104 research outputs found
25 Years of IIF Time Series Forecasting: A Selective Review
We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.Accuracy measures; ARCH model; ARIMA model; Combining; Count data; Densities; Exponential smoothing; Kalman Filter; Long memory; Multivariate; Neural nets; Nonlinearity; Prediction intervals; Regime switching models; Robustness; Seasonality; State space; Structural models; Transfer function; Univariate; VAR.
Efficient Estimation of an Additive Quantile Regression Model
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). With the aim to reduce variance of the first estimator, a second estimator is defined via sequential fitting of univariate local polynomial quantile smoothing for each additive component with the other additive components replaced by the corresponding estimates from the first estimator. The second estimator achieves oracle efficiency in the sense that each estimated additive component has the same variance as in the case when all other additive components were known. Asymptotic properties are derived for both estimators under dependent processes that are strictly stationary and absolutely regular. We also provide a demonstrative empirical application of additive quantile models to ambulance travel times.Additive models; Asymptotic properties; Dependent data; Internalized kernel smoothing; Local polynomial; Oracle efficiency
Estimating Generalized Additive Conditional Quantiles for Absolutely Regular Processes
We propose a nonparametric method for estimating the conditional quantile
function that admits a generalized additive specification with an unknown link
function. This model nests single-index, additive, and multiplicative quantile
regression models. Based on a full local linear polynomial expansion, we first
obtain the asymptotic representation for the proposed quantile estimator for
each additive component. Then, the link function is estimated by noting that it
corresponds to the conditional quantile function of a response variable given
the sum of all additive components. The observations are supposed to be a
sample from a strictly stationary and absolutely regular process. We provide
results on (uniform) consistency rates, second order asymptotic expansions and
point wise asymptotic normality of each proposed estimator
A multi-step kernelâbased regression estimator that adapts to error distributions of unknown form
For linear regression models, we propose and study a multi-step kernel density-based estimator that is adaptive to unknown error distributions. We establish asymptotic normality and almost sure convergence. An efficient EM algorithm is provided to implement the proposed estimator. We also compare its finite sample performance with five other adaptive estimators in an extensive Monte Carlo study of eight error distributions. Our method generally attains high mean-square-error efficiency. An empirical example illustrates the gain in efficiency of the new adaptive method when making statistical inference about the slope parameters in three linear regressions
Information Flows around the Globe: Predicting Opening Gaps from Overnight Foreign Stock Price Patterns
This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on price patterns from foreign markets that have become available overnight. As the close-to-open gap is a scalar response variable to a functional variable, it is natural to focus on functional data analysis. Both parametric and non-parametric modeling strategies are considered, and compared with a simple linear benchmark model. The overall best performing model is nonparametric, suggesting the presence of nonlinear relations between the overnight price patterns and the opening gaps. This effect is mainly due to the European and Asian markets. The North-American and Australian markets appear to be informationally more efficient in that linear models using only the last available information perform well
Efficient Estimation of an Additive Quantile Regression Model
In this paper two kernel-based nonparametric estimators are proposed for estimating the components
of an additive quantile regression model. The first estimator is a computationally convenient approach
which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). With the aim
to reduce variance of the first estimator, a second estimator is defined via sequential fitting of univariate
local polynomial quantile smoothing for each additive component with the other additive components
replaced by the corresponding estimates from the first estimator. The second estimator achieves oracle
efficiency in the sense that each estimated additive component has the same variance as in the case when
all other additive components were known. Asymptotic properties are derived for both estimators under
dependent processes that are strictly stationary and absolutely regular. We also provide a demonstrative
empirical application of additive quantile models to ambulance travel times
Inventory control with seasonality of lead times
The practical challenges posed by the seasonality of lead times have largely been ignored within the inventory control literature. The length of the seasons, as well as the length of the lead times during a season, may demonstrate cyclical patterns over time. This study examines whether inventory control policies that anticipate seasonal lead-time patterns can reduce costs. We design a framework for characterizing different seasonal lead-time inventory problems. Subsequently, we examine the effect of deterministic and stochastic seasonal lead times within periodic review inventory control systems. We conduct a base case analysis of a deterministic system, enabling two established and alternating lead-time lengths that remain valid through known intervals. We identify essential building blocks for developing solutions to seasonal lead-time problems. Lastly, we perform numerical experiments to evaluate the cost benefits of implementing an inventory control policy that incorporates seasonal lead-time lengths. The findings of the study indicate the potential for cost improvements. By incorporating seasonality in length of seasons and length of lead times within the season into the control models, inventory controllers can make more informed decisions when ordering their raw materials. They need smaller buffers against lead-time variations due to the cyclical nature of seasonality. Reductions in costs in our experiments range on average between 18.9 and 26.4% (depending on safety time and the probability of the occurrence of stock out). Therefore, inventory control methods that incorporate seasonality instead of applying large safety stock or safety time buffers can lead to substantial cost reductions
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