GDP Growth Forecasts Research Articles

Forecasting GDP Growth Rates Using Google Trends in the United States and Brazil

In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed (U.S.) and emerging-market economy (Brazil). Our focus is on the marginal contribution of Big Data in the form of Google Trends data over and above that of traditional predictors, and we use a dynamic factor model to handle the large number of potential predictors and the ragged-edge problem. We find that factor models based on economic indicators and Google categories data provide significant gains compared to models that exclude this information. Using more disaggregated Google Trends data than its categories is not helpful. The benefits of using Google Trends data are greater for the U.S. than Brazil.

Nowcasting and forecasting GDP growth with machine-learning sentiment indicators

We apply the two-step machine-learning method proposed by Claveria et al. (2021) to generate country-specific sentiment indicators that provide estimates of year-on-year GDP growth rates. In the first step, by means of genetic programming, business and consumer expectations are evolved to derive sentiment indicators for 19 European economies. In the second step, the sentiment indicators are iteratively re-computed and combined each period to forecast yearly growth rates. To assess the performance of the proposed approach, we have designed two out-of-sample experiments: a nowcasting exercise in which we recursively generate estimates of GDP at the end of each quarter using the latest survey data available, and an iterative forecasting exercise for different forecast horizons We found that forecasts generated with the sentiment indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool.

All Forecasters Are Not the Same: Time-Varying Predictive Ability across Forecast Environments

This paper examines data from the European Central Bank’s Survey of Professional Forecasters to investigate whether participants display equal predictive performance. We use panel data models to evaluate point- and density-based forecasts of real GDP growth, inflation, and unemployment. The results document systematic differences in participants’ forecast accuracy that are not time invariant, but instead vary with the difficulty of the forecasting environment. Specifically, we find that some participants display higher relative accuracy in tranquil environments, while others display higher relative accuracy in volatile environments. We also find that predictive performance is positively correlated across target variables and horizons, with density forecasts generating stronger correlation patterns. Taken together, the results support the development of expectations models featuring persistent heterogeneity.

Forecasting US Output Growth with Large Information Sets

We forecast US output growth using an array of both Classical and Bayesian models including the recently developed Dynamic Variable Selection prior with Variational Bayes [DVSVB] of Koop and Korobilis (2020). We accommodate over 300 predictors that are incrementally captured from 5 factors, 60 factors to over 300 factors covering relevant economic agents. For robustness, we allow for both constant and time varying coefficients as well as alternative proxies for output growth. Using data covering 1960:Q1 to 2018:Q4, our results consistently support the use of high-dimensional models when forecasting US output growth regardless of the choice of forecast measure. For the density forecast of real GDP growth in particular, the results favour the DVSVB and time varying parameter assumption.

World economy

After a truly dreadful 2020, during which the world economy contracted by about 4%, the onset of Covid‐19 vaccination programmes around the world has provided some light at the end of the tunnel. Despite this, we have downgraded our 2021 global GDP growth forecast slightly to 5.0% from 5.2% three months ago, reflecting the probable weak start to this year caused by the high rate of Covid‐19 cases and the associated increase in activity restrictions.

Forecasting UK GDP growth with large survey panels

By employing large panels of survey data for the UK economy, we aim at reviewing linear approaches for regularisation and dimension reduction combined with techniques from the machine learning literature, like Random Forests, Support Vector Regressions and Neural Networks for forecasting GDP growth at monthly frequency for horizons from one month up to two years ahead. We compare the predictive content of surveys with text based indicators from newspaper articles and a standard macroeconomic data set and extend the empirical evidence on the contribution of survey data against text indicators and more traditional macroeconomic time series in predicting economic activity. Among the linear models, the Ridge and the Partial Least Squares models report the largest gains consistently for most of the forecasting horizons, and for the non‑linear machine learning models, the SVR performs better at shorter horizons compared to the Neural Networks and Random Forest that seem to be more appropriate for longer‑term forecasting. Text based indicators appear to favour more the use of non‑linear models and the expansion of the information set with macroeconomic time series does not appear to add much more predictive power. The largest forecasting gains are overwhelmingly concentrated at the shorter horizons for the majority of models and datasets which provides further empirical support that non‑linear machine learning models appear to be more useful during the Great Recession.

Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective

We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables---outnumbering the total time-series observations---and apply machine learning techniques to train, validate, and test the prediction models. For near-term (current and next-quarter) GDP growth, accounting does not improve the out-of-sample accuracy of predictions because the professional forecasters' predictions are relatively efficient. Accounting's predictive usefulness increases for more distant-term (three- and four-quarters-ahead) GDP growth forecasts: they contribute more to the model's predictions; moreover, their inclusion increases the model's out-of-sample predictive accuracy by 13 to 46%. Overall, four categories of accounting variables---relating to profits, accrual estimates (e.g., loan loss provisions or write-offs), capital raises or distributions, and capital allocation decisions (e.g., investments)---are most informative of the longer-term outlook of the economy.

World Economic Prospects Monthly

Overview: Vaccine hopes lift 2021 prospects ▀ The recent vaccine news points to a faster vaccination rollout than we had previously assumed. As a result, we have raised our world GDP growth forecast for 2021 to 5.2% from 4.9%, after an estimated 4.0% decline in 2020, and now judge the risks around our baseline forecast to be more broadly balanced. ▀ The global economy is set to end 2020 on a weak note as a result of many economies, particularly in Europe, re‐imposing activity restrictions to contain the pandemic. Nonetheless, the survey data point to a relatively mild downturn at a global level. Despite the eurozone and UK composite PMIs falling back into recession territory in November, the global composite PMI barely fell, reflecting improvements in the US and elsewhere. ▀ More importantly, but conditional on the Pfizer/BioNTech, Oxford/AstraZeneca and Moderna vaccines quickly gaining approval for use, it seems increasingly likely that some advanced economies (AEs) could vaccinate significant proportions of their most vulnerable cohorts early next year. Reflecting this, we now expect many AEs to begin a meaningful and sustained lifting of restrictions around March/April time, triggering a mid‐year mini‐boom in these economies. ▀ Most emerging markets (EMs) are likely to see slower vaccine rollout, suggesting that the near‐term lift for these economies will be smaller. Nonetheless, stronger demand from AEs, rising commodity prices and the ongoing weakening of the US dollar should all be supportive for the outlook. ▀ The light at the end of the tunnel will allow governments to reverse some of the exceptional policy support measures put in place this year and allow the passing of the growth baton from the public to the private sector. While this is a necessary part of the recovery process, it will be a difficult transition and there are likely to be bumps in the process. So, although the chances of a stronger recovery have risen, we still see medium‐term economic scarring from the pandemic.

A Multi-Country BVAR Model for the External Sector

This paper describes a multi-country BVAR model developed and used by the Monetary Policy Department of the Bank of Russia. The model makes it possible to build coordinated scenario forecasts for the main macro-variables of the USA, the euro area, and China. The simultaneous modelling for the three economies makes it possible to take into account multi-country interactions of the variables and, thus, improve the predictive performance of the model compared to VAR analogues intended for individual countries. The model is based on the deviations of the variables from their potential values, which enhances GDP growth forecasts compared to a non-detrended design. A wide range of macroeconomic and financial indicators in the model makes the forecast of overall inflation more accurate against simpler benchmarks.

Tracking the Characteristics of Economic Growth Vulnerability to COVID-19: A Preliminary Analysis

In response to the global expansion of COVID-19, governments around the world have implemented social distancing measures resulting in an unprecedented fall in economic activity. Consequently, the economic growth forecasts for 2020 have been adjusted downward in most countries, yet the magnitude of the decline has been different. In this article, we examine the economic vulnerability to COVID-19 -measured as the change in GDP growth forecasts for 2020- and its determinants for a sample of 194 countries. We identify the characteristics of the external sector and macroeconomic policy stance that are associated with an increase in economic vulnerability during the pandemic.

Forecasting US economic growth in downturns using cross-country data

The Covid-19 pandemic has created tremendous downward pressure on economic activity and revived interest in forecasting economic growth during severe downturns. However, most dynamic factor models used to forecast GDP growth include only domestic data. We construct a large data set of 77 countries representing over 90 percent of global GDP and show that including cross-country data helps produce more accurate forecasts of US GDP growth during economic downturns, but is less helpful in normal times. We provide explanations why this is the case.

REAL-TIME PROBABILISTIC NOWCASTS OF UK QUARTERLY GDP GROWTH USING A MIXED-FREQUENCY BOTTOM-UP APPROACH

We propose a nowcasting system to obtain real-time predictive intervals for the first-release of UK quarterly GDP growth that can be implemented in a menu-driven econometric software. We design a bottom-up approach: forecasts for GDP components (from the output and the expenditure approaches) are inputs into the computation of probabilistic forecasts for GDP growth. For each GDP component considered, mixed-data-sampling regressions are applied to extract predictive content from monthly and quarterly indicators. We find that predictions from the nowcasting system are accurate, in particular when nowcasts are computed using monthly indicators 30 days before the GDP release. The system is also able to provide well-calibrated predictive intervals.

Are professional forecasters overconfident?

Do professional forecasters have an accurate sense of the uncertainties surrounding their own forecasts? This paper examines forecaster overconfidence by comparing ex ante, surveyed forecaster uncertainty with ex post, realised uncertainty based on the dispersion of an individual’s forecast errors. Unlike the literature that focuses on consensus forecasts, our focus is at the level of the individual forecaster. Using microdata from the three major surveys of professional forecasters (Euro Area, US and UK), we examine real GDP growth forecasts over the period 1999–2015. Our findings show that overconfidence dominates among individual forecasters, particularly for longer forecast horizons, and that individual forecasters appear to have little understanding of their own uncertainty.

World economy

Compared to three months ago, we have revised up our global GDP growth forecast for this year from −4.5% to −4.2% on the back of evidence showing a faster than anticipated recovery around the middle of the year. That said it has become clear than the expansion has already entered a second, slower growth phase. With many economies, particularly in Europe, needing to reimpose some restrictions on activity in the light of rising Covid‐19 cases, we have lowered our forecast for global GDP growth next year from 5.8% to 5.2%.

World Economic Prospects Monthly

Overview: Renewed restrictions dampen prospects ▀ The global economic recovery has now entered a second, slower growth phase. With many economies, particularly in Europe, needing to reimpose some restrictions on activity in the light of rising Covid‐19 cases, we have again nudged down our forecast for global GDP growth next year, from 5.4% to 5.2%. ▀ In response to recent GDP releases and some other timely indicators, we have edged up our growth estimates for Q2 and Q3 2020 a touch at a global level. But this improvement has been broadly offset by downward revisions to our GDP growth forecast for Q4 2020 and early next year. ▀ True, retail sales and consumer confidence data have recently been encouraging in the advanced economies as a whole. But it is unclear whether the strength of the former reflects households spending more freely or continued substitution of spending away from services towards goods as a result of restrictions to curb the spread of Covid‐19. ▀ It is also uncertain as to the extent that households and firms will adjust behaviour in response to the recent resurgence in the number of Covid‐19 cases in many parts of the world. Expectations of further curbs to come or a slower return to normal may also prompt households and firms to become more cautious. In this respect, the renewed, albeit small, fall in the global services PMI in September provides pause for thought. ▀ Overall then we have become somewhat more cautious about the pace of the expansion as we move into Q4. Growth over the next few quarters is still expected to be similar to that recorded during the post‐global financial crisis recovery. But this is conditional on very supportive fiscal and monetary policy as well as the avoidance of a widespread return to lockdown‐style conditions. Thus, risks to our baseline forecast remain firmly skewed to the downside.

A Counterfactual Economic Analysis of COVID-19 Using a Threshold Augmented Multi-Country Model

This paper develops a threshold-augmented dynamic multi-country model (TGVAR) to quantify the macroeconomic effects of COVID-19. We show that there exist threshold effects in the relationship between output growth and excess global volatility at individual country levels in a significant majority of advanced economies and in the case of several emerging markets. We then estimate a more general multi-country model augmented with these threshold effects as well as long term interest rates, oil prices, exchange rates and equity returns to perform counterfactual analyses. We distinguish common global factors from trade-related spillovers, and identify the Covid-19 shock using GDP growth forecast revisions of the IMF in 2020Q1. We account for sample uncertainty by bootstrapping the multi-country model estimated over four decades of quarterly observations. Our results show that the Covid-19 pandemic will lead to a significant fall in world output that is most likely long-lasting, with outcomes that are quite heterogenous across countries and regions. While the impact on China and other emerging Asian economies are estimated to be less severe, the United States, the United Kingdom, and several other advanced economies may experience deeper and longer-lasting effects. Non-Asian emerging markets stand out for their vulnerability. We show that no country is immune to the economic fallout of the pandemic because of global interconnections as evidenced by the case of Sweden. We also find that long-term interest rates could fall significantly below their recent lows in core advanced economies, but this does not seem to be the case in emerging markets.

COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting

The number of short-time workers from January to April 2020 is used to now- and forecast quarterly GDP growth. We purge the monthly log level series from the systematic component to extract unexpected changes or shocks to log short-time workers. These monthly shocks are included in a univariate model for quarterly GDP growth to capture timely, current-quarter unexpected changes in growth dynamics. Included shocks additionally explain 24% in GDP growth variation. The model is able to forecast quite precisely the decrease in GDP during the financial crisis. It predicts a mean decline in GDP of 5.7% over the next two quarters. Without additional growth stimulus, the GDP level forecast remains persistently 4% lower in the long run. The uncertainty is large, as the 95% highest forecast density interval includes a decrease in GDP as large as 9%. A recovery to pre-crisis GDP level in 2021 lies only in the upper tail of the 95% highest forecast density interval.

A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area

AbstractThis paper examines point and density forecasts of real GDP growth, inflation, and unemployment from the European Central Bank's Survey of Professional Forecasters. We analyze individual uncertainty measures as well as introduce individual point‐ and density‐based disagreement measures. The analysis indicates forecasters’ uncertainty and disagreement display substantial heterogeneity and persistence, with the latter feature challenging a key prediction of expectations models emphasizing information frictions. We also find that uncertainty is characterized by prominent respondent effects and disagreement by prominent time effects, suggesting these divergent properties underlie the well‐documented weak uncertainty–disagreement linkage. Taken together, our results provide a basis for further development of expectations models.

Growth forecast revisions over business cycles: Evidence from the Survey of Professional Forecasters

This paper studies how expectations regarding current and future output growth are revised as the state of the economy evolves. We study this issue using GDP growth forecasts from the Survey of Professional Forecasters. We find an asymmetric relationship between forecast revisions and GDP growth. Forecasters make larger forecast revisions during contractions than during expansions when the deviation of the economy from its normal state is controlled for.

World Economic Prospects Monthly

Overview: Recent economic strength will not last ▀ Recent data confirm that the initial post‐lockdown phase of the recovery has been robust and points to a record‐breaking rise in GDP in Q3. Nonetheless, there are already signs that some sectors are beginning to lose momentum. Given this, and the lack of tangible medical progress towards overcoming the pandemic, we have cut our forecast for global GDP growth in 2021 to 5.4% (from 5.8% seen last month) after an expected 4.4% drop this year. ▀ Recent monthly economic data have surprised to the upside, so we now expect the huge Q2 fall in global GDP to be broadly reversed in Q3. Even so, the level of GDP this quarter will still be over 4% below the Q4 2019 level, a bigger fall than the peak‐to‐trough decline in global GDP during the global financial crisis. ▀ Beyond Q3, growth looks set to slow notably. After initially bouncing strongly, retail sales growth has eased, perhaps even starting to contract. This may partly reflect compositional changed to households spending patterns as some services re‐open. ▀ Even so, our aggregate advanced economy consumer sentiment measure fell for a second month running in August, suggesting some caution among households. This will be exacerbated by the ongoing unwinding of emergency fiscal support measures for households. With banks already expecting to tighten credit standards and some firms beginning to repay some of the money that they borrowed during lockdown, we expect the investment recovery to be quite weak. ▀ Although we still expect average q/q global GDP growth to broadly match that seen during 2010 – the best year since the global financial crisis – we now see GDP expanding by 5.4% in 2021, 0.4pp lower than a month ago. This partly reflects the fact that we now assume a Covid‐19 vaccine will not be in circulation until the middle of next year, about six months later than we had previously assumed, resulting in a more prolonged period of social distancing measures.