Automatic Time Series Modelling and Forecasting: A Replication Case Study of Forecasting Real GDP, the Unemployment Rate, and the Impact of Leading Economic Indicators

30 Pages Posted: 7 May 2020

See all articles by John Guerard

John Guerard

Independent

Dimitrios D. Thomakos

University of Athens, Department of Business Administration

FOTEINH KYRIAZH

University of Peloponesse

Date Written: April 13, 2020

Abstract

We test and report on time series modelling and forecasting using several U.S. Leading Economic Indicators (LEI) as an input to forecasting real U.S. GDP and the unemployment rate. These time series have been addressed before, but our results are more statistically significant using more recently developed time series modelling techniques and software. Montgomery, Zarnowitz, Tsay, and Tiao (1998) modeled the U.S. unemployment rate as a function of the weekly unemployment claims time series, 1948 – 1992. In this replication case study, we apply the Hendry and Doornik automatic time series PC-Give (AutoMetrics) methodology to the well-studied macroeconomics series, U.S. real GDP and the unemployment rate. The Autometrics system substantially reduces regression sum of squares measures relative to traditional variations on the random walk with drift model. The LEI are a statistically significant input to real GDP. A similar conclusion is found for the impact of the LEI and weekly unemployment claims series leading the unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA-AR and the adaptive learning forecast, ADL, produced the smallest root mean square errors and lowest mean absolute errors.

Keywords: adaptive learning forecasting automatic time series modelling, forecasting, leading indicators

Suggested Citation

Guerard, John and Thomakos, Dimitrios D. and KYRIAZH, FOTEINH, Automatic Time Series Modelling and Forecasting: A Replication Case Study of Forecasting Real GDP, the Unemployment Rate, and the Impact of Leading Economic Indicators (April 13, 2020). Available at SSRN: https://ssrn.com/abstract=3577323 or http://dx.doi.org/10.2139/ssrn.3577323

Dimitrios D. Thomakos

University of Athens, Department of Business Administration ( email )

Athens
Greece

HOME PAGE: http://ba.uoa.gr/

FOTEINH KYRIAZH

University of Peloponesse ( email )

Tripolis, 22100
Greece

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