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Forcasting GDP using external data sources
Abstract
Describes how principal components analysis can be used to construct an alternative estimate of GDP.The Office for National Statistics (ONS) is the official provider of National Accounts data in the UK. However, business surveys and financial markets also provide a large number of other possible indicators of economic activity. This article outlines how ONS might use this “external“ data in compiling gross domestic product (GDP) estimates. A study of the literature suggests that these indicators may be useful predictors of economic activity, but their forecast performance in “real time“ is not proven. As a result ONS uses this data cautiously and only as an informal guide and a check on its own statistics. As there are a large number of potential indicators, this article describes how principal components analysis can be used to construct an alternative estimate of GDP which aims to summarise the external data “view of the world“ for comparison purposes. Economic & Labour Market Review (2007) 1, 18–23; doi:10.1057/palgrave.elmr.1410120
... Previous research has mainly focused on nowcasting current-quarter GDP growth. Small ([2,9]) or large ([4,6,12]) models are then used to " bridge " the information contained in monthly data with the quarterly growth rate of GDP. One exception is the research of [3,5] who describe how short term interest rates and business survey data can be used to produce nowcasts of the monthly industrial production index for the aggregated euro area. ...
... The empirical results of [5] indicate however that the nowcasting method that exploits the information in the survey results outperforms the simple autoregressive models. Regarding the practical application of nowcasting methods in statistical offices, [4] mentions that the Office for National Statistics (ONS) of the United Kingdom takes a conservative approach to 3 using external data sources such as business surveys and financial market variables in compiling its statistics. He notes that: " As a National Statistics institution, ONS has an obligation to meet international standards on the formulation of National Accounts, and produce estimates in a transparent way so that users can be confident that quality benchmarks are being maintained. ...
Manufacturing Value Added (MVA) is the key indicator of a country's industrial production. In order to facilitate international comparisons it is published in UNIDO's International Yearbook of Industrial Statistics for a large set of countries. Because of a time-gap of at least one year between the latest year for which data are available and the year for which MVA data must be reported in the Yearbook, nowcasting methods are used to fill in the missing data up to the current year. We propose a methodology that exploits the relationship between MVA and GDP and produces reliable nowcasts of MVA based on figures of GDP growth rates available from external sources.
... For many OECD countries, indicators based on business surveys became a standard tool for the analysis and forecasting of business cycles and for assessing models of expectation formation. Recent contributions and reviews on this literature include Chamberlin (2007), Claveria et al. (2007) and Goggin (2008). These indicators are also important as information on the current status of the economy given the publication delay of data on real production such as GDP or -with a smaller time lag -industrial production (Hüfner and Schröder 2002, p 317). ...
Business tendency survey indicators are widely recognised as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full-specified VAR models with subset models obtained using a Genetic Algorithm enabling “holes” in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both countries revealing marked differences between Russia and Germany. JEL classification: C52, C61, E37 Keywords: Leading indicators, business cycle forecasts, VAR, model selection, genetic algorithms
... For many OECD countries, indicators based on business surveys became a standard tool for the analysis and forecasting of business cycles and for assessing models of expectation formation. Recent contributions and reviews on this literature include Chamberlin (2007), Claveria et al. (2007) and Goggin (2008). These indicators are also important as information on the current status of the economy given the publication delay of data on real production such as GDP or -with a smaller time lag -industrial production (Hüfner and Schröder 2002, p 317). ...
Business tendency survey indicators are widely recognized as a key instrument for business cycle forecasting. Their leading indicator property is assessed with regard to forecasting industrial production in Russia and Germany. For this purpose, vector autoregressive (VAR) models are specified and estimated to construct forecasts. As the potential number of lags included is large, we compare full–specified VAR models with subset models obtained using a Genetic Algorithm enabling ’holes’ in multivariate lag structures. The problem is complicated by the fact that a structural break and seasonal variation of indicators have to be taken into account. The models allow for a comparison of the dynamic adjustment and the forecasting performance of the leading indicators for both
... The power of the PC approach is greatest when the indicator set is very large. A recent article by this author (Chamberlin 2007) showed that a set of over 400 business survey and financial markets indicators could adequately be described by eight PCs. The approach is also very good at isolating sources of idiosyncratic movements and potential outliers, as these are often identified as individual PCs and can therefore be discarded. ...
Presents new data for the New labour productivity measures from the ABI, calculated using published ABI data, updating previousresults for 1998 to 2000.This article presents new current price labour productivity data calculated using published data from the Annual BusinessInquiry (ABI). This is an update of previous work by Daffin and Lau (2002) where results for 1998 to 2000 were provided.The results in this article contain revised data for this period1 and new data for 2001 to 2005. The rest of the articlediscusses the results, the quality of the estimates and also industry-specific data issues. Economic & Labour Market Review (2007) [1], 25–39; doi:10.1057/palgrave.elmr.1410137
... The power of the PC approach is greatest when the indicator set is very large. A recent article by this author (Chamberlin 2007) showed that a set of over 400 business survey and financial markets indicators could adequately be described by eight PCs. The approach is also very good at isolating sources of idiosyncratic movements and potential outliers, as these are often identified as individual PCs and can therefore be discarded. ...
Provides an overview of the challenges faced by ONS in the ongoing process of social andeconomic integration.This article provides an overview of the challenges faced by the Office for National Statistics (ONS) to meet policy and wider evidence needs on the ongoing process of social and economic integration. Globalisation has been singled out by the UK Government as oneof the main challenges and opportunities facing the country. Migration is the main work priority for ONS in this area. Other crucial challenges are posed by new forms of “weightless“ trade, increasing specialisation in internationally engaged firms, greater importance of intangibleinvestment, unprecedented financial integration and the wide range of social and economic impacts on the population. This article documents the progress made towards meeting these challenges, and concludes that increased, and more focused, interdepartmental and international co-operation is needed. Italso invites external contributions to the ongoing consultation on the future ONS work programme. Economic & Labour Market Review (2007) 1, 18–24; doi:10.1057/palgrave.elmr.1410136
The article was devoted to the research of possibilities to use Business Outlook Survey results, which are carried out by National Bank of Ukraine, for the short-term forecasting of economic development, in particular, the Gross Domestic Product of Ukraine. The different methods of building of the leading index of economic development, their advantages, and their restrictions are examined. The choice of the best index, which provides for the higher accuracy of forecasting the GDP, is carried out with the use of econometric models.
The paper deals with principal component analysis and data whitening. The research is done in the area of main economic indicators. This means the data preprocessing problem. The main aim of this paper is to present and discuss the possible ways of data preprocessing. The paper deals with four main approaches. There are compared the results from raw data, absolute differences, relative differences and logarithmic differences. The classic principal component analysis is also used with some improvement, there is described the basement of data whitening. The main aim is to get the good data visualisation. The next aim of such approach can be to identify the similarities between some states and their main trends. For this reason there is presented the comparison of states of Visegrad Group. At this moment there is no aim to deeply discuss the reasons of development in detail. This paper suggests new point of view to time series connected to economic development. The deep analysis of all relationships is the topic for further research.
We investigate the usefulness of the European Commission confidence indicators for forecasting real GDP growth rates in the short run is investigated in selected euro area countries (Belgium, Spain, Germany, France, Italy and the Netherlands) which account for almost 90% of the euro area. A linear relationship between real GDP and confidence indicators is estimated and the forecasting performance of the estimated models compared with a benchmark ARIMA model. It is generally found that confidence indicators can be useful for forecasting real GDP growth rates in the short-run in most of the above-mentioned countries. Notwithstanding some signs of instability in the relation between confidence indicators and real GDP, improvements with the use of time-varying parameter models appear to be fairly limited but confirm the findings obtained with constant parameter techniques. The results obtained are robust to a wide range of variant tests implemented.
Could a researcher or policy analyst use data reported from surveys of consumer confidence to improve forecasts of consumer spending? This issue has been examined in the literature previously, with the conclusion that consumer-confidence data helped improve the forecasts slightly, but not statistically significantly. That research, however, was based on latest-available data rather than information that would have been available to forecasters in real time. This paper remedies that shortcoming, using the Real-Time Data Set for Macroeconomists (RTDSM) to analyze the quality of forecasts made with indexes of consumer confidence. We conjecture that using real-time data might show greater marginal significance for consumer confidence, because the surveys might capture effects that will not appear in the data until they are revised. The main finding is that the indexes of consumer confidence are not of significant value in forecasting consumer spending. In fact, in some cases, they make the forecasts significantly worse, suggesting that consumer-confidence surveys are no better than government data agencies in capturing information about consumer spending.
In the construction of a leading indicator model of economic activity, economists must select among a pool of variables which lead output growth. Usually the pool of variables is large, and selection of a subset must be carried out. In this paper we propose an `Automatic Leading Indicator' model. Rather than preselection, we use a dynamic factor model to summarise the information content of a pool of variables. Results show that the forecasting performance of our `Automatic Leading Indicator' model is signi#cantly better than that of traditional model selection criteria with VAR models. This study is carried out using quaterly data for France, Germany, Italy and the United Kingdom. KEYWORDS: Forecasting, State Space Models, Time Series. 1 Introduction The number of variables that can be used when forecasting output growth with a VAR model is limited. Given typical sample periods available in applied macroeconomics, forecasters must preselect a subset from a pool of variables...