrics Journal, 8, 214-234. · Zbl 1095.91047 |
| [29] | Proietti, T. (2005). Rapporto finale della Commissione di studio sul trattamento dei dati ai fini dell’analisi congiunturale. Temporal disaggregation by state space methods: Dynamic regression methods revisited. Rome, Italy: Istituto Nazionale di Statistica. · Zbl 1147.62367 |
| [30] | Proietti, T. (2006a). Temporal disaggregation by state space methods: Dynamic regression methods revisited. The Econometrics Journal, 9, 357-372. · Zbl 1147.62367 |
| [31] | Proietti, T. (2006b). On the estimation of nonlinearly aggregated mixed models. Journal of Computational and Graphical Statistics, 15, 18-38. |
| [32] | Proietti, T., & Moauro, F. (2006). Dynamic factor analysis with non‐linear temporal aggregation constraints. Journal of the Royal Statistical Society: Series C (Applied Statistics), 55, 281-300. · Zbl 1490.62475 |
| [33] | Santos Silva, J. M. C., & Cardoso, F. N. (2001). The Chow‐Lin method using dynamic models. Economic Modelling, 18, 269-280. |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
