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).     

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.     

后危机时期世界经济格局的动态转换

始于2007年的金融危机使全球经济遭到了历史罕见的巨大冲击。世界经济的各种力量对比开始发生此消彼长的变化,世界经济格局处于动态转换之中,表现为世界经济结构调整加快、全球经济治理机制发生深刻变革、科技创新孕育产业转型和升级、发展中国家特别是新兴市场国家整体实力步入上升期、世界多极化获得深入发展。我国应努力增强参与世界经济治理机制变革的能力,大力实施扩大内需战略,积极推进产业结构转型升级。     

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.     

Evaluating factor forecasts for the UK: The role of asset prices

This paper applies a large data set, consisting of 167 monthly time series for the UK, both economic and financial, to simulate out-of-sample predictions of industrial production, inflation, 3-month Treasury Bills, and other variables. Fifteen dynamic factor models that allow forecasting based on large panels of time series are considered. The performances of these factor models are then compared to the following competing models: a simple univariate autoregressive, a vector autoregressive, a leading indicator, and a Phillips curve models. The results show that the best dynamic factor models outperform the competing models in forecasting at 6-, 12-, and 24-month horizons. Thus, the financial markets may have predictive power for the economic activity. This can be a useful tool for central banks and financial institutions, which may use the factor models to construct leading indicators of the economic conditions. In addition, researchers can see a strategic application of factor models.     

A NEW VISION OF THE KNOWLEDGE ECONOMY

Abstract.  To date, more than half of the output in the major OECD countries has been knowledge based. This paper argues, however, that the current growth-oriented exposition of the knowledge economy rooted in the conventional concept of free competition is insufficient for promoting the long-term development of human societies. Although we now live in a knowledge economy, most countries have been concurrently characterized by serious phenomena such as environmental degradation and growing economic inequality. The prospect of meeting global commitments, for instance, to reducing inequality, as outlined in the 1995 World Summit for Social Development in Copenhagen and endorsed in the United Nations Millennium Declaration, is bleak and the global society as a whole has become less and less sustainable. Indeed, the world is better seen as composed of numerous (but finite) knowledge economies. To take up the challenge of sustainable development of human societies, we have to develop a pluralistic perspective of the knowledge economy and fully acknowledge the characteristics of each unique knowledge system (such as indigenous knowledge possessed by a small tribe). Once we can help each individual knowledge system develop into a specific set of economic institutions that freely exchange concepts and beliefs with each other in a global environment, we will be able to develop a global economy that embodies a value-committed basis that assures a sustainable path of development on earth.     

Probabilistic forecasting of output growth,inflation and the balance of trade in a GVAR framework

We apply a global vector autoregressive (GVAR) model to the analysis of inflation, output growth and global imbalances among a group of 33 countries (26 regions). We account for structural instability by use of country‐specific intercept shifts, the timings of which are identified taking into account both statistical evidence and our knowledge of historic economic conditions and events. Using this model, we compute both central forecasts and scenario‐based probabilistic forecasts for a range of events of interest, including the sign and trajectory of the balance of trade, the achievement of a short‐term inflation target, and the incidence of recession and slow growth. The forecasting performance of the GVAR model in relation to the ongoing financial crisis is quite remarkable. It correctly identifies a pronounced and widespread economic contraction accompanied by a marked shift in the net trade balance of the Eurozone and Japan. Moreover, this promising out‐of‐sample forecasting performance is substantiated by a raft of statistical tests which indicate that the predictive accuracy of the GVAR model is broadly comparable to that of standard benchmark models over short horizons and superior over longer horizons. Hence we conclude that GVAR models may be a useful forecasting tool for institutions operating at both the national and supra‐national levels. Copyright © 2010 John Wiley & Sons, Ltd.     

Financial information and macroeconomic forecasts

We study the forecasting power of financial variables for macroeconomic variables in 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices, and house prices have considerable predictive power for macroeconomic variables at the one- to four-quarter horizons. A forecasting model that includes financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85% of our sample countries at the four-quarter horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.     

我国服务贸易现状及竞争力研究

伴随着经济全球化与国际经济分工的发展,服务业已成为全球第一大产业,服务业发展水平成为衡量一个国家或地区生产社会化程度和市场经济发展水平的重要标志。与服务业相适应的国际服务贸易发展也十分迅速并已经成为一个国家对外开放的窗口和渠道,成为衡量一个国家整体水平的重要指标并正逐渐成为全球经济发展与竞争的重点。     

全球经济失衡的成因及我国的对策探析

当前世界经济的发展,是不平衡的发展。全球经济失衡是长期形成的,其原因是复杂的、多方面的,其调整是一个极其困难和缓慢的过程,然而它已经并将长期影响到世界及各国的均衡性和持续性发展,已成为当今世界日益严峻的问题。文章将分析全球经济失衡的原因,并提出一些对我国有益的建议,力求使中国的经济增长更加健康和可持续。     

我国火炬软件产业基地产业竞争力分析

随着世界经济的快速发展,软件产业发展己经成为衡量一个国家或区域综合实力的重要标志之一。我国软件产业在上世纪90年代末,在国家政策的鼓励与支持下,许多省份相继成立国家火炬计划软件产业基地,发展软件产业,顺应世界经济发展的需要。本文利用SPSS统计软件,采用因子分析法建立了我国软件产业竞争力评价模型,并针对最后的评价结果提出改进相关建议与意见。     

Forecasting economic and financial variables with global VARs

This paper considers the problem of forecasting economic and financial variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model, previously estimated by Dees, di Mauro, Pesaran, and Smith (2007) and Dees, Holly, Pesaran, and Smith (2007) over the period 1979Q1–2003Q4, is used to generate out-of-sample forecasts one and four quarters ahead for real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1–2005Q4. Forecasts are obtained for 134 variables from 26 regions, which are made up of 33 countries and cover about 90% of the world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modelling problem, and the heterogeneity of the economies considered–industrialised, emerging, and less developed countries–as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed, the double-averaged GVAR forecasts perform better than the benchmark competitors, especially for output, inflation and real equity prices.     

Long term projections of carbon emissions

Future scenarios of climate change depend on the projections of greenhouse gas emissions, which are highly uncertain. A framework for projecting emissions should focus on the sources of economic growth and the changing structure of the global economy over time. It also requires an understanding of key historical and statistical issues, including the role of convergence assumptions and the appropriate basis of comparisons between countries. This paper presents a methodology developed using the G-Cubed multi–country model in which the economic structure and emissions outcomes are determined simultaneously. In order to illustrate the importance of the assumptions underlying the way these long term carbon projections are produced we also explore the debate around the “Castles and Henderson Critique” of the Special Report on Emission Scenarios (SRES) regarding the use of Market Exchange Rates (MERs) rather than Purchasing Power Parity exchange rates (PPPs) for benchmarking income differentials in the world economy. We show that under one scenario, emission projections based on convergence assumptions defined in MER terms are 40% higher by 2100 than emission projections based on PPP comparisons of income differentials. While this result can not necessarily be generalized to all forecasting frameworks, the potential magnitude of the difference suggests that this is a significant issue for such projection efforts.     

新兴经济体发展对国际社会的影响力研究

当今世界经济呈现多极化趋势,新兴经济体成为世界经济增长的主要动力,对世界政治经济格局产生深刻影响。本文阐述新兴经济体在政治、经济、社会等各方面发展状况,分析新兴经济体的发展对国际社会的影响力。新兴经济体的发展促进了世界经济、国际贸易的稳步发展和产业结构调整,为消除世界范围的贫困和维护世界和平做出了积极的贡献,为发展中国家加快发展和加强“区域化”多边合作提供了可借鉴模式,同时对全球治理结构转型和国际秩序重塑产生一定影响。     

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.     

Cheap Labor Strategies and Export-Oriented Industrialization: Some Lessons from the Los Angeles/East Asia Apparel Connection

In today's global manufacturing system, production is increasingly dispersed across borders. Particularly noteworthy is the rapid expansion of industrial exports from third world countries, especially the NICs of East Asia. With the entry of the formerly socialist countries of eastern Europe into the global capitalist economy, the dynamics of global economic restructuring are likely to change. This paper focuses on the lessons that can be learned from studying trans-Pacific global commodity chains, drawing out implications for East-Central Europe. In particular, we explore the role of flexibilization, subcontracting and state policy.     

低碳贸易壁垒的内容、影响及应对措施

发展低碳经济,减少碳排放,是延缓全球气候变暖的迫切需要。以低碳经济为代表的新技术革命也是全球经济复苏的引擎和下一轮经济增长的动力。制定碳标准,征收碳关税,一方面起到了监督减排责任的作用,但另一方面也成为新的技术性贸易壁垒。低碳贸易壁垒是在应对气候变化的大背景下产生的一类新型贸易措施,贸易保护主义将来更多是以低碳壁垒等技术性壁垒出现。由碳关税和低碳壁垒衍生的税收成本和技术标准的提高,将给包括中国在内的新兴经济体的出口产品增加生产成本,影响其出口贸易的发展。为应对世界经济和贸易格局向低碳化演变及发达国家设置低碳贸易壁垒对出口造成的严峻挑战,中国必须实施贸易低碳化战略,加快转型步伐。     

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.     

Forecasting national activity using lots of international predictors: An application to New Zealand

We assess the marginal predictive content of a large international dataset for forecasting GDP in New Zealand, an archetypal small open economy. We apply “data-rich” factor and shrinkage methods to efficiently handle hundreds of predictor series from many countries. The methods covered are principal components, targeted predictors, weighted principal components, partial least squares, elastic net and ridge regression. We find that exploiting a large international dataset can improve forecasts relative to data-rich approaches based on a large national dataset only, and also relative to more traditional approaches based on small datasets. This is in spite of New Zealand’s business and consumer confidence and expectations data capturing a substantial proportion of the predictive information in the international data. The largest forecasting accuracy gains from including international predictors are at longer forecast horizons. The forecasting performance achievable with the data-rich methods differs widely, with shrinkage methods and partial least squares performing best in handling the international data.