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Author: Hui Zhu
Publisher:
ISBN:
Category :
Languages : en
Pages : 226
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Book Description
Forecasting firms' earnings has long been an interest of market participants and academics. Traditional forecasting studies in a multivariate time series setting do not take into account that the timing of market data release for a specific time period of observation is often spread over several days or weeks. This thesis focuses on the separation of announcement timing or data release and the use of econometric real-time methods, which we refer to as an updated vector autoregression (VAR) forecast, to predict data that have yet to be released. In comparison to standard time series forecasting, we show that the updated forecasts will be more accurate the higher the correlation coefficients among the standard VAR innovations are. Forecasting with the sequential release of information has not been studied in the VAR framework, and our approach to U.S. nonfarm payroll employment and the six Canadian banks shows its value. By using the updated VAR forecast, we conclude that there are relative efficiency gains in the one-step-ahead forecast compared to the ordinary VAR forecast, and compared to professional consensus forecasts. Thought experiments emphasize that the release ordering is crucial in determining forecast accuracy.
Author: Hui Zhu
Publisher:
ISBN:
Category :
Languages : en
Pages : 226
Get Book Here
Book Description
Forecasting firms' earnings has long been an interest of market participants and academics. Traditional forecasting studies in a multivariate time series setting do not take into account that the timing of market data release for a specific time period of observation is often spread over several days or weeks. This thesis focuses on the separation of announcement timing or data release and the use of econometric real-time methods, which we refer to as an updated vector autoregression (VAR) forecast, to predict data that have yet to be released. In comparison to standard time series forecasting, we show that the updated forecasts will be more accurate the higher the correlation coefficients among the standard VAR innovations are. Forecasting with the sequential release of information has not been studied in the VAR framework, and our approach to U.S. nonfarm payroll employment and the six Canadian banks shows its value. By using the updated VAR forecast, we conclude that there are relative efficiency gains in the one-step-ahead forecast compared to the ordinary VAR forecast, and compared to professional consensus forecasts. Thought experiments emphasize that the release ordering is crucial in determining forecast accuracy.
Author: Hui Zhu
Publisher:
ISBN:
Category :
Languages : en
Pages : 53
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Book Description
Forecasting firms' earnings has long been an interest of market participants and academics. Traditional forecasting studies in a multivariate time series setting do not take into account that the timing of data release for a specific time period of observation is often spread over several days or weeks. This paper focuses on the separation of announcement timing or data release and the use of econometric real-time methods, which we refer to as an updated vector autoregression (VAR) forecast, to predict data that have yet to be released. In comparison to standard time series forecasting, we show that the updated forecasts will be more accurate the higher the correlation coefficients among the standard VAR innovations are. Forecasting with the sequential release of information has not been studied in the VAR framework, and our approach to the six Canadian banks shows its value. By using the updated VAR forecast, we find that the relative efficiency gain is 33% in the one-step-ahead forecast compared to the ordinary VAR forecast, and 7% compared to professional consensus forecasts. Thought experiments suggest that if banks' order of information release were to change, forecast errors could be substantially reduced. These experiments emphasize that evaluating the release ordering is crucial in determining forecast accuracy.
Author: Theodore M. Crone
Publisher:
ISBN:
Category : Delaware
Languages : en
Pages : 30
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Book Description
Author: Dominik Bertsche
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Scott T. Murdoch
Publisher:
ISBN:
Category : Economic forecasting
Languages : en
Pages :
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Author: Robin Braun
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Editors : Raj Kapila & Uma Kapila
Publisher: Academic Foundation
ISBN: 9788171883547
Category :
Languages : en
Pages : 332
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Book Description
Author: David E. Rapach
Publisher: Emerald Group Publishing
ISBN: 044452942X
Category : Business & Economics
Languages : en
Pages : 691
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Book Description
Forecasting in the presence of structural breaks and model uncertainty are active areas of research with implications for practical problems in forecasting. This book addresses forecasting variables from both Macroeconomics and Finance, and considers various methods of dealing with model instability and model uncertainty when forming forecasts.
Author:
Publisher:
ISBN:
Category : Budget
Languages : en
Pages : 116
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Book Description
Author: Osman Kubilay Gursel
Publisher:
ISBN:
Category :
Languages : en
Pages : 218
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Book Description
In the first chapter the performance of two of the long memory tests, the Modified Rescaled Range Test and Geweke and Porter-Hudak Test for persistence in small samples is examined using Monte-Carlo methods. Some possible candidates for persistence in volatility are Autoregressive Conditional Heteroskedasticity (ARCH), Markov Regime Switching ARCH, and long memory. The long memory series are simulated through a Semi-Markov process with Pareto waiting times and lognormal realizations. The persistence in volatility arising from transition waiting probabilities for a Markov Regime Switching process, and from the tail index of the waiting time distribution for the Semi-Markov process is established through simulations with different parameter values. There is evidence that persistence in a regime switching process is closely linked to state transition probabilities and waiting times. The second chapter re-examines what structural vector autoregressive modeling of real exchange rates with differenced variables tells us about interesting macroeconomic questions. Using quarterly data from G-7 countries in the post Bretton-Woods period, the evidence suggests that shock identification is not an easy process in a Blanchard and Quah decomposition framework with long run restrictions. Confidence bands do not find significant impulse responses and the signs of the estimated impulse responses are very sensitive to the lag selection criteria adopted. Possible cointegration effects seem to be the main driving force behind the unsatisfactory performance of the structural approach. Chapter three extends the structural vector autoregression model by incorporating cointegration effects. Using the method of Warne (1993), in a simple four-variable vector autoregression (VAR) characterized by cointegration, the response of real exchange rates to various economic shocks are investigated with economically plausible long-run restrictions. The long-run relations and driving stochastic trends of the real exchange rate between United States and other G-7 countries are analyzed in a structural cointegrated framework. Productivity shocks depreciate the real exchange rate and the perverse sign effect of supply shock is corrected for most countries in the sample. More significant impulse responses are observed through confidence intervals. The structural vector error correction decompositions are also found to be not robust to estimating with different lag lengths owing to additional cointegration effects.
