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on Forecasting |
| By: | Knüppel, Malte; Krüger, Fabian |
| Abstract: | In many empirical applications, a combined density forecast is constructed using the linear pool which aggregates several individual density forecasts. We analyze the linear pool in a mean/variance prediction space setup. Our theoretical results indicate that a well-known 'disagreement' term can be detrimental to the linear pool's assessment of forecast uncertainty. We demonstrate this argument in macroeconomic and financial forecasting case studies. |
| JEL: | C53 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc17:168294&r=for |
| By: | Afees A. Salisu (Centre for Econometric and Allied Research, University of Ibadan); Ibrahim D. Raheem (School of Economics, University of Kent, Canterbury, UK); Umar B. Ndako (Monetary Policy Department, Central Bank of Nigeria, Nigeria.) |
| Abstract: | This paper revisits the stock-oil price nexus. The extant literature has shown that the nexus can be situated around the multifactor asset pricing models to accentuate the role of oil price risks in stock valuation but with mixed findings. However, attempts to improve on previous studies in this pursuit led researchers to account for nonlinearities in the relationship to assess the asymmetric response of stock prices to positive and negative oil price changes. Consequently, we fit a predictive model for stock price that accounts for asymmetry on the basis of the predictive power of oil price shocks. Innovatively, we advance arguments for considering the importance of persistence, endogeneity and conditional heteroscedasticity inherent in the relationship and the data generating process for the in-sample and out-of-sample forecasting of US sectoral stock prices. Our results emphasise the role of oil price shocks and its asymmetric impacts in the in-sample predictability model of the sectoral stock prices. This evidence is also consistent for out-of-sample forecast evaluation and robust to changes in the measure of oil prices. |
| Keywords: | Sectoral stock prices, Oil Prices, The U.S. Asymmetry, Persistence, endogeneity and conditional heteroscedasticity |
| Date: | 2017–10 |
| URL: | http://d.repec.org/n?u=RePEc:cui:wpaper:0033&r=for |
| By: | Bent Flyvbjerg; Chi-keung Hon; Wing Huen Fok |
| Abstract: | Reference class forecasting is a method to remove optimism bias and strategic misrepresentation in infrastructure projects and programmes. In 2012 the Hong Kong government's Development Bureau commissioned a feasibility study on reference class forecasting in Hong Kong - a first for the Asia-Pacific region. This study involved 25 roadwork projects, for which forecast costs and durations were compared with actual outcomes. The analysis established and verified the statistical distribution of the forecast accuracy at various stages of project development, and benchmarked the projects against a sample of 863 similar projects. The study contributed to the understanding of how to improve forecasts by de-biasing early estimates, explicitly considering the risk appetite of decision makers, and safeguarding public funding allocation by balancing exceedance and under-use of project budgets. |
| Date: | 2017–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1710.09419&r=for |
| By: | Conrad, Christian |
| Abstract: | We show that the consensus forecast can be biased if some forecasters minimize an asymmetric loss function and the DGP features conditional heteroscedasticity. The time-varying bias depends on the variance of the process. As a consequence, the information from the ex-ante variation of forecasts can be used to improve the predictive accuracy of the combined forecast. Forecast survey data from the Euro area and the U.S. confirm the implications of the theoretical model. |
| JEL: | C51 C53 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc17:168200&r=for |
| By: | Antoine A. Djogbenou (Queen's University) |
| Abstract: | This paper proposes two consistent model selection procedures for factor-augmented regressions in finite samples. We first demonstrate that the usual cross-validation is inconsistent, but that a generalization, leave-d-out cross-validation, selects the smallest basis for the space spanned by the true factors. The second proposed criterion is a generalization of the bootstrap approximation of the squared error of prediction of Shao (1996) to factor-augmented regressions. We show that this procedure is consistent. Simulation evidence documents improvements in the probability of selecting the smallest set of estimated factors than the usually available methods. An illustrative empirical application that analyzes the relationship between expected stock returns and factors extracted from a large panel of United States macroeconomic and financial data is conducted. Our new procedures select factors that correlate heavily with interest rate spreads and with the Fama-French factors. These factors have strong predictive power for excess returns. |
| Keywords: | factor model, consistent model selection, cross-validation, bootstrap, excess returns, macroeconomic and financial factors |
| JEL: | C52 C53 |
| Date: | 2017–10 |
| URL: | http://d.repec.org/n?u=RePEc:qed:wpaper:1391&r=for |