Flash Estimates of Household Final Consumption Expenditure in East Java

  • Rizky Zulkarnain Badan Pusat Statistik
Keywords: Big Data, Ensemble, Google Trends, Machine Learning, Nowcasting

Abstract

The purpose of this study is to construct a flash estimates model for household final consumption expenditure in East Java. There were 153 indicators that were used, covering Big Data and non Big Data indicators. Indicators from Big Data were the Google Trends indicators. The Google Trends categories were selected based on the highest correlation coefficient. While the non Big Data indicators were the total deposit, consumption credit, consumer lending rate, term deposit rate (1, 3, and 6 months), withdrawal (outflow), and Consumer Price Index (CPI). This study examines several models: ARIMA, ADL, Elastic Net, Support Vector Machine, Random Forest and Ensemble. The models were examined using various scenarios of out-of-sample and estimation periods (t+10 days, t+20 days and t+30 days). The prediction performance of models were evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The evaluation results showed that the Elastic Net and the Ensemble were the best model for any scenarios. Both models had good performance since t+10 days.

References

Aastveit, K. A., Fastbo, T. M., Granziera, E., Paulsen, K. S., & Torstensen, K. N. (2020). Nowcasting Norwegian Household Consumption with Debit Card Transaction Data. Norges Bank Research Working Paper No. 17.

Banbura, M., & Runstler, 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(2011), 333-346.

Banbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Now-Casting and The Real-Time Data Flow. European Central Bank Working Paper Series No. 1564.

BEA. (2022). Technical Note: Gross Domestic Product, First Quarter 2022 (Advance Estimate). Bureau of Economic Analysis.

Bok, B., Caratelli, D., Giannone, D., Sbordone, A., & Tambalotti, A. (2018). Macroeconomic Nowcasting and Forecasting with Big Data. Annual Review of Economics, 10, 615-643.

BPS Jawa Timur. (2022). Provinsi Jawa Timur dalam Angka, 2022. Surabaya: BPS Jawa Timur.

Buell, B., Chen, C., Cherif, R., Seo, H.-J., Tang, J., & Wendt, N. (2021). Impact of COVID-19: Nowcasting and Big Data to Track Economic Activity in Sub-Saharan Africa. IMF Working Paper, WP/21/124.

Castle, J., Hendry, D., & Kitov, O. (2017). Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview. Eurostat.

Ceron, A., Curini, L., & Iacus, S. M. (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. New York: Routledge.

Cryer, J., & Chan, K-S. (2008). Time Series Analysis with Applications in R (Second ed.). New York: Springer.

Damuri, Y. R., Tyas, P., Aswicahyono, H., Priyadi, L., Kusumawardhani, S., & Yazid, E. K. (2021). Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali. ERIA Discussion Paper Series No. 385.

Eurostat. (2016). Overview of GDP Flash Estimation Methods. Luxembourg: Publications Office of the European Union.

Gil, M., Perez, J., Sanchez-Fuentes, A., & Urtasun, A. (2017). Nowcasting Private Consumption: Traditional Indicators, Uncertainty Measures, and The Role of Internet Search Query Data. ISI World Statistics Congress.

Hastie, T., Tibshirani, R., & Friedman, J. (2017). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second ed.). New York: Springer.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. New York: Springer.

Luciani, M., Pundit, M., Ramayandi, A., & Veronese, G. (2015). Nowcasting Indonesia. ADB Economics Working Paper Series No. 471.

Montgomery, D., Jennings, C., & Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting (Second ed.). New Jersey: John Wiley & Sons, Inc.

Muchisha, N. D., Tamara, N., Andriansyah, & Soleh, A. M. (2021). Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms. Indonesian Journal of Statistics and Its Applications, 5(2), 355-368.

Richardson, A., Mulder, T. v., & Vehbi, T. (2018). Nowcasting New Zealand GDP Using Machine Learning Algorithms. Bank Indonesia International Workshop and Seminar on “Big Data for Central Bank Policies/Building Pathways for Policy Making with Big Data”.

Sampi, J., & Jooste, C. (2020). Nowcasting Economic Activity in Times of COVID-19: An Approximation from the Google Community Mobility Report. World Bank Policy Research Working Paper No. 9247.

Tarsidin, Idham, & Rakhman, R. N. (2018). Nowcasting Household Consumption and Investment in Indonesia. Bulletin of Monetary Economics and Banking, 20(3), 375-404.

Toth, I. J., & Hajdu, M. (2020). Google as a Tool for Nowcasting Household Consumption: Estimations on Hungarian Data. Institute for Eonomic and Enterprise Research.

Utari, D. T., & Ilma, H. (2018). Comparison of Methods for Mixed Data Sampling (MIDAS) Regression Models to Forecast Indonesian GDP Using Agricultural Exports. AIP Conference Proceeding: The 8th Annual Basic Science International Conference, (p. 060016).

Varlamova, J., & Larionova, N. (2015). Macroeconomic and Demographic Determinants of Household Expenditures in OECD Countries. Procedia Economics and Finance, 24(2015), 727-733.

Woloszko, N. (2020). Tracking Activity in Real Time With Google Trends. OECD Working Paper No. 1634, ECO/WKP(2020)42.

Woo, J., & Owen, A. (2018). Forecasting Private Consumption with Google Trends Data. Journal of Forecasting, 38(2).

Zou, H., & Hastie, T. (2005). Regularization and Variable Selection via The Elastic Net. Journal of The Royal Statistical Society, 67(2), 301-320.

Published
2022-08-26
How to Cite
Zulkarnain, R. (2022). Flash Estimates of Household Final Consumption Expenditure in East Java. East Java Economic Journal, 6(1), 60-85. https://doi.org/10.53572/ejavec.v6i1.84
Section
Articles