Forecasting GDP at the Regional Level with Many Predictors

In this study, we assess the accuracy of macroeconomic forecasts at the regional level using a large data set at quarterly frequency. We forecast gross domestic product (GDP) for two German states (Free State of Saxony and Baden‐Württemberg) and Eastern Germany. We overcome the problem of a ‘data‐poor environment’ at the sub‐national level by complementing various regional indicators with more than 200 national and international indicators. We calculate single‐indicator, multi‐indicator, pooled and factor forecasts in a ‘pseudo‐real‐time’ setting. Our results show that we can significantly increase forecast accuracy compared with an autoregressive benchmark model, both for short‐ and long‐term predictions. Furthermore, regional indicators play a crucial role for forecasting regional GDP.     

The Importance of Time‐Series Extrapolation for Macroeconomic Expectations

This article presents a simple experiment on how laypeople form macroeconomic expectations. Subjects have to forecast inflation and gross domestic product growth. By varying the information provided in different treatments, we can assess the importance of historical time‐series information vs. information acquired outside the experimental setting such as knowledge of expert forecasts. It turns out that the availability of historical data has a dominant impact on expectations and wipes out the influence of outside‐lab information completely. Consequently, backward‐looking behavior can be identified unambiguously as a decisive factor in expectation formation.     

Forecasting Turkish real GDP growth in a data-rich environment

This study generates nowcasts and forecasts for the growth rate of the gross domestic product in Turkey using 204 daily financial series with mixed data sa     

A structural Bayesian VAR for model-based fan charts

Inflation forecast uncertainty is of importance for a wide range of agents in the economy, central banks in particular. Ways to describe and account for this uncertainty in a consistent manner have received increasing attention of late, in part due to the growing number of inflation-targeting central banks. This article develops a large structural VAR for the Swedish economy and estimates it in a Bayesian framework. The methodology permits not only structural interpretation and analysis but offers a natural way to formalize forecast uncertainty, as the posterior predictive density from the model has the interpretation of a fan chart.     

Trends in energy consumption and carbon dioxide emissions of passenger cars and buses

In this work we develop aggregate car ownership and bus fleet models in order to forecast and compare fuel consumption and CO₂ emissions from passenger cars and buses. Greece was selected as a case study, being a country fairly representative of lower-income Mediterranean and Eastern European countries and data were collected for the period 1970 to 2002. Percent adults in the population, per capita gross domestic product, inflation, unemployment, car occupancy and bus kilometers were predictors included in the car ownership and bus fleet multiple regression models. A shift in the overall trend of both models around 1995 was explained as a slope change of per capita gross domestic product, possibly reflecting the impact of a boom of the Greek Stock Market along with a retirement program for older vehicles. Predictor variables were forecast via Box-Jenkins and the models were subsequently used to develop car ownership and bus fleet forecasts to the year 2010. We predict that the contribution of cars to total CO₂ emissions will rise to an astounding 95% of total CO₂ emissions from road passenger transport (excluding taxis and mopeds), an effect expected in other Mediterranean and Eastern European countries with socioeconomic characteristics similar to Greece. Suggestions for further research include developing regional car ownership forecasts in order to compare the dynamics of different regions within a country and looking into other land transportation means (such as mopeds, taxicabs and railway).     

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.     

How fast can China grow? The Middle Kingdom's prospects to 2030

Given its size and importance for global commodity markets, the question of how fast China can grow over the medium term is an important one. Using a Cobb–Douglas production function, we decompose the growth of trend GDP into those of the capital stock, labour, human capital and total factor productivity (TFP) and then forecast trend output growth out to 2030 using a bottom‐up approach based on forecasts that we build for each one of these factors. Our paper distinguishes itself from existing work in that we construct a forecast of Chinese TFP growth based on the aggregation of forecasts of its key determinants. In addition, our analysis is based on a carefully constructed estimate of the Chinese productive capital stock and a measure of human capital (based on Chinese wage survey data) that better reflects the returns to education in China. Our results suggest that Chinese GDP growth will slow from around 7% currently to approximately 5% by 2030, consistent with a gradual rebalancing of the Chinese economy characterized by a decline in the investment rate. Moreover, our findings underscore the growing importance of TFP growth as a driver of Chinese growth.     

Inflation,real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters

Expectations are at the centre of modern macroeconomic theory and policymakers. In this article, we examine the predictive ability and the consistency properties of macroeconomic expectations using data of the European Central Bank (ECB) Survey of Professional Forecasters (SPF). In particular, we provide evidence on the properties of forecasts for three key macroeconomic variables: the inflation rate, the growth rate of real gross domestic product and the unemployment rate.     

State dependent asymmetric loss and the consensus forecast of real U.S. GDP growth

It has been well documented that the consensus forecast from surveys of professional forecasters shows a bias that varies over time. In this paper, we examine whether this bias may be due to forecasters having an asymmetric loss function. In contrast to previous research, we account for the time variation in the bias by making the loss function depend on the state of the economy. The asymmetry parameter in the loss function is specified to depend on set state variables which may cause forecaster to intentionally bias their forecasts. We consider both the Lin–Ex and asymmetric power loss functions. For the commonly used Lin–Ex and Lin–Lin loss functions, we show the model can be easily estimated by least squares. We apply our methodology to the consensus forecast of real U.S. GDP growth from the Survey of Professional Forecasters. We find that forecast uncertainty has an asymmetric effect on the asymmetry parameter in the loss function dependent upon whether the economy is in expansion or contraction. When the economy is in expansion, forecaster uncertainty is related to an overprediction in the median forecast of real GDP growth. In contrast, when the economy is in contraction, forecaster uncertainty is related to an underprediction in the median forecast of real GDP growth. Our results are robust to the particular loss function that is employed in the analysis.     

MONTHLY GDP FORECASTING USING BRIDGE MODELS: APPLICATION FOR THE FRENCH ECONOMY

This paper presents a model to predict French gross domestic product (GDP) quarterly growth rate. The model is designed to be used on a monthly basis by integrating monthly economic information through bridge models, for both supply and demand sides, allowing thus economic interpretations. For each GDP component, bridge equations are specified by using a general‐to‐specific approach implemented in an automated way by Hoover and Perez and improved by Krolzig and Hendry. This approach allows to select explanatory variables among a large data set of hard and soft data. A rolling forecast study is carried out to assess the forecasting performance in the prediction of aggregated GDP, by taking publication lags into account in order to run pseudo real‐time forecasts. It turns out that the model outperforms benchmark models. The results show that changing the set of equations over the quarter is superior to keeping the same equations over time. In addition, GDP growth seems to be more precisely predicted from a supply‐side approach rather than a demand‐side approach.     

Economic policy uncertainty and the Chinese stock market volatility: new evidence

This study investigates the impacts of the economic policy uncertainty (EPU) indexes of China and the G7 countries on Chinese stock market volatility and further constructs a new diffusion index based on these indexes using principal component analysis (PCA) to achieve enhanced predictive ability. The in-sample results indicate that the EPU indexes of China and some of the G7 countries show a significantly negative impact on future volatility. Moreover, our constructed diffusion index also has a significantly negative impact. Furthermore, the out-of-sample results show that this diffusion index exhibits a significantly higher forecast accuracy than the EPU itself and combination forecasts. Finally, various robustness checks are consistent with our main conclusions. Overall, we construct a new and useful indicator that can substantially increase forecast accuracy with respect to the Chinese stock market.     

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.     

Effects of Eurobonds: A stochastic sovereign debt sustainability analysis for Portugal,Ireland and Greece

This paper assesses the impact of Eurobonds on sovereign debt dynamics for selected European member states (Greece, Ireland and Portugal). For each member state, we produce sovereign debt fan charts of (i) a baseline scenario (no Eurobonds) and (ii) a Full-Fledged Eurobond introduction. The key building blocks of our methodology are (i) a debt framework (which embeds the traditional recursive debt equation), (ii) a vector autoregressive model to take into account and parametrise macroeconomic uncertainty and (iii) a fiscal reaction function. Conditional on the absence of moral hazard, we find Eurobonds to be a good instrument to absorb macroeconomic shocks and to diminish uncertainty over future debt forecasts; for Ireland and Portugal, we find debt to be 20 percentage points lower than under our baseline scenario, by 2020.     

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.     

交通基础设施投资对中国经济增长影响的分析

1978—2012年中国国内生产总值(GDP)年均增长超过9%,被世界称为"中国奇迹",超前发展的基础设施是中国实现奇迹不容忽视的重要因素。利用1978—2011年数据研究交通基础设施投资对中国经济增长的作用,结果表明,交通基础设施与经济增长存在着长期稳定的均衡关系,基础设施投资经济增长存在正溢出效应。     

A two factor model to combine US inflation forecasts

The combination of individual forecasts is often a useful tool to improve forecast accuracy. The most commonly used technique for forecast combination is the mean, and it has frequently proved hard to surpass. This study considers factor analysis to combine US inflation forecasts showing that just one factor is not enough to beat the mean and that the second one is necessary. The first factor is usually a weighted mean of the variables and it can be interpreted as a consensus forecast, while the second factor generally provides the differences among the variables and, since the observations are forecasts, it may be related with the dispersion in forecasting expectations and, in a sense, with its uncertainty. Within this approach, the study also revisits Friedman's hypothesis relating the level of inflation with expectations uncertainty at the beginning of the twenty-first century.     

The exploration of economic crises: parameter uncertainty and predictive ability

Over a long horizon, this paper examines joint economic crises and determines the power of 49 variables in predicting such episodes. While incorporating dynamism in the prediction, we generate the predictive power of various specifications and model the uncertainty in the parameters of interest. The results reveal that growth of real gross domestic product per capita, regulation, bank non‐performing loans, interest rate and inflation rate are the most significant variables in predicting the joint economic crises. These variables predict economic crises with about 93% accuracy and can predict joint economic crises in developing countries and recent joint crises.     

国内生产总值的生态经济学剖析

国内生产总值(GDP)作为国民经济核算的主要方式,具有其重要性,同时也存在一定的问题,特别是在生态和环境方面。中国政府在进行十二五规划时,淡化了对GDP增长速度的要求,而强调宽容性增长,重视并着力解决民生问题,符合中国社会发展的趋势。但是GDP作为衡量国民经济发展的重要指标,今后仍将会发挥其重要作用,需要进行全面研究,并寻找改进的方式。因此,文章在文献综述的基础上,阐明了GDP产生的背景和内涵,指出了GDP在衡量社会经济发展中发挥的作用。同时,指出了GDP在衡量国家或地区社会经济综合发展方面的局限性,重点指出了生态和环境领域的问题和认识上的误区。最后,根据国内的研究情况,并结合我国具体国情,提出了一些改进和替代方面的建议。     

Terms of trade and economic growth in Japan and Korea: an empirical analysis

This study examines the impact of terms of trade and terms of trade volatility on economic growth in Japan and Korea using time series data. The results of the Johansen (1988) cointegration method show that real gross domestic product (GDP) per capita and terms of trade are jointly determined. Generally, an increase in terms of trade volatility will lead to a decrease in real GDP per capita. An increase in oil price will lead to a decrease in terms of trade. The results of the generalised forecast error variance decompositions show that the important contributors to real GDP per capita are different between Japan and Korea. A favourable and a less volatile terms of trade are important for economic growth.     

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.