Financial nowcasts and their usefulness in macroeconomic forecasting

Financial data often contain information that is helpful for macroeconomic forecasting, while multi-step forecast accuracy benefits from incorporating good nowcasts of macroeconomic variables. This paper considers the usefulness of financial nowcasts for making conditional forecasts of macroeconomic variables with quarterly Bayesian vector autoregressions (BVARs). When nowcasting quarterly financial variables’ values, we find that taking the average of the available daily data and a daily random walk forecast to complete the quarter typically outperforms other nowcasting approaches. Using real-time data, we find gains in out-of-sample forecast accuracy from the inclusion of financial nowcasts relative to unconditional forecasts, with further gains from the incorporation of nowcasts of macroeconomic variables. Conditional forecasts from quarterly BVARs augmented with financial nowcasts rival the forecast accuracy of mixed-frequency dynamic factor models and mixed-data sampling (MIDAS) models.     

A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates

In this paper, we focus on the different methods which have been proposed in the literature to date for dealing with mixed-frequency and ragged-edge datasets: bridge equations, mixed-data sampling (MIDAS), and mixed-frequency VAR (MF-VAR) models. We discuss their performances for nowcasting the quarterly growth rate of the Euro area GDP and its components, using a very large set of monthly indicators. We investigate the behaviors of single indicator models, forecast combinations and factor models, in a pseudo real-time framework. MIDAS with an AR component performs quite well, and outperforms MF-VAR at most horizons. Bridge equations perform well overall. Forecast pooling is superior to most of the single indicator models overall. Pooling information using factor models gives even better results. The best results are obtained for the components for which more economically related monthly indicators are available. Nowcasts of GDP components can then be combined to obtain nowcasts for the total GDP growth.     

A comparison of monthly global indicators for forecasting growth

This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world real GDP growth using mixed-frequency models. It shows that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecasting accuracy, while other monthly measures have more mixed success. Specifically, the best-performing model yields impressive gains with MSPE reductions of up to 34% at short horizons and up to 13% at long horizons relative to an autoregressive benchmark. The global economic conditions indicator also contains valuable information for assessing the current and future state of the economy for a set of individual countries and groups of countries. This indicator is used to track the evolution of the nowcasts for the U.S., the OECD area, and the world economy during the COVID-19 pandemic and the main factors that drive the nowcasts are quantified.     

Nowcasting food inflation with a massive amount of online prices

The consensus in the literature on providing accurate inflation forecasts underlines the importance of precise nowcasts. In this paper, we focus on this issue by employing a unique, extensive dataset of online food and non-alcoholic beverages prices gathered automatically from the webpages of major online retailers in Poland since 2009. We perform a real-time nowcasting experiment by using a highly disaggregated framework among popular, simple univariate approaches. We demonstrate that pure estimates of online price changes are already effective in nowcasting food inflation, but accounting for online food prices in a simple, recursively optimized model delivers further gains in the nowcast accuracy. Our framework outperforms various other approaches, including judgmental methods, traditional benchmarks, and model combinations. After the outbreak of the COVID-19 pandemic, its nowcasting quality has improved compared to other approaches and remained comparable with judgmental nowcasts. We also show that nowcast accuracy increases with the volume of online data, but their quality and relevance are essential for providing accurate in-sample fit and out-of-sample nowcasts. We conclude that online prices can markedly aid the decision-making process at central banks.     

Real‐Time Nowcasting of GDP: A Factor Model vs. Professional Forecasters

We perform a fully real‐time nowcasting (forecasting) exercise of US GDP growth using Giannone et al.'s (2008) factor model framework. To this end, we have constructed a real‐time database of vintages from 1997 to 2010 for a panel of variables, enabling us to reproduce, for any given day in that range, the exact information that was available to a real‐time forecaster. We track the daily evolution of the model performance along the real‐time data flow and find that the precision of the nowcasts increases with information releases and the model fares well relative to the Survey of Professional Forecasters (SPF).     

Discussion of “Nowcasting US GDP: The role of ISM Business Surveys”

This paper discusses the contribution of Lahiri and Monokroussos, published in the current issue of this journal, where they investigate the nowcasting power of ISM Business Surveys for real US GDP. The second part of this note includes some empirical considerations on nowcasting quarterly real GDP by using the monthly PMI index for Switzerland. The results indicate that the Swiss PMI is not leading GDP growth; rather, it is coincident, and its nowcasting power is quite good. The signs of the fitted values mostly correspond to the sign of the actual GDP growth, and the important turning points are identified accurately by the model. This also holds true during the recent crisis.     

Nowcasting Indian GDP

Nowcasting has become a useful tool for making timely predictions of gross domestic product (GDP) in a data‐rich environment. However, in developing economies this is more challenging due to substantial revisions in GDP data and the limited availability of predictor variables. Taking India as a leading case, we use a dynamic factor model nowcasting method to analyse these two issues. Firstly, we propose to compare nowcasts of the first release of GDP to those of the final release to assess differences in their predictability. Secondly, we expand a standard set of predictors typically used for nowcasting GDP with nominal and international series, in order to proxy the variation in missing employment and service sector variables in India. We find that the factor model improves over several benchmarks, including bridge equations, but only for the final GDP release and not for the first release. Also, the nominal and international series improve predictions over and above real series. This suggests that future studies of nowcasting in developing economies which have similar issues of data revisions and availability as India should be careful in analysing first‐ vs. final‐release GDP data, and may find that predictions are improved when additional variables from more timely international data sources are included.     

Forecasting with news sentiment: Evidence with UK newspapers

We investigate the performance of newspapers for forecasting inflation, output and unemployment in the United Kingdom. We concentrate on whether the economic policy content reported in popular printed media can improve on existing point forecasts. We find no evidence supporting improved nowcasts or short-term forecasts for inflation. The sentiment inferred from printed media, can however be useful for forecasting unemployment and output. Considerable improvements are also noted when using individual newspapers and keyword based indices.     

Nowcasting with payments system data

We consider the potential usefulness of a large set of electronic payments data, comprising the values and numbers of both debit card transactions and cheques that clear through the banking system, for the problem of reducing the current-period forecast (‘nowcast’) loss for (the growth rates of) GDP and retail sales. The payments system variables capture a broad range of spending activity and are available on a very timely basis, making them suitable current indicators. We generate nowcasts of GDP and retail sales growth for a given month on seven different dates, over a period of two and a half months preceding the first official releases, which is the period over which nowcasts would be of interest. We find statistically significant evidence that payments system data can reduce the nowcast error for both GDP and retail sales growth. Both debit transaction and cheque clearance data are of value in reducing nowcast losses for GDP growth, although the latter are of little or no value when debit data are also included. For retail sales, cheque data appear to produce no further nowcast loss reductions, regardless of whether or not debit transactions are included in the nowcasting model.     

Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model

This paper investigates the benefits of internet search data in the form of Google Trends for nowcasting real U.S. GDP growth in real time through the lens of mixed frequency Bayesian Structural Time Series (BSTS) models. We augment and enhance both model and methodology to make these better amenable to nowcasting with large number of potential covariates. Specifically, we allow shrinking state variances towards zero to avoid overfitting, extend the SSVS (spike and slab variable selection) prior to the more flexible normal-inverse-gamma prior which stays agnostic about the underlying model size, as well as adapt the horseshoe prior to the BSTS. The application to nowcasting GDP growth as well as a simulation study demonstrate that the horseshoe prior BSTS improves markedly upon the SSVS and the original BSTS model with the largest gains in dense data-generating-processes. Our application also shows that a large dimensional set of search terms is able to improve nowcasts early in a specific quarter before other macroeconomic data become available. Search terms with high inclusion probability have good economic interpretation, reflecting leading signals of economic anxiety and wealth effects.     

Nowcasting China’s GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated. Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises. In this study, we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data. We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors. We demonstrate the validity of our approach through simulation studies, and explore the applicability of our approach through an empirical study in nowcasting the China’s GDP using 117 monthly data series of several categories in the Chinese market. The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China’s GDP.     

Measuring output gap nowcast uncertainty

We propose a methodology for gauging the uncertainty in output gap nowcasts across a large number of commonly-deployed vector autoregressive (VAR) specifications for inflation and the output gap. Our approach utilises many output gap measures to construct ensemble nowcasts for inflation using a linear opinion pool. The predictive densities for the latent output gap utilise weights based on the ability of each specification to provide accurate probabilistic forecasts of inflation. In an application based on US real-time data, nowcasting over the out-of-sample evaluation period from 1991q2 to 2010q1, we demonstrate that a system of bivariate VARs produces well-calibrated ensemble densities for inflation, in contrast to univariate autoregressive benchmarks. The implied nowcast densities for the output gap are multimodal and indicate a considerable degree of uncertainty. For example, we assess the probability of a negative output gap at around 45% between 2004 and 2007. Despite the Greenspan policy regime, there still remained a substantial risk that the nowcast for output was below potential in real time. We extend our methodology to include distinct output gap measures, based on alternative filters, and show that, in our application, the nowcast density for the output gap is sensitive to the detrending method.     

The accuracy of IMF crises nowcasts

The International Monetary Fund (IMF) provides loans to countries in economic crises as a lender of last resort. IMF loan approvals are tied to policy reforms and quantitative targets that reflect the IMF’s crisis assessment. An extensive literature scrutinizes the efficacy of IMF loan programs, instead, we examine the accuracy of the IMF’s crisis assessments (nowcasts) that predicate program designs. Analyzing an unprecedented 602 IMF loan programs from 1992 to 2019, we contradict previous findings that IMF nowcasts are generally optimistic. Disentangling the structure of the IMF’s nowcast bias, we find the IMF systematically overestimates high-growth recoveries GDPs, while low-growth recoveries for low-income countries (LICs) are underestimated. In contrast, non-LICs’ nowcasts exhibit no statistically significant optimistic and pessimistic bias. Interestingly, shorter nowcast horizons do not improve accuracy, and GDP growth nowcasts improved substantially since 2013, while inflation nowcasts remain inefficient. We also isolate the sources of IMF nowcast inefficiencies according to ((i) program objectives, ((ii) program conditionality type, ((iii) geographic regions, ((iv) global crises, and ((v) geopolitics (elections, conflicts, and disasters).     

POOLING VERSUS MODEL SELECTION FOR NOWCASTING GDP WITH MANY PREDICTORS: EMPIRICAL EVIDENCE FOR SIX INDUSTRIALIZED COUNTRIES

This paper discusses pooling versus model selection for nowcasting with large datasets in the presence of model uncertainty. In practice, nowcasting a low‐frequency variable with a large number of high‐frequency indicators should account for at least two data irregularities: (i) unbalanced data with missing observations at the end of the sample due to publication delays; and (ii) different sampling frequencies of the data. Two model classes suited in this context are factor models based on large datasets and mixed‐data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst other things, the factor estimation method and the number of factors, lag length and indicator selection. Thus there are many sources of misspecification when selecting a particular model, and an alternative would be pooling over a large set of different model specifications. We evaluate the relative performance of pooling and model selection for nowcasting quarterly GDP for six large industrialized countries. We find that the nowcast performance of single models varies considerably over time, in line with the forecasting literature. Model selection based on sequential application of information criteria can outperform benchmarks. However, the results highly depend on the selection method chosen. In contrast, pooling of nowcast models provides an overall very stable nowcast performance over time. Copyright © 2012 John Wiley & Sons, Ltd.     

Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP*

In this article, we merge two strands from the recent econometric literature. First, factor models based on large sets of macroeconomic variables for forecasting, which have generally proven useful for forecasting. However, there is some disagreement in the literature as to the appropriate method. Second, forecast methods based on mixed‐frequency data sampling (MIDAS). This regression technique can take into account unbalanced datasets that emerge from publication lags of high‐ and low‐frequency indicators, a problem practitioner have to cope with in real time. In this article, we introduce Factor MIDAS, an approach for nowcasting and forecasting low‐frequency variables like gross domestic product (GDP) exploiting information in a large set of higher‐frequency indicators. We consider three alternative MIDAS approaches (basic, smoothed and unrestricted) that provide harmonized projection methods that allow for a comparison of the alternative factor estimation methods with respect to nowcasting and forecasting. Common to all the factor estimation methods employed here is that they can handle unbalanced datasets, as typically faced in real‐time forecast applications owing to publication lags. In particular, we focus on variants of static and dynamic principal components as well as Kalman filter estimates in state‐space factor models. As an empirical illustration of the technique, we use a large monthly dataset of the German economy to nowcast and forecast quarterly GDP growth. We find that the factor estimation methods do not differ substantially, whereas the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the Factor MIDAS models, which confirms the usefulness of the mixed‐frequency techniques that can exploit timely information from business cycle indicators.     

Modeling and forecasting of Brazilian reservoir inflows via dynamic linear models

This work focuses on developing a forecasting model for the water inflow at an hydroelectric plant’s reservoir for operations planning. The planning horizon is 5 years in monthly steps. Due to the complex behavior of the monthly inflow time series we use a Bayesian dynamic linear model that incorporates seasonal and autoregressive components. We also use climate variables like monthly precipitation, El Niño and other indices as predictor variables when relevant. The Brazilian power system has 140 hydroelectric plants. Based on geographical considerations, these plants are collated by basin and classified into 15 groups that correspond to the major river basins, in order to reduce the dimension of the problem. The model is then tested for these 15 groups. Each group will have a different forecasting model that can best describe its unique seasonality and characteristics. The results show that the forecasting approach taken in this paper produces substantially better predictions than the current model adopted in Brazil (see Maceira & Damazio, 2006), leading to superior operations planning.     

MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model specification in the presence of mixed-frequency data, e.g. monthly and quarterly series. MIDAS leads to parsimonious models which are based on exponential lag polynomials for the coefficients, whereas MF-VAR does not restrict the dynamics and can therefore suffer from the curse of dimensionality. However, if the restrictions imposed by MIDAS are too stringent, the MF-VAR can perform better. Hence, it is difficult to rank MIDAS and MF-VAR a priori, and their relative rankings are better evaluated empirically. In this paper, we compare their performances in a case which is relevant for policy making, namely nowcasting and forecasting quarterly GDP growth in the euro area on a monthly basis, using a set of about 20 monthly indicators. It turns out that the two approaches are more complements than substitutes, since MIDAS tends to perform better for horizons up to four to five months, whereas MF-VAR performs better for longer horizons, up to nine months.     

Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence

Policymakers, firms, and investors closely monitor traditional survey-based consumer confidence indicators and treat them as an important piece of economic information. To obtain a daily nowcast of monthly consumer confidence, we introduce a latent factor model for the vector of monthly survey-based consumer confidence and daily sentiment embedded in economic media news articles. The proposed mixed-frequency dynamic factor model uses a Toeplitz correlation matrix to account for the serial correlation in the high-frequency sentiment measurement errors. We find significant accuracy gains in nowcasting survey-based Belgian consumer confidence with economic media news sentiment.     

Spillover effects within supply chains: Evidence from Chinese-listed firms

There is increasing attention on information transfers along supply chain partners for firm (extreme) events. This growing literature finds spillover effects following certain types of firm events. Using data from credit rating actions of Chinese-listed firms over the period between March 2007 and May 2020, we examine the spillover effects of supply chains by focusing on the market reactions of event firms to the action announcements. We find strong evidence of spillover effects driven by the market reactions of event firms, which are enhanced through information diffusion channels as supply chain partners receive more investor attention. Moreover, the effects are stronger when event firms' market reactions are negative, event firms are non-stated-owned, the industry concentration of event firms is higher, or the supplier-customer business relationship is closer. Overall, these findings highlight the role of investor attention and network characteristics in supply chain spillovers.