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.     

Forecasting Singapore's quarterly GDP with monthly external trade

In this paper we suggest a methodology to formulate a dynamic regression with variables observed at different time intervals. This methodology is applicable if the explanatory variables are observed more frequently than the dependent variable. We demonstrate this procedure by developing a forecasting model for Singapore's quarterly GDP based on monthly external trade. Apart from forecasts, the model provides a monthly distributed lag structure between GDP and external trade, which is not possible with quarterly data.     

Google data in bridge equation models for German GDP

Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.     

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.     

World trade recovering but still reasons for caution

• ? World trade has picked up in recent months, expanding at the fastest pace in six years in the first quarter, with the rise fairly evenly split between advanced and emerging markets. Stronger activity in China and a broader upturn in global investment have been key factors. But there are still reasons for caution. Although the ‘cyclical’ element in world trade is improving, the ‘trend’ element is not thanks to changes in supply chains and a lack of trade liberalisation.
• ? World trade growth looks set to reach about a 4% annual rate in Q1 2017, the fastest pace since 2011. Alternative freight‐based indicators confirm the upturn. This suggests some modest near‐term upside risk to our world growth forecasts.
• ? Recent growth has been evenly split between advanced countries and emerging markets (EM). In EM, the end of deep recessions in Russia and Brazil and an upturn in China have been key factors. China directly added 0.5 percentage points to annual world trade growth over recent months and firmer growth there has also pushed up commodity prices and the spending power and imports of commodity exporters.
• ? Another important positive factor is an improvement in investment, which is a trade‐intensive element of world GDP. Rising capital goods imports across a range of countries suggest the drag on world trade from weak investment is fading.
• ? The decline in the ratio of world trade growth to world GDP growth over recent years has both cyclical and structural elements. But while the cyclical component now seems to be improving, there is little evidence that the structural part – responsible for between a half and two‐thirds of the recent decline – is doing likewise.
• ? Key factors behind the structural decline in world trade growth are changes in supply chains and a lack of trade liberalisation/protectionism. Both are likely to remain a drag over the coming years. Meanwhile, a levelling‐off of growth in China and drop back in commodity prices could curb the recent cyclical uptick.

     

Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis

The literature on mixed-frequency models is relatively recent and has found applications across economics and finance. The standard application in economics considers the use of (usually) monthly variables (e.g. industrial production) for predicting/fitting quarterly variables (e.g. real GDP). This paper proposes a multivariate singular spectrum analysis (MSSA) based method for mixed-frequency interpolation and forecasting, which can be used for any mixed-frequency combination. The novelty of the proposed approach rests on the grounds of simplicity within the MSSA framework. We present our method using a combination of monthly and quarterly series and apply MSSA decomposition and reconstruction to obtain monthly estimates and forecasts for the quarterly series. Our empirical application shows that the suggested approach works well, as it offers forecasting improvements on a dataset of eleven developed countries over the last 50 years. The implications for mixed-frequency modelling and forecasting, and useful extensions of this method, are also discussed.     

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.     

The J-Curve at industry level: Evidence from Sweden–US trade

Previous research that investigated the impact of real depreciation of kronor on Swedish trade balance used trade data either between Sweden and the rest of the world or between Sweden and each of her trading partners. Not much support was provided for a significant effect, especially in the case of Swedish–US trade. In this paper we consider the trade flows between Sweden and the US one more time and try to disaggregate the data by industry. We consider the trade balance of each of the 87 industries that trade between the two countries and investigate the short-run and the long-run effects of real depreciation of kronor on each industry's trade balance. While we find short-run significant effects in the majority of the industries, the short-run effects last into the long-run favorable effects only in 23 of 87 industries.     

Trade networks and economic fluctuations in Asian countries

The paper presents a new methodology, based on tensor decomposition, to map dynamic trade networks and to assess its strength in forecasting economic fluctuations at different periods of time in Asia. Using the monthly merchandise import and export data across 33 Asian economies, together with the US, EU and UK, we detect the community structure of the evolving network and we identify clusters and central nodes inside each of them. Our findings show that data are well represented by two communities, in which People's Republic of China and Japan play the major role. We then analyze the synchronisation between GDP growth and trade. Furthermore we apply our model to the prediction of economic fluctuations. Our findings show that the model leads to an increase in predictive accuracy, as higher order interactions between countries are taken into account.     

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of both point and density forecasts.     

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.     

Keeping track of global trade in real time

This paper builds an innovative composite world trade-cycle index by means of a dynamic factor model for short-term forecasts of world trade growth of both goods and (usually neglected) services. Trade indicators are selected using a multidimensional approach, including Bayesian model averaging techniques, dynamic correlations, and Granger non-causality tests in a linear vector autoregression framework. To overcome real-time forecasting challenges, the dynamic factor model is extended to account for mixed frequencies, to deal with asynchronous data publication, and to include hard and survey data along with leading indicators. Nonlinearities are addressed with a Markov switching model. Pseudo-real-time empirical simulations suggest that: (i) the global trade index is a useful tool for tracking and forecasting world trade in real time; (ii) the model is able to infer global trade cycles very precisely and better than several competing alternatives; and (iii) global trade finance conditions seem to lead the trade cycle, a conclusion that is in line with the theoretical literature.     

Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

Economies in Transition: How Important Is Trade Openness for Growth?*

We investigate the effect of trade openness on economic growth in transition countries using a transparent statistical methodology that leads to data‐driven case studies. In particular, we employ synthetic control methods in a panel of transition economies and compare GDP growth in treated (that is, open) countries with growth in a convex combination of similar but untreated (that is, closed) countries. We find that trade liberalization tends to have a positive effect on the pattern of real GDP per capita. One of our most robust results shows that making the transition without opening up to trade considerably hampers growth.     

The determinants of venture creation time: a cross-country perspective

The purpose of this paper is to examine the impact of macro-institutional and macro-non-institutional factors on the new venture creation time across emerging as well as developed economies in Europe using panel data from 2003 to 2006 in 15 emerging and developed countries. This paper finds significant relationships between the venture start-up time and institutional factors that include lending interest rates, start-up procedures, and taxation and one non-institutional factor, GDP per capita. Additionally, we found differences in the factors between emerging and developed countries. Institutional factors, such as start-up procedures and trade opportunities, are important determinants of new venture creation time in emerging countries, consistent with the findings of recent studies. To encourage business formation, policy makers may need to revise policies concerning these factors which can facilitate or restrict new venture formation. Implications for further research and practice are discussed.     

Weak world trade could spark competitive devaluations

• World trade growth has slowed sharply in 2015, with our forecast for growth just 1% for the year. High frequency indicators suggest a stagnant picture, with trade in key emerging markets (EM) especially weak. Import growth in the US and Eurozone remains positive and is holding up world trade, but there are downside risks here also. Very slow world trade growth risks incentivising competitive depreciations and depressing global bond yields.
• In August our OE export indicator fell to its lowest level since late‐2012 –; the point when the US announced ‘QE3’. Its weakness is corroborated by other indicators such as container trade and air freight.
• The main drag to world trade is from emerging markets, especially the BRIC‐4 whose import volumes contracted sharply in H1 2015, cutting more than 1 percentage point from annual growth in goods trade.
• US and European import growth looks stronger and should be supported in 2016 by firming GDP growth. This is an important support for world trade, but the latest data suggest some downside risks here also.
• The weaker world demand growth is then the more that trade will appear like a zero‐sum game where a country can benefit only at the expense of its competitors. This has potentially important implications for asset prices: in particular, countries may turn to competitive depreciation, adding further to global deflationary pressures and holding down global bond yields.

     

Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data

This paper discusses a factor model for short-term forecasting of GDP growth using a large number of monthly and quarterly time series in real-time. To take into account the different periodicities of the data and missing observations at the end of the sample, the factors are estimated by applying an EM algorithm, combined with a principal components estimator. We discuss some in-sample properties of the estimator in a real-time environment and propose alternative methods for forecasting quarterly GDP with monthly factors. In the empirical application, we use a novel real-time dataset for the German economy. Employing a recursive forecast experiment, we evaluate the forecast accuracy of the factor model with respect to German GDP. Furthermore, we investigate the role of revisions in forecast accuracy and assess the contribution of timely monthly observations to the forecast performance. Finally, we compare the performance of the mixed-frequency model with that of a factor model, based on time-aggregated quarterly data.     

World trade forecasts upgraded,but headwinds from Asia

• ? Most leading indicators of world trade point to growth remaining robust in the next few months, but there are some headwinds, especially from Asia. Overall, we expect trade growth to decelerate this year, yet the outlook has improved since August. We see world trade rising by 6.1% in 2017 and by 4.8% this year, up from our previous forecasts of 5.7% and 3.8%, respectively .
• ? The latest trade volume data for the major economies support our forecasts, as does our survey‐based export indicator, which leads trade by around three months. This indicator and the main measure of global freight volumes are consistent with world trade continuing to grow by around 6% y/y in the near term.
• ? World trade growth is likely to be supported by emerging markets (EMs), which made a large contribution to the trade recovery last year. Another factor that may be supportive – especially for EMs – is the slippage in the US dollar last year, as there is some evidence of a negative correlation between dollar strength and world trade.
• ? The recovery of demand in the Eurozone and expected fiscal stimulus in the US add to the positive constellation of factors supporting world trade growth. Business sentiment indicators remain positive and imply upside risks to our forecasts. Yet it is not obvious that they have a strong leading relationship with trade – and the statistical relationship has become weaker since 2007–2009. This reinforces our view that there has been a structural change in the relationship between world trade and world GDP.
• ? The main near‐term downside risks to world trade come from Asia. Freight indicators for Shanghai and Hong Kong have slowed markedly, as have semiconductor billings. Although Chinese activity indicators have also moderated, China's trade volume growth remains surprisingly strong.

     

人民币实际汇率与中国进出口贸易结构变迁(1997～2007年)——基于多种模型的动态分析

为分析人民币实际汇率对中国进出口贸易结构变迁的影响,基于1997～2007年季度SITC二位数水平下的中国进出口贸易面板数据、人民币实际汇率和中国及其贸易伙伴GDP季节时序数据,本文对相关变量进行了异质面板和单时序季节单整和协整检验,并进一步构建异质面板季节误差修正模型,对人民币实际汇率对中国进出口贸易结构变迁的长短期动态影响进行了实证分析,并针对中国当前的对外经贸和宏观经济发展现状提出了相应的政策建议。     

Will Brexit speed a seismic shift in UK trade patterns?

• The UK's trade pattern has shifted significantly away from the EU since the 1990s. Our analysis suggests that this shift will continue in the decades to come, with the EU share in UK goods exports potentially slipping to around 35% by 2035. Shifts in relative prices from moves in tariff and especially non‐tariff barriers could lower the share further.
• Over 60% of UK goods exports went to the EU in the late 1990s but this has fallen to around 45%. Slow EU growth is partly to blame, with UK exports to the EU barely expanding since 2007. But our analysis also shows that a 1% rise in EU GDP leads to only around half the rise in UK exports to the EU that a 1% rise in GDP in the rest of the world induces in UK exports to non‐EU countries.
• Based on our findings and OE forecasts of long‐term growth in the EU and the world, the EU share of UK goods exports could fall to 37% by 2035 and around 30% by 2050 – back to its 1960 level. The share of services exports to the EU has held up better but is lower than for goods, at around 40%.
• Weakening growth of UK exports to the EU has taken place despite the development of the EU single market since the early 1990s. Indeed, based on our projections UK goods exports to the single market could drop below 5% of UK GDP by 2050. These projections make no allowance for Brexit effects, but the declining importance of exports to the EU single market could colour prospective Brexit negotiations.
• Simple income‐based projections of potential country shares in future UK exports suggest a further swing towards emerging countries (EM) in the decades ahead, especially China and India. Exports to EM could approach 40% of the total by 2035. A shift in the pattern of trade preferences and restrictions faced by the UK post‐Brexit could spark even larger shifts in the structure of UK exports.