30,345 research outputs found

    On the performance of US fiscal forecasts : government vs. private information

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    This paper contributes to shed light on the quality and performance of US fiscal forecasts. The first part inspects the causes of official (CBO) fiscal forecasts revisions between 1984 and 2016 that are due to technical, economic or policy reasons. Both individual and cumulative means of forecast errors are relatively close to zero, particularly in the case of expenditures. CBO averages indicate net average downward revenue and expenditure revisions and net average upward deficit revisions. Focusing on the causes of the technical component, we uncover that its revisions are quite unpredictable which casts doubts on inferences about fiscal policy sustainability that rely on point estimates. Comparing official with private-sector (Consensus) forecasts, despite the informational advantages CBO might have, one cannot unequivocally say that one or the other is more accurate. Evidence also seems to suggest that CBO forecasts are consistently heavily biased towards optimism while this is less the case for Consensus forecasts. Not only is the extent of information rigidity is more prevalent in CBO forecasts, but evidence also seems to indicate that Consensus forecasts dominate CBO’s in terms of information content.info:eu-repo/semantics/publishedVersio

    The IFS Green Budget: January 2007

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    Managing Uncertainty: How State Budgeting Can Smooth Revenue Volatility

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    This is the first in a series of reports by The Pew Charitable Trusts offering policymakers strategies that improve long-term fiscal health and manage budget uncertainty. In this report, Pew researchers examine patterns in revenue volatility across the 50 states between 1994 and 2012. The report examines the factors that drive volatility, including state-specific patterns of economic growth and contraction and their interaction with state taxes, and recommends the best ways to respond to these conditions. Future research will explore in greater detail how states can use fiscal management tools such as rainy day funds and revenue forecasting to better manage their finances over the course of the business cycle.Pew's research methods in reaching the conclusions and findings are discussed in detail at the end of this report. The team analyzed the relationships between state-specific economic data from the Federal Reserve Bank of Philadelphia and state revenue data compiled by the Rockefeller Institute of Government for all 50 states, and interviewed key fiscal policymakers and independent analysts in 15 states

    FORECASTING OF THE STOCK RATE OF LEADING WORLD COMPANIES USING ECONOMETRIC METHODS AND DCF ANALYSIS

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    In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods.In this article, we will cover various models for forecasting the stock price of global companies, namely the DCF model, with well-reasoned financial analysis and the ARIMA model, an integrated model of autoregression − moving average, as an econometric mechanism for point and interval forecasting. The main goal is to compare the obtained forecasting results and evaluate their real accuracy. The article is based on forecasting stock prices of two companies: Coca-Cola HBC AG (CCHGY) and Nestle S.A. (NSRGF). At the moment, it is not determined which approach is better for predicting the stock price − the analysis of financial indicators or the use of econometric data analysis methods
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