scholarly journals The macroeconomic projections of the German government: A comparison to an independent forecasting institution

2020 ◽  
Vol 21 (2) ◽  
pp. 235-270
Author(s):  
Robert Lehmann ◽  
Timo Wollmershäuser
Keyword(s):  
Forecast Accuracy ◽  
Economic Forecast ◽  
German Government ◽  
Systematic Failure ◽  
Trend Growth

AbstractThis paper investigates the macroeconomic projections of the German government since the 1970s and compares it to those of the Joint Economic Forecast, which is an independent forecasting institution in Germany. Our results indicate that both nominal GDP projections are upward biased for longer forecast horizons, which seems to be driven by a false assessment of the decline in Germany’s trend growth and a systematic failure to correctly anticipate recessions. Furthermore, we show that the German government deviates from the projections of the Joint Economic Forecast, which in fact worsened the forecast accuracy. Finally, we find evidence that these deviations are driven by political motives.

Regressive Oil Price Expectations Toward More Fundamental Values of the Oil Price

2010 ◽  
Vol 230 (4) ◽  
Author(s):  
Stefan Reitz ◽  
Jan-Christoph Rülke ◽  
Georg Stadtmann
Keyword(s):  
Random Walk ◽  
Rational Expectations ◽  
Forecast Accuracy ◽  
Oil Price ◽  
Economic Forecast ◽  
Roller Coaster ◽  
Price Expectations ◽  
Oil Market ◽  
Time Period ◽  
Fundamental Values

SummaryWe use oil price forecasts from the Consensus Economic Forecast poll for the time period Oct. 1989 - Dec. 2008 to analyze how forecasters form their expectations. Our findings indicate that the extrapolative as well as the regressive expectation formation hypothesis play a role. Standard measures of forecast accuracy reveal forecasters’ under performance relative to the random walk benchmark. We test the hypothesis of rational expectations by relying on the criteria of unbiasedness and orthogonality. Although both conditions are met, the forecast accuracy is significantly lower compared to naïve random walk forecast. The forecasters have problems to forecast the trends in the oil price. The recent roller-coaster movements in the international oil market have revealed forecasters’ inability to predict major trends in the spot oil price. As a consequence, some research institutes have stopped forecasting the oil price as an ingredient of their macroeconomic models and use a random walk forecast instead.

scholarly journals Measurement of Economic Forecast Accuracy: A Systematic Overview of the Empirical Literature

2021 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
Goran Buturac
Keyword(s):  
Systematic Review ◽  
Literature Review ◽  
Forecast Accuracy ◽  
Empirical Literature ◽  
Economic Forecast ◽  
Methodological Framework ◽  
Methodological Development ◽  
Advantages And Disadvantages ◽  
Methodological Approaches ◽  
Insight Into

The primary purpose of the paper is to enable deeper insight into the measurement of economic forecast accuracy. The paper employs the systematic literature review as its research methodology. It is also the first systematic review of the measures of economic forecast accuracy conducted in scientific research. The citation-based analysis confirms the growing interest of researchers in the topic. Research on economic forecast accuracy is continuously developing and improving with the adoption of new methodological approaches. An overview of the limits and advantages of the methods used to assess forecast accuracy not only facilitate the selection and application of appropriate measures in future analytical works but also contribute to a better interpretation of the results. In addition to the presented advantages and disadvantages, the chronological presentation of methodological development (measures, tests, and strategies) provides an insight into the possibilities of further upgrading and improving the methodological framework. The review of empirical findings, in addition to insight into existing results, indicates insufficiently researched topics. All in all, the results presented in this paper can be a good basis and inspiration for creating new scientific contributions in future works.

Economic Forecast: Rough Road Ahead

2012 ◽  
Vol 43 (4) ◽  
pp. 1-10
Author(s):  
BRUCE JANCIN
Keyword(s):  
Economic Forecast

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

On the Structure of Analyst Research Portfolios and Forecast Accuracy

CFA Digest ◽  
2010 ◽  
Vol 40 (1) ◽  
pp. 74-76
Author(s):  
Stephen Phillip Huffman
Keyword(s):  
Forecast Accuracy

Feedback structure and EFAS flood warning verification for the rivers in the northwestern Russian Federation

2020 ◽  
Vol 4 ◽  
pp. 96-109
Author(s):  
A.V. Romanov ◽  
◽  
M.V. Yachmenova ◽  
Keyword(s):  
Russian Federation ◽  
Forecast Accuracy ◽  
Warning System ◽  
General Description ◽  
System Quality ◽  
Flood Warning ◽  
Forecast Lead Time ◽  
Feedback Structure ◽  
The Russian Federation ◽  
Flood Warning System

Based on the example of flood warning data provided by EFAS for the territory of Northwestern Administration for Hydrometeorology and Environmental Monitoring in 2018-2020, the structure of the systematized issues of the EFAS portal is analyzed. The issues determine a feedback for the year-round monitoring of the accuracy of flood forecasting using the LISFLOOD base model, as well as its calibration. Several most important feedback sections are highlighted, that allow improving significantly a procedure for the quantitative and qualitative differentiated assessment of short- and medium-range flood forecasts. Using the results of the numerical analysis, a general description of the EFAS flood warning system quality and the prospects for the participation of the Russian Federation in it are given. Keywords: flooding, hydrological forecasts, forecast lead time, feedback, forecast accuracy

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