In this issue we have: Budgetary Forecasting in India: Partitioning Errors and Testing for Rational Expectations ; Exchange Rate Forecasting: Evidence from the Emerging Central and Eastern European Economies ; On the accuracy of judgmental interventions on Statistical Forecasts and more.

Date: | 2008-01 |

By: | Chakraborty, Lekha S Sinha, Darshy |

URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:7538&r=for |

According to the theory of efficient markets, economic agents use all available information to form rational expectations. Fiscal marksmanship, the accuracy of budgetary forecasting, can be one important piece of such information the rational agents must consider in forming expectations. Using Theil's inequality coefficient (U) based on the mean square prediction error, the paper estimates the magnitude of errors in the budgetary forecasts in India for the period 1990-91 to 2003-04 and also decomposed the errors into biasedness, unequal variation and random components to analyze the source of error. The test of rational expectations revealed that neither revenue nor expenditure forecasts in India is rational. However, capital budget revealed more forecast errors than revenue budget. The results also revealed that degree of errors in forecasting of receipts was relatively higher than that of expenditure. However ! there is no specific trend in the forecasting errors, which reveals that budgetary estimates are made not based on adaptive expectations. The proportion of error due to random variation has been significantly higher (which is beyond the control of the forecaster), while the errors due to bias has been negligible. The analysis related to efficiency of forecasts also showed that no significant improvement in forecasts over time. | |

Keywords: | fiscal marksmanship; Theils' inequality coefficient; rational expectations; budgetary forecast errors |

JEL: | E62 H68 |

Date: | 2008-03-06 |

By: | Ardic, Oya Pinar Ergin, Onur Senol, G. Bahar |

URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:7505&r=for |

There is a vast literature on exchange rate forecasting focusing on developed economies. Since the early 1990s, many developing economies have liberalized their financial accounts, and become an integral part of the international financial system. A series of financial crises experienced by these emerging market economies ed them to switch to some form of a flexible exchange rate regime, coupled with inflation targeting. These developments, in turn, accentuate the need for exchange rate forecasting in such economies. This paper is a first attempt to compile data from the emerging Central and Eastern European (CEE) economies, to evaluate the performance of versions of the monetary model of exchange rate determination, and time series models for forecasting exchange rates. Forecast performance of these models at various horizons are evaluated against that of a random walk, which, overwhelmingly, was found to be the ! best exchange rate predictor for developed economies in the previous literature. Following Clark and West (2006, 2007) for forecast performance analysis, we report that in short horizons, structural models and time series models outperform the random walk for the six CEE countries in the data set. | |

Keywords: | Exchange rate forecasting; Out-of-sample forecast performance |

JEL: | C53 F31 |

Date: | 2008 |

By: | Konstantinos Nikolopoulos |

URL: | http://d.repec.org/n?u=RePEc:uop:wpaper:0021&r=for |

Forecasting at the Stock Keeping Unit (SKU) level in order to support operations management has proved a very difficult task. The levels of accuracy achieved have major consequences for companies at all levels in the supply chain; errors at each stage are amplified resulting in poor service and overly high inventory levels. In most companies, the size and complexity of the forecasting task necessitates the use of Forecasting Support Systems (FSS). The present study examines monthly demand data and forecasts for 753 fast moving SKUs, collected from three major U.K. suppliers. The companies rely upon FSSs to obtain baseline forecasts per SKU for each period. Final forecasts are produced at a later stage through the superimposition of judgments based on marketing intelligence gathered by the companies' forecasters. The benefits of the intervention are evaluated by comparing the actual sales both to system and final! forecasts as well as two simple benchmarks. The findings support that adjustments do improve accuracy with an overall gain of 3.72 (MdAPE) that is a percentage improvement of 18.9% (final forecasts being better in 55% of the cases). The accuracy gain differentiates according the size and the direction of the adjustments, and the level of noise in the series; two interesting findings: a) in almost one out of three cases the forecasters got the direction of the adjustment wrong, and b) small adjustments – less than 10% – should be avoided! | |

Keywords: | Statistical Forecasts; Judgmental Interventions; Stock Keeping Units. |

Date: | 2007 |

By: | Marcellino, Massimiliano Schumacher, Christian |

URL: | http://d.repec.org/n?u=RePEc:zbw:bubdp1:7034&r=for |

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the 'ragged edge' of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the 'nowcast', using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections wit! h respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data. | |

Keywords: | MIDAS, large factor models, nowcasting, mixed-frequency data, missing values |

JEL: | C53 E37 |

Date: | 2008-03-06 |

By: | Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I) Cyril Caillault (FORTIS Investments – Fortis investments) |

URL: | http://d.repec.org/n?u=RePEc:hal:papers:halshs-00185374_v1&r=for |

Using non-parametric (copulas) and parametric models, we show that the bivariate distribution of an Asian portfolio is not stable along all the period under study. We suggest several dynamic models to compute two market risk measures, the Value at Risk and the Expected Shortfall: the RiskMetric methodology, the Multivariate GARCH models, the Multivariate Markov-Switching models, the empirical histogram and the dynamic copulas. We discuss the choice of the best method with respect to the policy management of bank supervisors. The copula approach seems to be a good compromise between all these models. It permits taking financial crises into account and obtaining a low capital requirement during the most important crises. | |

Keywords: | Value at Risk – Expected Shortfall – Copula – RiskMetrics – Risk management -<br />GARCH models – Switching models. |

Date: | 2008-03-06 |

By: | Gilles Dufrenot (GREQAM – Groupement de Recherche en Économie Quantitative d'Aix-Marseille – Université de la Méditerranée – Aix-Marseille II – Université Paul Cézanne – Aix-Marseille III – Ecole des Hautes Etudes en Sciences Sociales – CNRS : UMR6579) Dominique Guegan (CES – Centre d'économie de la Sorbonne – CNRS : UMR8174 – Université Panthéon-Sorbonne – Paris I) Anne Peguin-Feissolle (GREQAM – Groupement de Recherche en Économie Quantitative d'Aix-Marseille – Université de la Méditerranée – Aix-Marseille II – Université Paul Cézanne – Aix-Marseille III – Ecole des Hautes Etudes en Sciences Sociales – CNRS : UMR6579) |

URL: | http://d.repec.org/n?u=RePEc:hal:papers:halshs-00185369_v1&r=for |

This paper presents a 2-regime SETAR model with different long-memory processes in both regimes. We briefly present the memory properties of this model and propose an estimation method. Such a process is applied to the absolute and squared returns of five stock indices. A comparison with simple FARIMA models is made using some forecastibility criteria. Our empirical results suggest that our model offers an interesting alternative competing framework to describe the persistent dynamics in modeling the returns. | |

Keywords: | SETAR – Long-memory – Stock indices – Forecasting |

Taken from the NEP-FOR mailing list edited by Rob Hyndman.