Leading Indicator Project : Lithuania

The authors present a method for forecasting growth cycles in economic activity, measured as total industrial production. They construct a series which they aggregate into a composite leading indicator to predict the path of the economy in Lithuania. The cycle is the result of the economy's dev...

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Main Authors:	Everhart, Stephen S., Duval-Hernandez, Robert
Format:	Publications & Research
Language:	en_US
Published:	World Bank, Washington, DC 2015
Subjects:	growth models cyclical swings economic growth industrial production aggregate variability economic indicators growth patterns economic forecasts indexes of economic conditions financial indicators monetary indexes real variables business data processing statistical information industrialized societies transition economies benchmark burns carbon carbon dioxide carbon dioxide emissions central banks correlations economic activity economic development economic research emissions emissions taxes employment environmental policy exchange rate exercises forecasts forestry free trade growth rate interest rate interest rates joint implementation leading indicators macroeconomics methodology modeling multilateral trade private sector reliability research working papers survey data techniques time series time series analysis trade liberalization trough unemployment variability violence weighting welfare effects
Online Access:	http://hdl.handle.net/10986/21459

id	okr-10986-21459
recordtype	oai_dc
spelling	okr-10986-214592021-04-23T14:04:02Z Leading Indicator Project : Lithuania Everhart, Stephen S. Duval-Hernandez, Robert growth models cyclical swings economic growth industrial production aggregate variability economic indicators growth patterns economic forecasts indexes of economic conditions financial indicators monetary indexes real variables business data processing statistical information industrialized societies transition economies benchmark burns carbon carbon dioxide carbon dioxide emissions central banks correlations economic activity economic development economic research emissions emissions taxes employment environmental policy exchange rate exercises forecasts forestry free trade growth rate interest rate interest rates joint implementation leading indicators macroeconomics methodology modeling multilateral trade private sector reliability research working papers survey data techniques time series time series analysis trade liberalization trough unemployment variability violence weighting welfare effects The authors present a method for forecasting growth cycles in economic activity, measured as total industrial production. They construct a series which they aggregate into a composite leading indicator to predict the path of the economy in Lithuania. The cycle is the result of the economy's deviations from its long-term trend. A contractionary phase means a decline in the growth rate of the economy, not necessarily an absolute decline in economic activity. The indicator they select for economic activity is usually the Index of Industrial Production, plus a group of variables that, when filtered and adjusted, becomes the composite leading indicator that forecasts the reference series. Variables include economically, and statistically significant financial, monetary, real sector, and business survey data. They base selection of the components of the leading indicator on the forecast efficiency and economic significance of the series. Once selected, the relevant variables are aggregated into a single composite leading indicator, which forecasts the de-trended Index of Industrial Production. They apply the Hodrick-Prescott filter method for de-trending the series. This is a smoothing technique that decomposes seasonally adjusted series, into cyclical and trend components. One advantage of the Hodrick-Prescott filter is that it provides a reasonable estimate of a series' long-term trend. The OECD uses a system of leading indicators to predict growth cycles in the economies of its member countries. These exercises have been very effective in their forecasting ability and accuracy - but for the technique to work it is essential to have an adequate statistical system that provides many economic variables in a precise and timely manner, preferably monthly. The authors extend the OECD technique, and present an application to a country of the former Soviet Union. 2015-02-13T19:48:34Z 2015-02-13T19:48:34Z 2000-06 http://hdl.handle.net/10986/21459 en_US Policy Research Working Paper;No. 2365 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Europe and Central Asia Lithuania
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	en_US
topic	growth models cyclical swings economic growth industrial production aggregate variability economic indicators growth patterns economic forecasts indexes of economic conditions financial indicators monetary indexes real variables business data processing statistical information industrialized societies transition economies benchmark burns carbon carbon dioxide carbon dioxide emissions central banks correlations economic activity economic development economic research emissions emissions taxes employment environmental policy exchange rate exercises forecasts forestry free trade growth rate interest rate interest rates joint implementation leading indicators macroeconomics methodology modeling multilateral trade private sector reliability research working papers survey data techniques time series time series analysis trade liberalization trough unemployment variability violence weighting welfare effects
spellingShingle	growth models cyclical swings economic growth industrial production aggregate variability economic indicators growth patterns economic forecasts indexes of economic conditions financial indicators monetary indexes real variables business data processing statistical information industrialized societies transition economies benchmark burns carbon carbon dioxide carbon dioxide emissions central banks correlations economic activity economic development economic research emissions emissions taxes employment environmental policy exchange rate exercises forecasts forestry free trade growth rate interest rate interest rates joint implementation leading indicators macroeconomics methodology modeling multilateral trade private sector reliability research working papers survey data techniques time series time series analysis trade liberalization trough unemployment variability violence weighting welfare effects Everhart, Stephen S. Duval-Hernandez, Robert Leading Indicator Project : Lithuania
geographic_facet	Europe and Central Asia Lithuania
relation	Policy Research Working Paper;No. 2365
description	The authors present a method for forecasting growth cycles in economic activity, measured as total industrial production. They construct a series which they aggregate into a composite leading indicator to predict the path of the economy in Lithuania. The cycle is the result of the economy's deviations from its long-term trend. A contractionary phase means a decline in the growth rate of the economy, not necessarily an absolute decline in economic activity. The indicator they select for economic activity is usually the Index of Industrial Production, plus a group of variables that, when filtered and adjusted, becomes the composite leading indicator that forecasts the reference series. Variables include economically, and statistically significant financial, monetary, real sector, and business survey data. They base selection of the components of the leading indicator on the forecast efficiency and economic significance of the series. Once selected, the relevant variables are aggregated into a single composite leading indicator, which forecasts the de-trended Index of Industrial Production. They apply the Hodrick-Prescott filter method for de-trending the series. This is a smoothing technique that decomposes seasonally adjusted series, into cyclical and trend components. One advantage of the Hodrick-Prescott filter is that it provides a reasonable estimate of a series' long-term trend. The OECD uses a system of leading indicators to predict growth cycles in the economies of its member countries. These exercises have been very effective in their forecasting ability and accuracy - but for the technique to work it is essential to have an adequate statistical system that provides many economic variables in a precise and timely manner, preferably monthly. The authors extend the OECD technique, and present an application to a country of the former Soviet Union.
format	Publications & Research
author	Everhart, Stephen S. Duval-Hernandez, Robert
author_facet	Everhart, Stephen S. Duval-Hernandez, Robert
author_sort	Everhart, Stephen S.
title	Leading Indicator Project : Lithuania
title_short	Leading Indicator Project : Lithuania
title_full	Leading Indicator Project : Lithuania
title_fullStr	Leading Indicator Project : Lithuania
title_full_unstemmed	Leading Indicator Project : Lithuania
title_sort	leading indicator project : lithuania
publisher	World Bank, Washington, DC
publishDate	2015
url	http://hdl.handle.net/10986/21459
_version_	1764448323734863872

Leading Indicator Project : Lithuania

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