Consensus Economics – Asia Pacific 2019 Forecast Accuracy Award Winners

London, May 6, 2020: Consensus Economics, the world’s leading economic survey organization, has announced the recipients of its 2019 Forecast Accuracy Award (FAA). The FAA program recognises the achievements of a select group of expert country economic forecasters who have most accurately predicted the final outturns of GDP growth and Consumer Price Inflation in their targeted economies for the year 2019.

Australia Forecast Accuracy 2019

FAA winners vary from year to year and across variables, due to shocks, surprises and cyclical and structural adjustments. However, the winners of the 2019 FAA program have been recognised for their high quality research, their commitment to regular forecasts and their ability to identify most accurately the trends and levels of key indicators over the 24 month forecasting cycle.

2019 Forecast Accuracy Award Winners

Country Company Economists
Australia Westpac Banking Corporation Bill Evans, Andrew Hanlan, Justin Smirk,
Matthew Hassan, Elliot Clarke
China Economist Intelligence Unit Tom Rafferty, Nick Marro, Dan Wang, Yue Su, Tianjun Wu, Imogen Page-Jarrett
Hong Kong Bank of China (Hong Kong) Ricky Choi
Indonesia JP Morgan Chase Sin Beng Ong
Japan Deutsche Securities Kentaro Koyama
Malaysia Capital Economics Alex Holmes
New Zealand Capital Economics Ben Udy, Marcel Thieliant
Philippines Capital Economics Alex Holmes
Singapore Capital Economics Alex Holmes
South Korea Capital Economics Alex Holmes
Taiwan Goldman Sachs Asia Goohoon Kwon, Helen Hu, Irene Choi
Thailand Nomura Euben Paracuelles, Charnon Boonnuch

Consensus Economics collects forecasts from over 1,000 economists around the world each month. It was founded in 1989 to measure consensus expectations, which are seen as macroeconomic forecast benchmarks by investment and planning managers, as well as government and public sector institutions, who find the Consensus data useful, timely and accurate.

Each monthly Consensus Forecasts publication shows the average (mean) forecasts, as well as individual forecaster predictions. It is distributed in hard-copy, PDF, and Excel formats, and is also available across major statistical data platforms.
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Forecast Accuracy Awards – Methodology Note

The Forecast Accuracy Awards were determined using Mean Absolute Error analysis. The errors measured were the differences between the forecasts and the actual data outturns. The forecaster with the lowest average error rate was deemed to have been the most accurate over the testing period.

The Forecasts Used The forecasts we examined were our panellists’ monthly survey contributions for 2019 Real GDP growth and 2019 Consumer Price Inflation (CPI) expectations (annual average % change). We began collecting these forecasts in January 2018, and the 24 month forecasting cycle ended in December 2019, providing 24 monthly data points to test for each variable.

The Outturns Used The outturns we used as a comparison to the forecasts were the official estimates of 2019 GDP and CPI, which were released between January and April 2020, depending on the country and variable concerned.

The Calculation When calculating the error (the difference between the forecast and the outturn) we looked at the absolute errors, ignoring sign, as errors resulting from over-estimation and under-estimation are equivalent. We then calculated the mean average absolute error for the forecasts of each panellist for both GDP and CPI over the 24 month forecasting period.

Establishing the Most Accurate Forecaster To determine the most accurate forecaster for a given year, we added the mean absolute error rates for GDP and Inflation to identifiy the panellist with the lowest overall error rate. Smaller errors are best when considering accuracy, and to win the award requires the panellist to have exhibited a strong forecasting performance across both GDP and Inflation variables over the 24 month forecasting horizon.

Qualification To be considered for the award, a panellist needed to have participated consistently in our monthly surveys over the 24 month forecasting period. This ensured that no panellist gained an advantage through non-participation, or by only providing forecasts nearer the end of the 24 month cycle, when the steady release of official data can assist forecasters in making revisions.