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2020 | 7 | nr 54 | 1--11
Tytuł artykułu

Macroeconomic Forecasting in Poland: the Role of Forecasting Competitions

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Macroeconomic forecasters are often believed to idealistically work on improving the accuracy of their estimates based on for example the Root Mean Squared Error (RMSE). Unfortunately, reality is far more complex. Forecasters are not awarded equally for each of their estimates. They have their targets of acquiring publicity or to earn prestige. This article aims to study the results of Parkiet's competitions of macroeconomic forecasting during 2015-2019. Based on a logit model, we analyse whether more accurate forecasting of some selected macroeconomic variables (e.g. inflation) increases the chances of winning the competition by a greater degree comparing to the others. Our research shows that among macroeconomic variables three groups have a significant impact on the final score: inflation (CPI and core inflation), the labour market (employment in the enterprise sector and unemployment rate) and financial market indicators (EUR/PLN and 10-year government bond yields). Each group is characterised by a low disagreement between forecasters. In the case of inflation, we found evidence that some forecasters put a greater effort to score the top place. There is no evidence that forecasters are trying to somehow exploit the contest. (original abstract)
Rocznik
Tom
7
Numer
Strony
1--11
Opis fizyczny
Twórcy
  • SGH Warsaw School of Economics, Poland
Bibliografia
  • Ashiya, M. (2009). Strategic bias and professional affiliations of macroeconomic forecasters. Journal of Forecasting, 28(2), 120-130.
  • Bacchetta, P., & Van Wincoop, E. (2007). Random walk expectations and the forward discount puzzle. American Economic Review, 97(2), 346-350.
  • Elliott, G., Komunjer, I., & Timmermann, A. (2008). Biases in macroeconomic forecasts: irrationality or asymmetric loss? Journal of the European Economic Association, 6(1), 122-157.
  • Eroglu, C., & Croxton, K. L. (2010). Biases in judgmental adjustments of statistical forecasts: The role of individual differences. International Journal of Forecasting, 26(1), 116-133.
  • Hann, R. N., Ogneva, M., & Sapriza, H. (2012). Forecasting the macroeconomy: Analysts versus economists 2012. Available at SSRN.
  • Hess, D., & Orbe, S. (2013). Irrationality or efficiency of macroeconomic survey forecasts? Implications from the Anchoring Bias Test. Review of Finance, 17(6), 2097-2131.
  • Kilian, L., & Taylor, M. P. (2003). Why is it so difficult to beat the random walk forecast of exchange rates? Journal of International Economics, 60(1), 85-107.
  • Lamont, O. A. (2002). Macroeconomic forecasts and microeconomic forecasters. Journal of Economic Behavior & Organization, 48(3), 265-280.
  • Loungani, P., Stekler, H., & Tamirisa, N. (2013). Information rigidity in growth forecasts: Some crosscountry evidence. International Journal of Forecasting, 29(4), 605-621.
  • Marinovic, I., Ottaviani, M., & Sorensen, P. (2013). Forecasters' objectives and strategies. Handbook of Economic Forecasting, 2, 690-720.
  • Ottaviani, M., & Sørensen, P. N. (2006). The strategy of professional forecasting. Journal of Financial Economics, 81(2), 441-466.
  • Pierdzioch, C., Rülke, J. C., & Stadtmann, G. (2012). A note on forecasting emerging market exchange rates: Evidence of anti-herding. Review of International Economics, 20(5), 974-984.
  • Pons-Novell, J. (2004). Behavioural biases among interest rate forecasters? Applied Economics Letters, 11(5), 319-321.
  • Scharfstein, D. S., & Stein, J. C. (1990). Herd behavior and investment. The American Economic Review, 80, 465-479.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171603287

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