GDP Estimates: Rationality Tests and Turning Point Performance

The reliability of BEA’s estimates, as measured by the magnitude and pattern of revisions, is highly important to economic policy-making and business decisions. We find evidence that the revisions are partially predicable using contemporaneously available information for the current quarterly estimates of GDP. Information about national income is found to significantly supplement the information found in the final current quarterly estimates of GDP in explaining the revisions to the latest-available estimates of GDP. However, there is little evidence of the predictability of revisions in GDI or national income. Finally, both the advance and final current quarterly estimates are found to do a reliable job of measuring GDP and GDI around cyclical peaks, but a less reliable job around cyclical troughs, the declines preceding the troughs are overstated and the upturns after the troughs are understated. An earlier and somewhat expanded version of this paper, “Revisions, Rationality, and Turning Points in GDP,” was presented at the session “Tracking the Turning Points in the Economy,” AEA meetings January 3–5 2003, Washington DC. It is available in the “working papers” section of BEA’s web site, www.bea.gov     

Information rigidity in growth forecasts: Some cross-country evidence

We document information rigidity in forecasts of real GDP growth in 46 countries over the past two decades. We also investigate: (i) whether rigidities differ across countries, particularly between advanced countries and emerging markets; (ii) whether rigidities are lower around turning points in the economy, such as in times of recessions and crises; and (iii) how quickly forecasters incorporate news about growth in other countries into their growth forecasts, with a focus on the way in which advanced countries’ growth forecasts incorporate news about emerging market growth, and vice versa.     

Calling recessions in real time

This paper surveys efforts to automate the dating of business cycle turning points. Doing this on a real time, out-of-sample basis is a bigger challenge than many academics might assume, due to factors such as data revisions and changes in economic relationships over time. The paper stresses the value of both simulated real-time analysis — looking at what the inference of a proposed model would have been using data as they were actually released at the time — and actual real-time analysis, in which a researcher stakes his or her reputation on publicly using the model to generate out-of-sample, real-time predictions. The immediate publication capabilities of the internet make the latter a realistic option for researchers today, and many are taking advantage of it. The paper reviews a number of approaches to dating business cycle turning points and emphasizes the fundamental trade-off between parsimony — trying to keep the model as simple and robust as possible — and making full use of the available information. Different approaches have different advantages, and the paper concludes that there may be gains from combining the best features of several different approaches.     

Can univariate models forecast turning points in seasonal economic time series?

Based on a particular class of recently developed unobserved component models with, time varying parameters, the objectives of this paper are two-fold. On the one hand, we propose an alternative measure of underlying growth based on our estimated trend derivative with no need for any further transformations. Additionally, using the information embedded on the trend derivative, we provide a simple method for improving quantitative point forecasts in the vicinity of turning points. Empirical applications are presented for a set of seasonal monthly economic indicators of the Spanish economy.     

A useful tool for forecasting the Euro-area business cycle phases

Based on a novel extension of existing multivariate Markov-switching models, we provide the reader with a useful tool for analyzing current business conditions and making predictions about the future state of the Euro-area economy in real time. Apart from the Industrial Production Index, we find that the European Commission Industrial Confidence Indicator, which is issued with no delay, is very useful for constructing the real-time predictions.     

Conformal prediction interval estimation and applications to day-ahead and intraday power markets

In this study, we investigated the application of the conformal prediction (CP) concept in the context of short-term electricity price forecasting. In particular, we determined the most important aspects related to the utility of CP, as well as explaining why this simple but highly effective idea has proved useful in other application areas and why its characteristics make it promising for short-term power applications. We compared the performance of CP with various state-of-the-art electricity price forecasting models, such as quantile regression averaging, in an empirical out-of-sample study of three short-term electricity time series. We combined CP with various underlying point forecast models to demonstrate its versatility and behavior under changing conditions. Our findings suggest that CP yields sharp and reliable prediction intervals in short-term power markets. We also inspected the effects of each of the model components to provide path-based guideline regarding how to find the best CP model for each market.     

分位数误差校正模型及应用

误差校正模型具有较好的预测能力,在时间序列分析中占据重要地位。将误差校正模型从均值框架推广到分位数框架,提出了分位数误差校正模型的概念,并给出一整套建模技术：模型表示、参数估计、模型定阶、诊断检验、密度预测等。通过数值模拟,将其与经典的均值误差校正模型、分位数自回归模型进行比较,发现分位数误差校正模型极大地提高了预测的准度与精度。此外,选取中国货币供应与物价水平之间关系作为研究对象,实证检验了分位数误差校正模型的条件密度预测能力。     

Bayesian VAR forecasts,survey information,and structural change in the euro area

We incorporate external information extracted from the European Central Bank’s Survey of Professional Forecasters into the predictions of a Bayesian VAR using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve the plain BVAR point and density forecasts. Importantly, we do not restrict the forecasts at a specific quarterly horizon but their possible paths over several horizons jointly since the survey information comes in the form of one- and two-year-ahead expectations. As well as improving the accuracy of the variable that we target, the spillover effects on “other-than-targeted” variables are relevant in size and are statistically significant. We document that the baseline BVAR exhibits an upward bias for GDP growth after the financial crisis, and our results provide evidence that survey forecasts can help mitigate the effects of structural breaks on the forecasting performance of a popular macroeconometric model.     

汛期地面测报工作浅析

在汛期,天气复杂多变,地面测报人员若不具备全面的实际工作经验,对突发性的、灾害性的天气应急能力不强,较易出现各种错误。本文针对本地区汛期常见的雷暴、强降水、大风、冰雹天气现象,浅要分析了汛期地面测报人员的注意事项。最后,概述了汛期其他天气状况下的工作要点,希望有助于提高地面测报工作的业务质量。     

Future changes in age and household patterns: Some implications for public finances

Using stochastic forecasting techniques, this paper assesses the consequences for public finances of changes in age and household structures in Denmark over the period 2008–2037. Focusing on components of welfare provisions and tax payments with noticeable differences across age and household status, we show that, based on a point forecast, the fiscal impact of changes in household structures amounts to an annual negative effect of 0.5% of GDP, and the effect of changes in age structures is forecast to worsen the public budget by 3.7% of GDP per year. While being subject to a considerable amount of uncertainty, the prospect of such a dramatic weakening of public finances is likely to trigger demands for welfare reforms characterized by a more individualized system of public transfer and tax payments, in addition to the measures that have already been taken to address the fiscal effects of population ageing.