A Dynamic Factor Model for Nowcasting Household Consumption
DOI:
https://doi.org/10.29244/ijsa.v6i2p202-212Keywords:
deseasonalized, factor analysis, gross domestic product, quartimax, varimaxAbstract
A Dynamic Factor Model (DFM) is one of the time series models that can be used to forecast within a very short period in the future known as nowcasting. This model can be used to accommodate the frequency difference that exists between monthly explanatory variables and a response variable which is measured quarterly. This model has been commonly used in economics especially to forecast household consumption for the purpose of constructing economic policies. The economic condition of a country can be reflected in the country's Gross Domestic Product (GDP). Consumption is an important component of GDP because of its large proportion of GDP. One of the household economic activities to meet the various needs of goods and services is referred to as household consumption. This paper discusses the DFM to forecast household consumption based on the varimax and quartimax rotations. The results show that both rotational methods can be used for transmitting household consumption with the same precision.
Downloads
References
Bai, J., & Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1): 191.
Banbura, M., & Rünstler, G. (2011). A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP. International Journal of Forecasting, 27: 333–346.
Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Nowcasting: The Real-Time Informational Content of Macroeconomic Data, 55(4): 665–676.
Hair, J. F., Black, W. C., & Babin, B. J. (2010). Multivariate Data Analysis (7th ed.). New York: Prentice Hall International Inc.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice (2nd ed.). Melbourne: OTexts.
Lamprou, D. (2015). Nowcasting GDP in Greece: A note on forecasting improvements from the use of bridge models. South- Eastern Europe Journal of Economics, 85–100.
Mariano, R. S., & Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. Journal of Applied Econometrics, 18(4): 427–443.
Montgomery. (2015). Introduction to Time Series Analysis and Forecasting: Second Edition (2nd ed.). New Jersey: John Wiley & Sons.
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Pearson.
Valk, D. S., Mattos, D. D., & Ferreira, P. (2019). Nowcasting: An R Package for Predicting Economic Variables Using Dynamic Factor Models. The R Journal, 11(1): 1–15.
Wooldridge, J. M. (2015). Introductory Econometrics: A Modern Approach (7th ed.). Massachusetts: Cengage.
