For how long do IMF forecasts of world economic growth stay up-to-date?

This study analyses the performance of the International Monetary Fund (IMF) World Economic Outlook output forecasts for the world and for both the advanced economies and the emerging and developing economies. With a focus on the forecast for the current year and the next year, we examine the durability of IMF forecasts, looking at how much time has to pass so that IMF forecasts can be improved by using leading indicators with monthly updates. Using a real-time data set for GDP and for indicators, we find that some simple single-indicator forecasts on the basis of data that are available at higher frequency can significantly outperform the IMF forecasts as soon as the publication of the IMF’s Outlook is only a few months old. In particular, there is an obvious gain using leading indicators from January to March for the forecast of the current year.     

Did political constraints bind during transition?Evidence from Czech elections, 1990–20021

Many theoretical models of transition are driven by the assumption that economic decision making is subject to political constraints. In this paper we test whether the ‘winners’ and ‘losers’ of economic reform influenced voting behaviour in the first five national elections in the Czech Republic. We propose that voters, taking stock of endowments from the planning era, could predict whether they would become winners or losers of transition. Using survey data we measure the degree to which regions were ‘not afraid’ or ‘afraid’ of economic reform in 1990. We define the former as potential ‘winners’ who should vote for pro‐reform parties and the latter as potential ‘losers’ who should support left‐wing parties. Using election results and economic indicators at the regional level, we demonstrate that there is persistence in support for pro‐reform and communist parties which is driven by prospective voting based on initial conditions in 1990. We find that regional unemployment rates in 2002 are good predictors of voting patterns in 1990 and provide empirical evidence that political constraints bind during transition.     

Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.     

Using newspapers for tracking the business cycle: a comparative study for Germany and Switzerland

The use of news-based data for tracking the real economy has gained popularity recently as newspapers archives have become accessible and the need for timely information has soared. In this article, on the basis of keyword searches in newspaper articles we construct several versions of the so-called Recession-word Index (RWI) for Germany and Switzerland and exploit its use for forecasting. Our main findings are the following. First, we show that augmenting benchmark autoregressive models with the RWI leads to improvement in accuracy of one-step-ahead forecasts of GDP growth compared with those obtained by benchmark models. Second, the accuracy of out-of-sample forecasts obtained with models augmented with the RWI is comparable to that of models augmented with established economic indicators, such as the Ifo Business Climate Index and the ZEW Indicator of Economic Sentiment for Germany, and the KOF Economic Barometer and the Purchasing Managers Index in manufacturing for Switzerland. Our results are robust to changes in estimation/forecast samples, the use of rolling versus expanding estimation windows and the inclusion of a web-based recession indicator from Google Trends. As our indices are timely and simple to construct, they could be replicated in countries or regions where no reliable economic indicators exist or their provision is very costly.     

Global value chain and growth convergence: Applied especially to China

We propose a new convergence potential indicator for standard growth convergence regressions: the global value chain (GVC) position index, measured by the contribution of high-skilled labour in total domestic value added. Our convergence indicator can avoid the lagged dependent variable problem discussed in Barro (2015 and 2016) and deliver more accurate forecasts for China’s GDP growth than GDP per capita does. Using the GVC position index, we predict that China’s growth potential remains at 7–8% between 2010 and 2015, while the predictions using GDP per capita as the convergence potential indicator are much lower.     

A variable P value rolling Grey forecasting model for Taiwan semiconductor industry production

The semiconductor industry plays an important role in Taiwan's economy. In this paper, we constructed a rolling Grey forecasting model (RGM) to predict Taiwan's annual semiconductor production. The univariate Grey forecasting model (GM) makes forecast of a time series of data without considering possible correlation with any leading indicators. Interestingly, within the RGM there is a constant, P value, which was customarily set to 0.5. We hypothesized that making the P value a variable of time could generate more accurate forecasts. It was expected that the annual semiconductor production in Taiwan should be closely tied with U.S. demand. Hence, we let the P value be determined by the yearly percent change in real gross domestic product (GDP) by U.S. manufacturing industry. This variable P value RGM generated better forecasts than the fixed P value RGM. Nevertheless, the yearly percent change in real GDP by U.S. manufacturing industry is reported after a year ends. It cannot serve as a leading indicator for the same year's U.S. demand. We found out that the correlation between the yearly survey of anticipated industrial production growth rates in Taiwan and the yearly percent changes in real GDP by U.S. manufacturing industry has a correlation coefficient of 0.96. Therefore, we used the former to determine the P value in the RGM, which generated very accurate forecasts.     

Forecasting UK consumer price inflation using inflation forecasts

The inflation rate is a key economic indicator for which forecasters are constantly seeking to improve the accuracy of predictions, so as to enable better macroeconomic decision making. Presented in this paper is a novel approach which seeks to exploit auxiliary information contained within inflation forecasts for developing a new and improved forecast for inflation by modeling with Multivariate Singular Spectrum Analysis (MSSA). Unlike other forecast combination techniques, the key feature of the proposed approach is its use of forecasts, i.e. data into the future, within the modeling process and extracting auxiliary information for generating a new and improved forecast. We consider real data on consumer price inflation in UK, obtained via the Office for National Statistics. A variety of parametric and nonparametric models are then used to generate univariate forecasts of inflation. Thereafter, the best univariate forecast is considered as auxiliary information within the MSSA model alongside historical data for UK consumer price inflation, and a new multivariate forecast is generated. We find compelling evidence which shows the benefits of the proposed approach at generating more accurate medium to long term inflation forecasts for UK in relation to the competing models. Finally, through the discussion, we also consider Google Trends forecasts for inflation within the proposed framework.     

Forecasting GDP all over the world using leading indicators based on comprehensive survey data

Comprehensive and international comparable leading indicators across countries and continents are rare. In this paper, we use a free and instantaneous available source of leading indicators, the ifo World Economic Survey (WES), to forecast growth of Gross Domestic Product (GDP) in 44 countries and three country aggregates separately. We come up with three major results. First, for more than three-fourths of the countries or country-aggregates in our sample, a model containing one of the major WES indicators produces on average lower forecast errors compared to a benchmark model. Second, the most important WES indicators are either the economic climate or the expectations on future economic development for the next six months. And third, adding the WES indicators of the main trading partners leads to a further increase in forecast accuracy in more than 50% of the countries. It seems therefore reasonable to incorporate economic signals from the domestic economy’s main trading partners.     

European commission’s fiscal forecasts in CEE countries: a thorough assessment

ABSTRACT

This paper assesses European Commission’s fiscal forecasts for a sample of 10 Central and Eastern European countries between 2005 and 2015. The analysis focus on forecasts of the budget balance, revenues, expenditures and debt and pays special attention to dynamics around business cycle turning points. Results suggest that the distribution of projection errors appears to be biased towards optimism of fiscal aggregates and accuracy increases as the forecast horizon shortens. We also find evidence of “forecast smoothing”. In addition, we find that, on average, the extent of optimism seems to increase during recessions (and to a lesser extent during recoveries). Moreover, errors in forecasting fiscal variables can be explained by forecasts errors of real GDP growth and inflation.     

Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.     

Do equity index industry groups improve forecasts of inflation and production? A US analysis

This study develops a new financial market indicator, which may be a useful addition to analysing real activity in the US. By taking the ratio of the price return of equity industry groups of the S&P 500 over a benchmark industry group, in this case taken to be the Utilities industry group, an indicator is created which represents the price return performance specific to each individual industry. We then perform recursive pseudo out-of-sample bivariate forecasts of future changes in the Industrial Production Index (IPI) and the Consumer Price Index (CPI) at 3-month, 6-month and 12-month horizons using each of the indicators and compare results against an AR forecast. The results of the bivariate forecasts using a number of the indicators produce better forecasts of changes in the IPI and are also significant for causality, both for the full sample period and when tested recursively. Bivariate forecasts of changes to the CPI, however, do not improve upon the AR forecasts.     

Macroeconomic expectations and time varying heterogeneity:evidence from individual survey data

ABSTRACT

The goal of this paper is to investigate forecast heterogeneity and time variability in the formation of expectations using disaggregated monthly survey data on macroeconomic indicators provided by Bloomberg from June 1998 to August 2017. We show that our panel of forecasters are not rational and are moderately heterogeneous and thus confirm that previously well-established results on asset prices hold for macroeconomic indicators. We propose a flexible hybrid forecast model defined at any time as a combination of the extrapolative, regressive, adaptive and interactive heuristics. Controlling for endogenous structural breaks, we find that experts adjust their forecast behaviour at any time with some inertia in extrapolative and adaptive profiles. Changes in the formation of expectations are triggered mostly by financial shocks, and uncertainty is dealt with by using complex processes in which the fundamentalist component overweighs chartist activity. Forecasters whose models combine different relevant rules and display high temporal flexibility provide the most accurate forecasts. Authorities can then stabilize the domestic markets by encouraging fundamentalists’ forecasts through increased transparency policy.     

Real-time forecasts of economic activity for Latin American economies

Macroeconomic policy decisions in real-time are based on the assessment of current and future economic conditions. Crucially, these assessments are made difficult by the presence of incomplete and noisy data. The problem is more acute for emerging market economies, where most economic data are released infrequently with a (sometimes substantial) lag. This paper evaluates nowcasts and forecasts of real GDP growth using five models for ten Latin American countries. The results indicate the flow of monthly data helps to improve forecast accuracy, and the dynamic factor model consistently produces more accurate nowcasts and forecasts relative to other model specifications, across most of the countries we consider.     

Does a multi-sectoral design improve indicator-based forecasts of the GDP growth rate? Evidence from Switzerland

This article presents a multi-sectoral composite indicator for the Swiss GDP growth rate, targeting a lead of two quarters. The in-sample period ranges from 1991 to 2002 and 14 data points are reserved as out of sample to assess the forecasting performance. The results appear promising, in terms of both phase and amplitude. Comparisons with two other uni-sectoral composite leading indicators for the same reference series–the traditional KOF (Konjunkturforschungsstelle) barometer as published until March 2006 and a uni-sectoral composite indicator computed from the same indicators as the multi-sectoral instrument–show that the new approach is superior to the alternatives, which is due to both its broader information basis as well as to the structure that is imposed by the multi-sectoral design. Yet, there are pronounced differences regarding the accuracy of the sectoral forecasts, so that there is scope for improvement.     

Evaluating FOMC forecast ranges: an interval data approach

The Federal Open Market Committee (FOMC) of the U.S. Federal Reserve publishes the range of members’ forecasts for key macroeconomic variables, but not the distribution of forecasts within this range. To evaluate these projections, previous papers compare the midpoint of the range with the realized outcome. This paper proposes an alternative approach to forecast evaluation that takes account of the interval nature of projections. It is shown that using the conventional Mincer–Zarnowitz approach to evaluate FOMC forecasts misses important information contained in the width of the forecast interval. This additional information plays a minor role at short forecast horizons but turns out to be of sometimes crucial importance for longer-horizon forecasts. For 18-month-ahead forecasts, the variation of members’ projections contains information that is more relevant for explaining future inflation than information embodied in the midpoint. Likewise, when longer-range forecasts for real GDP growth and the unemployment rate are considered, the width of the forecast interval comprises information over and above the one given by the midpoint alone.     

Optimal use of forecasts in economic policy decisions: An empirical test

A criterion for optimal use of forecasts of exogenous variables in a linear policy model with quadratic preferences is given, based on Johansen (1972). By calculating regressions of observed values with respect to raw forecast values used in the observation period, equations are established which can be used to transform raw forecasts into ‘certainty equivalents’. The method is applied to data from national budgeting and corresponding national accounts in Norway 1948–-1971. Especially the forecasts for the volume of exports, but also for import prices, could be considerably improved, for policy purposes in the context of the model of the type mentioned, by the transformation described. Some of the forecasts, when optimally transformed, should be permitted to fluctuate more strongly and thereby influence policy more strongly. Some tentative results for Sweden and the Netherlands are briefly mentioned and compared with the evidence for Norway.     

Survey-based forecast distributions for Euro Area growth and inflation: ensembles versus histograms

Ensemble methods can be used to construct a forecast distribution from a collection of point forecasts. They are used extensively in meteorology, but have received little direct attention in economics. In a real-time analysis of the ECB’s Survey of Professional Forecasters, we compare ensemble methods to histogram-based forecast distributions of GDP growth and inflation in the Euro Area. We find that ensembles perform very similarly to histograms, while being simpler to handle in practice. Given the wide availability of surveys that collect point forecasts but not histograms, these results suggest that ensembles deserve further investigation in economics.     

Has The Accuracy of Macroeconomic Forecasts for Germany Improved?

This study asks whether the accuracy of macroeconomic forecasts for Germany has improved over time. We examine one‐year‐ahead forecasts of rates of real GDP growth and inflation for the years 1967–2010, by three major German forecasters and the OECD. We find that overall error levels are high but not much different from those of the U.S. and U.K. In the 1980s and 1990s accuracy improved somewhat, but has now returned to its 1970s level, indicating that it reflects the variance of growth and inflation. Benchmark comparisons with these predictions with ex post forecasts of a macroeconometric model indicate that accuracy can be improved, but it will be difficult to achieve.     

Experts,firms, consumers or even hard data? Forecasting employment in Germany

In this article, we forecast employment growth for Germany with data for the period from November 2008 to November 2015. Hutter and Weber (2015) introduced an innovative unemployment indicator and evaluated the performance of several leading indicators, including the Ifo Employment Barometer (IEB), to predict unemployment changes. Since the IEB focuses on employment growth instead of unemployment developments, we mirror the study by Hutter and Weber (2015). It turns out that in our case, and in contrast to their article, the IEB outperforms their newly developed indicator. Additionally, consumers’ unemployment expectations and hard data such as new orders exhibit a high forecasting accuracy.     

Monetary policy regime change and regional inflation dynamics: looking through the lens of sector‐level data for Korea

This paper explores the impact of the adoption of inflation targeting (IT) on the dynamics of city‐level inflation in Korea using both aggregate and sector‐level data. When looking at aggregate regional inflation, we find that the mean, volatility and persistence fell in all cities in the wake of the monetary policy regime change, consistent with other evidence in the literature. Delving more deeply into the disaggregate data reveals additional insights however. For most of the changes we observe in the dynamics of regional inflation, we find that the aggregate effects are being driven primarily by sectors that fall into the ‘Services’ category. We posit that the impact of better anchored inflationary expectations is primarily on the less‐traded services sectors of the economy, where the domestic monetary policy framework has a relatively larger influence. When it comes to the increased co‐movement observed across regions under an IT regime, however, it is the ‘Commodities’ sectors rather than ‘Services’ that are responsible, probably because services inflation becomes relatively more influenced by local factors once it has stabilized within the target range. Therefore, adoption of IT may not necessarily increase all measures of regional synchronization even when the goal of better‐anchored inflationary expectations is achieved.