The Role of Survey Data in Nowcasting Euro Area GDP Growth

This paper evaluates the impact of new releases of financial, real activity and survey data on nowcasting euro area gross domestic product (GDP). We show that all three data categories positively impact on the accuracy of GDP nowcasts, whereby the effect is largest in the case of real activity data. When treating variables as if they were all published at the same time and without any time lag, financial series lose all their significance, while survey data remain an important ingredient for the nowcasting exercise. The subsequent analysis shows that the sectoral coverage of survey data, which is broader than that of timely available real activity data, as well as their information content stemming from questions focusing on agents' expectations, are the main sources of the ‘genuine’ predictive power of survey data. When the forecast period is restricted to the 2008–09 financial crisis, the main change is an enhanced forecasting role for financial data. Copyright © 2015 John Wiley & Sons, Ltd.     

Google Trends and the forecasting performance of exchange rate models

In this paper, we use Google Trends data for exchange rate forecasting in the context of a broad literature review that ties the exchange rate movements with macroeconomic fundamentals. The sample covers 11 OECD countries’ exchange rates for the period from January 2004 to June 2014. In out‐of‐sample forecasting of monthly returns on exchange rates, our findings indicate that the Google Trends search query data do a better job than the structural models in predicting the true direction of changes in nominal exchange rates. We also observed that Google Trends‐based forecasts are better at picking up the direction of the changes in the monthly nominal exchange rates after the Great Recession era (2008–2009). Based on the Clark and West inference procedure of equal predictive accuracy testing, we found that the relative performance of Google Trends‐based exchange rate predictions against the null of a random walk model is no worse than the purchasing power parity model. On the other hand, although the monetary model fundamentals could beat the random walk null only in one out of 11 currency pairs, with Google Trends predictors we found evidence of better performance for five currency pairs. We believe that these findings necessitate further research in this area to investigate the extravalue one can get from Google search query data.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

秦微1,2,张其林1,2,赖悦1,2,姜苏1,2,陈媛1,2,苏建峰1,2

为了更好的利用闪电资料和雷达回波资料来对雷电进行临近预警,本文以深圳地区2012年4月一次强飑线过程为例,分析该次过程中的闪电活动与雷达回波特征,发现地闪分布与雷达回波强度之间表现出很好的对应关系。因此,本文采用TITAN(Thunderstorm Identification, Tracking, Analysis, and Nowcasting)算法对这次强风暴过程中40dBZ以上的强回波区进行识别、跟踪和外推,并与闪电高密度区域的识别、跟踪和外推结果进行对比分析。结果表明：TITAN算法识别效果理想,且识别结果与单体的发展旺盛程度有关;另外,雷达强回波区和闪电高密度区的外推精度与外推开始时刻、外推时间的长短有关;利用闪电高密度区进行外推时,外推的质心误差小于雷达强回波区的外推质心误差。     

改进的光流法在回波外推预报中的应用

为了充分利用雷达资料进行强对流天气的临近预报,通过改进的光流法反演风场并对雷达组合反射率因子进行0~1 h的外推预报。为了提高传统光流法外推预报的准确性,采用金字塔分层技术,减小由于回波移速较快造成的反演误差,提高反演风场的计算精度和运算效率;并且采用半拉格朗日外推方案改进外推,保持回波的旋转性,提高回波预报效果。试验及评分结果表明,改进后的光流法改善了强对流天气的预报效果。     

Nowcasting and predicting data revisions using panel survey data

The qualitative responses that firms give to business survey questions regarding changes in their own output provide a real‐time signal of official output changes. The most commonly used method to produce an aggregate quantitative indicator from business survey responses—the net balance or diffusion index—has changed little in 40 years. This paper investigates whether an improved real‐time signal of official output data changes can be derived from a recently advanced method on the aggregation of survey data from panel responses. We find, in a New Zealand application, that exploiting the panel dimension to qualitative survey data gives a better in‐sample signal about official data than traditional methods. Out‐of‐sample, it is less clear that it matters how survey data are quantified, with simpler and more parsimonious methods hard to improve. It is clear, nevertheless, that survey data, exploited in some form, help to explain revisions to official data. Copyright © 2009 John Wiley & Sons, Ltd.     

Nowcasting with Google Trends in an Emerging Market

Most economic variables are released with a lag, making it difficult for policy‐makers to make an accurate assessment of current conditions. This paper explores whether observing Internet browsing habits can inform practitioners about aggregate consumer behavior in an emerging market. Using data on Google search queries, we introduce an index of online interest in automobile purchases in Chile and test whether it improves the fit and efficiency of nowcasting models for automobile sales. Despite relatively low rates of Internet usage among the population, we find that models incorporating our Google Trends Automotive Index outperform benchmark specifications in both in‐sample and out‐of‐sample nowcasts, provide substantial gains in information delivery times, and are better at identifying turning points in the sales data. Copyright © 2011 John Wiley & Sons, Ltd.     

光流法在强对流天气临近预报中的应用

针对强对流降水的情况,提出了使用光流法计算得到的光流场来代替交叉相关法得到的运动矢量场。与简单的交叉相关法相比,光流法从偏微分方程的角度来求解光流场,在计算过程中使用了严格的约束条件,运用递归法进行求解。试验及评价的结果表明,对变化较快的强对流降水天气过程,光流法明显优于交叉相关法。     

Forecasting Private Consumption by Consumer Surveys

Survey‐based indicators are widely seen as leading indicators for economic activity. As such, consumer confidence might be informative for the future path of private consumption. Although the indicators receive high attention in the media, their forecasting power often appears to be very limited. This paper takes a fresh look at the data that serve as a basis for the consumer confidence indicator (CCI) reported by the EU Commission for the euro area. Different pooling methods are applied to exploit the survey information. Forecasts are based on mixed data sampling (MIDAS) and bridge equations. While the CCI does not outperform the autoregressive benchmark, the new indicators are able to raise forecasting performance. The best performing indicator should be built upon pre‐selection methods. Data‐driven aggregation methods should be preferred to determine the weights of the individual ingredients. Copyright © 2011 John Wiley & Sons, Ltd.     

A note on the predictive power of survey data in nowcasting euro area GDP

This paper investigates the trade‐off between timeliness and quality in nowcasting practices. This trade‐off arises when the frequency of the variable to be nowcast, such as gross domestic product (GDP), is quarterly, while that of the underlying panel data is monthly; and the latter contains both survey and macroeconomic data. These two categories of data have different properties regarding timeliness and quality: the survey data are timely available (but might possess less predictive power), while the macroeconomic data possess more predictive power (but are not timely available because of their publication lags). In our empirical analysis, we use a modified dynamic factor model which takes three refinements for the standard dynamic factor model of Stock and Watson (Journal of Business and Economic Statistics, 2002, 20, 147–162) into account, namely mixed frequency, preselections and cointegration among the economic variables. Our main finding from a historical nowcasting simulation based on euro area GDP is that the predictive power of the survey data depends on the economic circumstances; namely, that survey data are more useful in tranquil times, and less so in times of turmoil.