Nowcasting US GDP with artificial neural networks

Loermann, Julius and Maas, Benedikt (2019): Nowcasting US GDP with artificial neural networks.

Abstract

We use a machine learning approach to forecast the US GDP value of the current quarter and several quarters ahead. Within each quarter, the contemporaneous value of GDP growth is unavailable but can be estimated using higher-frequency variables that are published in a more timely manner. Using the monthly FRED-MD database, we compare the feedforward artificial neural network forecasts of GDP growth to forecasts of state of the art dynamic factor models and the Survey of Professional Forecasters, and we evaluate the relative performance. The results indicate that the neural network outperforms the dynamic factor model in terms of now- and forecasting, while it generates at least as good now- and forecasts as the Survey of Professional Forecasters.

• Item Type: MPRA Paper
• Language: English
• Keywords: Nowcasting; Machine learning; Neural networks; Big data
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles

References

Banbura, M., Giannone, D., Modugno, M., and Reichlin, L. (2013). Now-casting and the realtime data flow. In Handbook of economic forecasting, volume 2, pages 195–237. Elsevier.

Crone, S. F. and Kourentzes, N. (2010). Feature selection for time series prediction–a combined filter and wrapper approach for neural networks. Neurocomputing, 73(10-12):1923–1936.

Croushore, D. D. (1993). Introducing: the Survey of Professional Forecasters. Business Review-Federal Reserve Bank of Philadelphia, 6:3–15.

Diebold, F. X. and Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3).

Doz, C., Giannone, D., and Reichlin, L. (2011). A two-step estimator for large approximate dynamic factor models based on Kalman filtering. Journal of Econometrics, 164(1):188– 205.

Giannone, D., Reichlin, L., and Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4):665–676.

Heravi, S., Osborn, D. R., and Birchenhall, C. (2004). Linear versus neural network forecasts for european industrial production series. International Journal of Forecasting, 20(3):435–446.

Jung, J.-K., Patnam, M., and Ter-Martirosyan, A. (2018). An algorithmic crystal ball: Forecastsbased on machine learning. IMF Working Paper 230.

Kourentzes, N., Barrow, D. K., and Crone, S. F. (2014). Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9):4235–4244.

Kuan, C.-M. and Liu, T. (1995). Forecasting exchange rates using feedforward and recurrent neural networks. Journal of applied econometrics, 10(4):347–364.

Lachtermacher, G. and Fuller, J. D. (1995). Back propagation in time-series forecasting. Journal of Forecasting, 14(4):381–393.

McCracken, M. W. and Ng, S. (2016). FRED-MD: A monthly database for macroeconomic research. Journal of Business & Economic Statistics, 34(4):574–589.

Ord, K., Fildes, R., and Kourentzes, N. (2017). Principles of Business Forecasting–2nd Ed. wessex, Incorporated. 20

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning representations by backpropagating errors. nature, 323(6088):533.

Sims, C. A., Stock, J. H., and Watson, M. W. (1990). Inference in linear time series models with some unit roots. Econometrica: Journal of the Econometric Society, pages 113–144.

Tkacz, G. (2001). Neural network forecasting of Canadian GDP growth. International Journal of Forecasting, 17(1):57–69.

Torres, D. G. and Qiu, H. (2018). Applying recurrent neural networks for multivariate time series forecasting of volatile financial data.

Wallis, K. F. (1986). Forecasting with an econometric model: The “ragged edge” problem. Journal of Forecasting, 5(1):1–13.