Real-time GDP Research Articles

Understanding uncertainty shocks and the role of black swans

Uncertainty shocks can trigger business cycles, bank runs and asset price fluctuations. But how do those shocks translate into changes in beliefs and actions? What makes people suddenly become uncertain? This paper provides an explanation for subjective uncertainty shocks, estimated on real-time GDP data, that fits puzzling features of professional forecasts. The key ingredients are that agents do not know the true model governing the distribution of macroeconomic outcomes, but they believe that distribution to be potentially skewed. When agents estimate higher probabilities of extreme tail events, their uncertainty rises. Our estimates reveal that learning about probabilities of tail events – black swans – explains much of the fluctuations in subjective uncertainty.

Understanding Uncertainty Shocks and the Role of Black Swans

Economic uncertainty is a powerful force in the modern economy. Research shows that surges in uncertainty can trigger business cycles, bank runs and asset price fluctuations. But where do sudden surges in uncertainty come from? This paper provides a data-disciplined theory of belief formation that explains large fluctuations in uncertainty. It argues that people do not know the true distribution of macroeconomic outcomes. Like Bayesian econometricians, they estimate a distribution. Our main contribution is to explain why real-time estimation of distributions with non-normal tails results in large uncertainty fluctuations. We use theory and data to show how small changes in estimated skewness whip around probabilities of unobserved tail events (black swans). Our estimates, based on real-time GDP data, reveal that revisions in the estimates of black swan risk explain most of the fluctuations in uncertainty.

Exploring what stock markets tell us about GDP in theory and practice

This paper explores the stock market-GDP relationship from basic theory to simple empirics to better understand what stock market movements tell us about underlying GDP in real time. We present a simple theoretical model to make key relationships clear, then explore US GDP and US stock market (S&P 500) performance through a range of analytical tools from visual inspection to correlations, regressions, counting and extreme value calculations to a few illustrative narrative investigations. We find that the S&P 500 is weakly correlated with real GDP as well as with vintage GDP releases contemporaneous, but more strongly and statistically significantly with one lag as theory predicts. We also find that the S&P 500 is more closely related both contemporaneously and with a lag to final, revised GDP numbers - only known months later - than to vintage GDP estimates, suggesting that stock market trends are informative about true GDP.

Tracking U.S. GDP in Real Time

Tracking U.S. GDP in Real Time

Tracking U.S. GDP in Real Time

Measuring the current state of the U.S. economy in real time is an important but challenging task for monetary policymakers. The most comprehensive measure of the state of the economy?real gross domestic product?is available at a relatively low frequency (quarterly) and with a significant delay (one month). To obtain more timely assessments of the state of the economy, the Federal Reserve Bank of Kansas City has developed a GDP tracking model that combines new econometric methods with two conventional approaches to estimating GDP. {{p}} Taeyoung Doh and Jaeheung Bae review the Kansas City Fed model?s underlying details and illustrate its performance by comparing the model?s tracking estimates to those from other real-time tracking models. Their results suggest the Kansas City Fed model provides a useful tool for policymakers by combining estimates and forecasts from factor and accounting-based models.

Euro Area Growth Signals from Industrial Production: Warnings from a Comparison of Gross Value Added and Production

AbstractThis study compares industrial production and gross value added in volume terms in the euro area and euro area countries, because real GDP growth signals from industrial production growth might be misleading and earlier released industrial production growth is not one-to-one translated into industrial value added growth. This is an important issue for analysts and policy makers, because industrial production is a standard element in tools for nowcasting real GDP in real time. It also raises the question about the factors explaining these differences. Differences in terms of (changes in) quarterly growth between production and gross value added include sign reversals and can last for consecutive quarters. Persistent level differences might also exist. The explanatory factors for these differences are the treatment of prices, seasonality and coverage. Data limitations prevent a detailed analysis of the price factor, but the other two factors are more closely evaluated. It turns out that the relative importance of these factors varies over time and thus is difficult to assess ex ante for a specific quarter. A remedy is that statisticians further harmonize national accounts and short-term statistics as well as national practices for seasonal adjustment.

Does a Survey Based Capacity Utilization Measure Help Predicting Brazilian Output Gap in Real-Time?

The aim of the paper is to assess the informational content of a survey-based measure of the Brazilian manufacturing capacity utilization rate produced by the Brazilian Institute of Economics from Getulio Vargas Foundation. From a theoretical point of view, the survey-based capacity measure should provide meaningful information with respect to the Brazilian business cycle. In this work, we choose to use the output gap as the most comprehensive and convincing proxy of the cyclical situation (Graff and Sturm in CESifo Econ Stud 58(1):220–251, 2012). More specifically, we will refer to a real-time version of the output gap, using an existing real-time GDP data set produced by the Central Bank of Brazil (Cusinato et al. in Empir Econ, 2012). The information efficiency of the real-time output gap estimates is tested checking whether survey data can help producing real-time estimates that are significantly closer to the latest releases. In this sense, the paper provides a significant contribution to the existing literature, extending analysis already available for OECD countries to the Brazilian economy. Our main result is that survey data can indeed help to produce more efficient estimates of the output gap in real time.

Nowcasting Czech GDP in real time

In this paper, we employ a Dynamic Factor Model (DFM) to nowcast Czech GDP. Using multiple vintages of historical data and taking into account the publication lags of various monthly indicators, we evaluate the real-time performance of the DFM over the 2005–2012 period. The main result of this paper is that the accuracy of model-based nowcasts is comparable to that of the nowcasts of the Czech National Bank (CNB). Moreover, combining the DFM and the CNB nowcasts results in more accurate performance than in the case of the individual nowcasts alone. Our results also suggest that foreign variables are crucial for the accuracy of the model, while omitting financial and confidence indicators does not worsen the nowcasting performance.

Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?

This paper empirically investigates two alternative combination strategies, namely forecast combination and information pooling, in the context of nowcasting French GDP in real time with monthly survey opinions. According to the encompassing paradigm, we claim that the outperformance of the forecast combination strategy reported by recent works may be related to the issues of model selection and misspecification. To address these issues, we promote the blocking modeling approach to allow us to handle mixed frequencies in a linear framework that is compatible with an automatic model selection algorithm. Selected restricted- and pooled-information models are specified and tested for forecast encompassing in order to determine the best combination strategy. The results suggest that the forecast combination strategy dominates as long as no individual (restricted) model encompasses the rivals. However, when a predictive encompassing model is obtained by pooling the information sets, this model outperforms the most accurate forecast combination scheme.

The use of payment systems data as early indicators of economic activity

Payment system data are available with virtually no time lag and could therefore help in tracking the evolution of GDP in real time. Using data from the Spanish national system for retail payments, we compare the performance of GDP nowcasts against that of alternative indicators. The main result is that payment system data can make a significant contribution to nowcasting GDP and they can also improve the performance of simple models that are currently based only on purchasing managers indices (PMIs).

Understanding Uncertainty Shocks and the Role of Black Swans

Economic uncertainty is a powerful force in the modern economy. Recent work shows that surges in uncertainty can trigger business cycles, bank runs and asset price fluctuations. But where do sudden surges in uncertainty come from? This paper provides a data-disciplined theory of belief formation that explains large fluctuations in uncertainty. It argues that people do not know the true distribution of macroeconomic outcomes. Like Bayesian econometricians, they estimate a distribution. Our main contribution is to explain why real-time estimation of distributions with non-normal tails are prone to large uncertainty fluctuations. We use theory and data to show how small changes in estimated skewness whip around probabilities of unobserved tail events (black swans). Our estimates, based on real-time GDP data, reveal that revisions in the estimates of black swan risk explain most of the fluctuations in uncertainty.

Tracking world trade and GDP in real time

This paper proposes a simple procedure for obtaining monthly assessments of short-run perspectives for quarterly world GDP and trade. It combines high-frequency information from emerging and advanced countries so as to explain quarterly national accounts variables through bridge models. The union of all bridge equations leads to our world bridge model (WBM). The WBM approach of this paper is new for two reasons: its equations combine traditional short-run bridging with theoretical level-relationships, and it is the first time that forecasts of world GDP and trade have been computed for both advanced and emerging countries on the basis of a real-time database of approximately 7000 time series. Although the performances of the equations that are searched automatically should be taken as a lower bound, our results show that the forecasting ability of the WBM is superior to the benchmark. Finally, our results confirm that the use of revised data leads to models’ forecasting performances being overstated significantly.

Real-time GDP forecasting for Japan: A dynamic factor model approach

Accurate and timely information on GDP is important to gauge the overall state of the economy and is thus essential for economic policymaking. A single-index dynamic factor model is estimated using mixed-frequency data on GDP, industrial production, employment, private consumption and exports to obtain early estimates of Japan’s quarterly GDP growth in real time. The results of a real-time forecasting exercise suggest the model performs well in comparison to the consensus forecasts, in terms both of its accuracy, measured by the size of forecast errors, to predict actual GDP estimates, and of its ability to signal turning points in GDP, showing the advantage of using the model for early assessment of ongoing economic activities in Japan. An equally important goal of this study is to share with other forecasters the results of ongoing real-time GDP forecasts for Japan, aiming at increasing knowledge regarding Japan’s GDP forecasting.

On stock market illiquidity and real-time GDP growth

This study examines whether stock market illiquidity forecasts real UK GDP growth using data over the period 1989q1-2012q2. Apart from standard linear model specifications, we also utilize non-linear models, which allow for regime switching behavior in terms of a liquid versus an illiquid market regime and over the phases of the business cycle. Our findings support a statistically significant negative relationship between stock market illiquidity and future UK GDP growth over and above the usual control variables. This relationship is found to be stronger during periods of highly illiquid market conditions and weak economic growth. Our out-of-sample forecasting analysis indicates that a regime-switching model of illiquid versus liquid market conditions predicts UK growth better than any other model. Actually, this model is the only one to significantly outperform the GDP growth forecasts published in the Bank of England's Inflation Report.

Tracking World Trade and GDP in Real Time

This paper proposes a simple procedure to obtain monthly assessments of short-run perspectives for quarterly world GDP and trade. It combines emerging and advanced countries’ high frequency information to explain quarterly national accounts variables through bridge models. The union of all bridge equations leads to our world bridge model (WBM). The WBM econometric approach is new for two reasons: its equations combine traditional short-run bridging with theoretical level-relationships; it is the first time that forecasts of world GDP and trade are computed for advanced and emerging countries on the basis of a real-time database of 7,000 time series. Although the performance of the equations that are automatically searched for should be taken as a lower bound, results show a better WBM forecasting ability than the benchmark case and confirm the usefulness of combining WBM real-time forecasts with preliminary releases to improve the prediction of world trade. Finally, we show that the (unrealistic) use of revised data leads to a systematic overstatement of model forecasting performance.

Real-Time Forecasting Revisited: Letting the Data Decide

Real-time GDP forecasting, also often known as “nowcasting,” produces estimates for current-quarter real GDP growth, typically based on a centered value from a set of estimates from incoming indicators. These real-time measures are usually intended to be data-based and to not be based on forecaster judgment or add factors. Even so, estimation methodologies in this research area—and prior versions of the system we use—typically have been constrained by using various “fixed” relationships, such as a fixed historical sample horizon and fixed empirical specifications for the indicator variables. This paper describes the methodology, estimation, and software code for a more flexible real-time GDP system that allows the data to decide the best real-time GDP forecast for varying sample horizons and varying specifications for each indicator variable through time. Our system uses data on key indicators as they become available (accounting for the “jagged-edge” nature of the data in the current quarter) to generate an estimate of current-quarter real GDP growth, with weights for combining the indicator-specific estimates as determined by the strength of the indicators’ historical relationships to GDP growth. The improved system searches across a variety of specifications and across sample horizons to choose the best specification as determined by a minimum Schwarz criterion test while also searching for the best sample horizon for minimizing the mean absolute error for a recent prediction period. We illustrate the operation of the system for real-time estimates of real GDP growth over a specific quarter, and examine the properties of the estimates and the implications for predictions. We also discuss potential additional applications and demonstrate a specific application for real-time predictions of the monthly change in payroll jobs.

Fluorescence detection of GDP in real time with the reagentless biosensor rhodamine–ParM

The development of novel fluorescence methods for the detection of key biomolecules is of great interest, both in basic research and in drug discovery. Particularly relevant and widespread molecules in cells are ADP and GDP, which are the products of a large number of cellular reactions, including reactions catalysed by nucleoside triphosphatases and kinases. Previously, biosensors for ADP were developed in this laboratory, based on fluorophore adducts with the bacterial actin homologue ParM. It is shown in the present study that one of these biosensors, tetramethylrhodamine–ParM, can also monitor GDP. The biosensor can be used to measure micromolar concentrations of GDP on the background of millimolar concentrations of GTP. The fluorescence response of the biosensor is fast, the response time being <0.2 s. Thus the biosensor allows real-time measurements of GTPase and GTP-dependent kinase reactions. Applications of the GDP biosensor are exemplified with two different GTPases, measuring the rates of GTP hydrolysis and nucleotide exchange.

Simulating Inflation Forecasting in Real-Time: How Useful is a Simple Phillips Curve in Germany, the U.K.L4434, and the U.S.?

This paper simulates out-of-sample inflation forecasting for Germany, the UK, and the US. In contrast to other studies, we use output gaps estimated with unrevised real-time GDP data. This exercise assumes an information set similar to that available to a policymaker at a given point in time since GDP data is subject to sometimes substantial revisions. In addition to using real-time datasets for the UK and the US, we employ a dataset for real-time German GDP data not used before. We find that Phillips curves based on ex post output gaps generally improve the accuracy of inflation forecasts compared to an AR(1) forecast but that real-time output gaps often do not help forecasting inflation. This raises the question how operationally useful certain output gap estimates are for forecasting inflation.

Simulating Inflation Forecasting in Real-Time: How Useful is a Simple Phillips Curve in Germany, the UK, and the US?

This paper simulates out-of-sample inflation forecasting for Germany, the UK, and the US. In contrast to other studies, we use output gaps estimated with unrevised real-time GDP data. This exercise assumes an information set similar to that available to a policymaker at a given point in time since GDP data is subject to sometimes substantial revisions. In addition to using real-time datasets for the UK and the US, we employ a dataset for real-time German GDP data not used before. We find that Phillips curves based on ex post output gaps generally improve the accuracy of inflation forecasts compared to an AR(1) forecast but that real-time output gaps often do not help forecasting inflation. This raises the question how operationally useful certain output gap estimates are for forecasting inflation.

Real-time squared: A real-time data set for real-time GDP forecasting

This paper uses real-time data to mimic real-time GDP forecasting activity. Through automatic searches for the best indicators for predicting GDP one and four steps ahead, we compare the out-of-sample forecasting performance of adaptive models using different data vintages, and produce three main findings. First, despite data revisions, the forecasting performance of models with indicators is better, but this advantage tends to vanish over longer forecasting horizons. Second, the practice of using fully updated datasets at the time the forecast is made (i.e., taking the best available measures of today's economic situation) does not appear to bring any effective improvement in forecasting ability: the first GDP release is predicted equally well by models using real-time data as by models using the latest available data. Third, although the first release is a rational forecast of GDP data after all statistical revisions have taken place, the forecast based on the latest available GDP data (i.e. the “temporarily best” measures) may be improved by combining preliminary official releases with one-step-ahead forecasts.