Analysis of forecasts of GDP growth and inflation for the Croatian economy

Abstract

The paper provides, for the first time, the analysis of the quality of the GDP growth and inflation forecasts by multiple forecasters for the Croatian economy. Forecast data of 6 different institutions in the 2006-2015 period are analysed. Efficiency and biasedness test are conducted following the Davies and Lahiri econometric framework based on a three-dimensional panel dataset which includes multiple individual forecasters, target years and forecast horizons. In order to assess directional accuracy we follow the approach by Pesaran and Timmermann. Based on MAE values we find the forecasts to be accurate on a scale comparable to the European Commission's forecast reported in 2016 for the EU and the euro area. GDP growth forecasts exhibit a strong bias related to a notable tendency to over-predict GDP growth. In the case of inflation forecasting the bias is still present for all forecasters, albeit less pronounced and not statistically significant for all of them. There is evidence of forecast inefficiency regarding both analysed variables. Overall, inflation forecasting presents less of a challenge due to specific monetary policy strategy and inaccurate national accounts data accompanied by extended revision process of the GDP data by the government's statistics office.

Future growth pattern projections under shared socioeconomic pathways: a municipal city bottom-up aggregated study based on a localised scenario and population projections for China

“…As long-term economic projections are highly uncertain (Vlah Jeri c et al, 2020), international economic institutions such as the IMF (2020) and World Bank (2020a) tend to base their outlooks on short-term working hypotheses. In general, previous efforts to develop global environmental scenarios have generated economic output scenarios by assuming an exogenous regional growth rate of GDP or productivity IEA, 2009;Kemp-Benedict et al, 2002;Naki cenovi c & Swart, 2000;Raskin et al, 2002), or by using complicated models involving mathematical recurrence or trend extrapolation in the projection of major growth drivers (Hertel, 1999;Huang et al, 2019;Jiang et al, 2018;Leimbach et al, 2017;Lejour et al, 2006).…”