|
on Forecasting |
| By: | Skriner, Edith (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria) |
| Abstract: | The theory suggests that investment activities and monetary policy influence the development of the global business cycle. The oil price and other raw material prices also play a key role in the economic development and there is a co-movement among oil consumption and global output. Therefore, the aim of this study is to explain the development of this set of variables by ARs, small-scale VARs and ECMs. The lag length and the rank of the time series models have been determined using information criteria. Then one-step ahead forecasts have been generated. It was found, that the ARs generate the best forecasts at the beginning of the forecasting horizon. However, when the forecasting horizon increases the VARs outperform the ARs. Comparing the forecasting performance of the ECMs, it was found that the forecasting ability of the ECMs in first differences outperform the level based ECMs when the forecasting horizon increases. |
| Keywords: | International economics, time series models, forecasts, forecast evaluation |
| JEL: | F17 C22 C5 |
| Date: | 2007–07 |
| URL: | http://d.repec.org/n?u=RePEc:ihs:ihsesp:214&r=for |
| By: | Paul SÃÂöderlind |
| Abstract: | The out-of-sample forecasting performance of traditional stock return models (dividend yield, t-bill rate, etc.) is compared with the forecasting performance of the Livingston survey. The results suggest that the survey forecasts are much like a "too large" forecasting model: poor performance and too sensitive to irrelevant information. |
| Keywords: | Livingston survey, out-of-sample forecasts |
| JEL: | G12 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:usg:dp2007:2007-23&r=for |
| By: | Francesco Audrino; Fabio Trojani |
| Abstract: | We propose a multivariate nonparametric technique for generating reliable shortterm historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest rate series. It is computationally feasible in large dimensions and it can account for non-linearities in the dependence of interest rates at all available maturities. Based on FGD we apply filtered historical simulation to compute reliable out-of-sample yield curve scenarios and confidence intervals. We back-test our methodology on daily USD bond data for forecasting horizons from 1 to 10 days. Based on several statistical performance measures we find significant evidence of a higher predictive power of our method when compared to scenarios generating techniques based on (i) factor analysis, (ii) a multivariate CCC-GARCH model, or (iii) an exponential smoothing covariances estimator as in the RiskMetricsTM approach. |
| Keywords: | Conditional mean and variance estimation, Filtered Historical Simulation, Functional Gradient Descent, Term structure; Multivariate CCC-GARCH models |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:usg:dp2007:2007-24&r=for |
| By: | Gregor W. Smith (Department of Economics, Queen's University) |
| Abstract: | Estimating linear rational expectations models requires replacing the expectations of future, endogenous variables either with forecasts from a fully solved model, or with the instrumented actual values, or with forecast survey data. Extending the methods of McCallum (1976) and Gottfries and Persson (1988), I show how to pool these methods and also use actual, future values of these variables to improve statistical efficiency. The method is illustrated with an application using SPF survey data in the US Phillips curve, where the output gap plays a significant role but lagged inflation plays none. |
| Keywords: | rational expectations, recursive projection, Phillips curve |
| JEL: | E37 C53 |
| Date: | 2007–06 |
| URL: | http://d.repec.org/n?u=RePEc:qed:wpaper:1129&r=for |
| By: | Francesco Audrino; Fabio Trojani |
| Abstract: | We propose a new multivariate GARCH model with Dynamic Conditional Correlations that extends previous models by admitting multivariate thresholds in conditional volatilities and correlations. The model estimation is feasible in large dimensions and the positive deniteness of the conditional covariance matrix is easily ensured by the structure of the model. Thresholds in conditional volatilities and correlations are estimated from the data, together with all other model parameters. We study the performance of our model in three distinct applications to US stock and bond market data. Even if the conditional volatility functions of stock returns exhibit pronounced GARCH and threshold features, their conditional correlation dynamics depends on a very simple threshold structure with no local GARCH features. We obtain a similar result for the conditional correlations between government and corporate bond returns. On the contrary, we ÃÂïnd both threshold and GARCH structures in the conditional correlations between stock and government bond returns. In all applications, our model improves signiÃÂïcantly the in-sample and out-of-sample forecasting power for future conditional correlations with respect to other relevant multivariate GARCH models. |
| Keywords: | Multivariate GARCH models, Dynamic conditional correlations, Tree-structured GARCH models |
| JEL: | C12 C13 C51 C53 C61 |
| Date: | 2007–04 |
| URL: | http://d.repec.org/n?u=RePEc:usg:dp2007:2007-25&r=for |
| By: | Eliashberg, J.; Hegie, Q.; Ho, J.; Huisman, D.; Miller, S.J.; Swami, S.; Weinberg, C.B.; Wierenga, B. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University) |
| Abstract: | This paper describes a model that generates weekly movie schedules in a multiplex movie theater. A movie schedule specifies within each day of the week, on which screen(s) different movies will be played, and at which time(s). The model consists of two parts: (i) conditional forecasts of the number of visitors per show for any possible starting time; and (ii) an optimization procedure that quickly finds an almost optimal schedule (which can be demonstrated to be close to the optimal schedule). To generate this schedule we formulate the so-called movie scheduling problem as a generalized set partitioning problem. The latter is solved with an algorithm based on column generation techniques. We have applied this combined demand forecasting /schedule optimization procedure to a multiplex in Amsterdam where we supported the scheduling of fourteen movie weeks. The proposed model not 2 only makes movie scheduling easier and less time consuming, but also generates schedules that would attract more visitors than the current ?intuition-based? schedules. |
| Keywords: | Optimization of movie schedules;Integer programming;Column generation;Demand forecasting; |
| Date: | 2007–05–10 |
| URL: | http://d.repec.org/n?u=RePEc:dgr:eureri:300011308&r=for |