An assessment of NIESR forecast accuracy

2006 ◽  
Vol 196 ◽  
pp. 36-39 ◽  
Author(s):  
Ray Barrell ◽  
Robert Metz
Keyword(s):  
Random Walk ◽  
Forecast Accuracy ◽  
Simple Rule ◽  
Forecast Horizon ◽  
Inflation Forecasts ◽  
Similar Vein ◽  
Point Forecast ◽  
Measure Of Uncertainty ◽  
Economic Forecasts ◽  
Better Than

The Institute periodically reviews the accuracy of its macroeconomic forecasts. Pain et al. (2001) compare the performance of NIESR output forecasts to a naïve forecast that uses a simple rule to predict growth next year. They find that between 1980 and 2000 the National Institute forecast performed better than a naïve, or random walk, forecast in two years out of three. Poulizac et al. (1996) consider a sequence of quarterly economic forecasts published by NIESR between 1982 and 1995 (beginning with that produced in February for the growth of GDP and inflation in the following year and finishing with the forecast produced in November for growth in the current year). They show how the reliability of the Institute's forecast improves as the forecast horizon approaches and conclude that errors to GDP and inflation forecasts are normally distributed. In a similar vein, Mitchell (2005) shows that the Institute's point forecast of inflation is reliable whilst its measure of uncertainty has been exaggerated.

scholarly journals How Accurate Are Professional Economic Forecasts?

2011 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Hamid Baghestani ◽  
David Nelson
Keyword(s):  
Forecast Error ◽  
Forecast Accuracy ◽  
Annual Data ◽  
Forecast Horizon ◽  
Macroeconomic Indicators ◽  
Economic Forecasts

<span>This paper examines the forecast accuracy of the ASA-NBER survey annual data on seven major macroeconomic indicators for 1983-1991. Although no single forecaster was consistently accurate, it is generally shown that (i) the average forecast error is reasonably low, (ii) the forecast accuracy and the forecasters agreement on their man forecast improve as the forecast horizon becomes shorter, and (iii) the consensus of professional forecasts is superior to the nae forecast.</span>

G-7 INFLATION FORECASTS: RANDOM WALK, PHILLIPS CURVE OR WHAT ELSE?

2007 ◽  
Vol 11 (1) ◽  
pp. 1-30 ◽  
Author(s):  
FABIO CANOVA
Keyword(s):  
Economic Theory ◽  
Random Walk ◽  
Turning Point ◽  
Phillips Curve ◽  
Time Varying ◽  
Varying Coefficients ◽  
Inflation Forecasts ◽  
Forecasting Performance ◽  
Time Varying Coefficients ◽  
Better Than

This paper compares the forecasting performance of some leading models of inflation for G-7 countries. We show that bivariate and trivariate models suggested by economic theory or statistical analysis are not much better than univariate ones. Phillips curve specifications fit well into this class. Improvements in both the MSE of the forecasts and turning point prediction are obtained with time-varying coefficients models, which exploit international interdependencies. The performance of the latter class of models is stable throughout the 1990s.

scholarly journals Assessing point forecast accuracy by stochastic error distance

2017 ◽  
Vol 36 (6-9) ◽  
pp. 588-598 ◽  
Author(s):  
Francis X. Diebold ◽  
Minchul Shin
Keyword(s):  
Forecast Accuracy ◽  
Stochastic Error ◽  
Point Forecast ◽  
Error Distance

Does Forecast Accuracy Matter to Security Analysts?

1999 ◽  
Vol 74 (2) ◽  
pp. 185-200 ◽  
Author(s):  
Michael B. Mikhail ◽  
Beverly R. Walther ◽  
Richard H. Willis
Keyword(s):  
Forecast Accuracy ◽  
Earnings Forecast ◽  
Statistical Relation ◽  
Security Analysts ◽  
Forecast Horizon ◽  
Stock Recommendations ◽  
Period Effects ◽  
Time Period ◽  
Turn Over ◽  
Relative Profitability

We investigate if earnings forecast accuracy matters to security analysts by examining its association with analyst turnover. Controlling for firm- and time-period effects, forecast horizon and industry forecasting experience, we find that an analyst is more likely to turn over if his forecast accuracy is lower than his peers. We find no association between an analyst's probability of turnover and his absolute forecast accuracy. We also investigate another observable measure of the analyst's performance, the profitability of his stock recommendations. There is no statistical relation between the absolute or relative profitability of an analyst's stock recommendations and his probability of turnover. We interpret our findings as indicating that forecast accuracy is important to analysts.

scholarly journals Post-Processing and Evaluation of Precipitation Ensemble Forecast under Multiple Schemes in Beijiang River Basin

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2631
Author(s):  
Xinchi Chen ◽  
Xiaohong Chen ◽  
Dong Huang ◽  
Huamei Liu
Keyword(s):  
Time Scales ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Ensemble Forecast ◽  
Efficiency Coefficient ◽  
Forecast System ◽  
Factors Affecting ◽  
Hydrological Forecasting ◽  
Better Than

Precipitation is one of the most important factors affecting the accuracy and uncertainty of hydrological forecasting. Considerable progress has been made in numerical weather prediction after decades of development, but the forecast products still cannot be used directly for hydrological forecasting. This study used ensemble pro-processor (EPP) to post-process the Global Ensemble Forecast System (GEFS) and Climate Forecast System version 2 (CFSv2) with four designed schemes, and then integrated them to investigate the forecast accuracy in longer time scales based on the best scheme. Many indices such as correlation coefficient, Nash efficiency coefficient, rank histogram, and continuous ranked probability skill score were used to evaluate the results in different aspects. The results show that EPP can improve the accuracy of raw forecast significantly, and the scheme considering cumulative forecast precipitation is better than that only considers single-day forecast. Moreover, the scheme that considers some observed precipitation would help to improve the accuracy and reduce the uncertainty. In terms of medium- and long-term forecasts, the integrated forecast based on GEFS and CFSv2 after post-processed would be better than CFSv2 significantly. The results of this study would be a very important demonstration to remove the deviation of ensemble forecast and improve the accuracy of hydrological forecasting in different time scales.

Forecasting of an extreme flood in mountainous area of North China based on WRF-Hydro with distributing parameters

2020 ◽  
Author(s):  
Wei Wang ◽  
JIa Liu ◽  
Chuanzhe Li ◽  
Qingtai Qiu ◽  
Yuchen Liu
Keyword(s):  
Extreme Event ◽  
Forecast Accuracy ◽  
Northern China ◽  
Calibration Method ◽  
Mountainous Area ◽  
Coupling System ◽  
Extreme Flood ◽  
Spatio Temporal ◽  
Merging Method ◽  
Better Than

&lt;p&gt;The flood events in the mountainous area of northern China has the characteristics of high intensity and strong sudden occurrence, and atmospheric-hydrological coupling system can improve the forecast accuracy and prolong the lead time. This paper discusses the simulations of the enhanced WRF-Hydro model on a historical flood that occurrs in a mesoscale catchment of Taihang mountain on July 21, 2012. Firstly, the precipitation accuracy of WRF, WRF data assimilation, co-kriging merging method of radar QPE data are as three different input sources for WRF-Hydro. The results show that the rainfall of merging QPE can achieve better simulations in time and space. In addition, the rainfall of WRF assimilation data is obviously better than that of WRF, but still underestimates the rainfall values. The extreme event rainstorm mainly &lt;span&gt;proceeds&amp;#160;&lt;/span&gt;in 5 hours, and for the assimilation data, the spatio-temporal simulations of the rainfall data in the first 2 hours are slightly poor. Hence we compare the combination of the first few hours to use the merging QPE and following by assimilation precipitation as the model input. In addition, according to the parameters of the WRF-Hydro model, a gridding parameter calibration method based on topographic index is constructed.&lt;/p&gt;

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 Improving Inflation Forecasts in the Medium to Long Term

2013 ◽  
pp. 1-6 ◽  
Author(s):  
Saeed Zaman
Keyword(s):  
Time Series ◽  
Forecast Accuracy ◽  
Time Series Models ◽  
Powerful Technique ◽  
Inflation Forecasts ◽  
Statistical Time

A simple but powerful technique for incorporating a changing underlying inflation trend into standard statistical time series models can improve forecast accuracy significantly—about 20 percent to 30 percent, two to three years out.

scholarly journals Verification as a service to bring more transparency on forecast accuracy to weather services and users

2021 ◽  
Author(s):  
Evelyn Müller ◽  
Jan Hoffmann ◽  
Dennis Schulze
Keyword(s):  
Forecast Accuracy ◽  
Cost Benefit ◽  
Forecast Horizon ◽  
Weather Forecasts ◽  
Industry Applications ◽  
Cost Benefit Ratio ◽  
The Cost ◽  
Available Information ◽  
Forecast Quality

&lt;p&gt;Actual, continuously available information on the accuracy of forecasts can support both weather services and users of forecasts in quality assurance during operations and identify systematic weaknesses. Comparing the forecast success of different forecasting methods allows decision makers in the weather service and on the user side to evaluate the cost-benefit ratio of available forecasting approaches, be it different models, DMO and post-processing, or different providers. Finally, in addition to on-off experiments for version comparison, the success of developments to the forecast system can be seen in the comparison of time series of verification results against those of other forecasts.&amp;#160;&lt;/p&gt;&lt;p&gt;From the development of the forecasting process to daily operations to the use of forecasts in subsequent industry applications, stakeholders have very different questions about the quality of weather forecasts. From the weather room, there is a particular need for up-to-date information on the previous day's forecast success and rapid access to case verification analyses following unusual events. Especially in B2B, case-specific comparison with the success of other forecasts is also in demand. For management, on the other hand, longer-term trends in forecast quality are the focus of interest. Finally, users often base their choice of a forecasting provider not only on procurement costs and convenience of access, but also take into account the current forecast accuracy of their relevant parameters, in their region, in the forecast horizon relevant to them. Especially weather-sensitive industries such as road weather services, energy production and transmission, but also media often agree with forecast suppliers on continuous monitoring of forecast quality.&amp;#160;&lt;/p&gt;&lt;p&gt;We present different perspectives and questions and show possible answers as use cases in a verification portal.&lt;/p&gt;

scholarly journals Is It Possible to Beat the Random Walk Model in Exchange Rate Forecasting? More Evidence for Brazilian Case.

2016 ◽  
Vol 14 (1) ◽  
pp. 65
Author(s):  
Emerson Fernandes Marçal ◽  
Eli Hadad Junior
Keyword(s):  
Exchange Rate ◽  
Random Walk ◽  
Exchange Rate Regime ◽  
Forecast Accuracy ◽  
Structural Models ◽  
Extensive Study ◽  
Forecast Combination ◽  
Correction Technique ◽  
Random Walk With Drift ◽  
Floating Exchange Rate

Abstract The seminal study of Meese et al. (1983) on exchange rate forecastability had a great impact on the international finance literature. The authors showed that exchange rate forecasts based on structural models are worse than a naive random walk. This result is known as the Meese--Rogoff (MR) puzzle. Although the validity of this result has been checked for many currencies, studies for the Brazilian currency are not common. In 1999, Brazil adopted the dirty floating exchange rate regime. Rossi (2013) ran an extensive study on the MR puzzle but did not analyse Brazilian data. Our goal is to run a “pseudo real-time experiment” to investigate whether forecasts based on econometric models that use the fundamentals suggested by the exchange rate monetary theory of the 80s can beat the random model for the case of the Brazilian currency. Our work has three main differences with respect to Rossi (2013). We use a bias correction technique and forecast combination in an attempt to improve the forecast accuracy of our projections. We also combine the random walk projections with the projections of the structural models to investigate if it is possible to further improve the accuracy of the random walk forecasts. However, our results are quite in line with Rossi (2013). We show that it is not difficult to beat the forecasts generated by the random walk with drift using Brazilian data, but that it is quite difficult to beat the random walk without drift. Our results suggest that it is advisable to use the random walk without drift, not only the random walk with drift, as a benchmark in exercises that claim the MR result is not valid.

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