An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem

This problem involves optimizing product collection and redistribution from production locations to a set of processing plants over a planning horizon. This horizon consists of several days, and the collection-redistribution is performed on a repeating daily basis. A single routing plan must be prepared for the whole horizon, taking into account the seasonal variations in the supply. We model the problem using a sequence of periods, each corresponding to a season. We propose an adaptive large-neighborhood search with several specifically designed operators and features. The results show the excellent performance of the algorithm in terms of solution quality and computational efficiency.     

General convex order on risk aggregation

Using a general notion of convex order, we derive general lower bounds for risk measures of aggregated positions under dependence uncertainty, and this in arbitrary dimensions and for heterogeneous models. We also prove sharpness of the bounds obtained when each marginal distribution has a decreasing density. The main result answers a long-standing open question and yields an insight in optimal dependence structures. A numerical algorithm provides bounds for quantities of interest in risk management. Furthermore, our numerical results suggest that the bounds obtained in this paper are generally sharp for a broader class of models.     

A nesting framework for Markov-switching GARCH modelling with an application to the German stock market

In this paper, we establish a generalized two-regime Markov-switching GARCH model which enables us to specify complex (symmetric and asymmetric) GARCH equations that may differ considerably in their functional forms across the two Markov regimes. We show how previously proposed collapsing procedures for the Markov-switching GARCH model can be extended to estimate our general specification by means of classical maximum-likelihood methods. We estimate several variants of the generalized Markov-switching GARCH model using daily excess returns of the German stock market index DAX sampled during the last decade. Our empirical study has two major findings. First, our generalized model outperforms all nested specifications in terms of (a) statistical fit (when model selection is based on likelihood ratio tests) and (b) out-of-sample volatility forecasting performance. Second, we find significant Markov-switching structures in German stock market data, with substantially differing volatility equations across the regimes.     

Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism

We evaluate the short term forecasting performance of methods that systematically incorporate high frequency information via covariates. Our study provides a thorough introduction of these methods to the tourism literature. We highlight the distinguishing features and limitations of each tool and evaluate their forecasting performance in two tourism-specific applications. The first uses monthly indicators to predict quarterly tourist arrivals to Hawaii; the second predicts quarterly labor income in the accommodations and food services sector. Our results indicate that compared to the exclusive use of low frequency aggregates, including timely intra-period data in the forecasting process results in significant gains in predictive accuracy. Anticipating growing popularity of these techniques among empirical analysts, we present practical implementation guidelines to facilitate their adoption.     

Currency crisis early warning systems: Why they should be dynamic

Traditionally, financial crisis Early Warning Systems (EWSs) have relied on macroeconomic leading indicators when forecasting the occurrence of such events. This paper extends such discrete-choice EWSs by taking the persistence of the crisis phenomenon into account. The dynamic logit EWS is estimated using an exact maximum likelihood estimation method in both a country-by-country and a panel framework. The forecasting abilities of this model are then scrutinized using an evaluation methodology which was designed recently, specifically for EWSs. When used for predicting currency crises for 16 countries, this new EWS turns out to exhibit significantly better predictive abilities than the existing static one, both in- and out-of-sample, thus supporting the use of dynamic specifications for EWSs for financial crises.     

CO‐INTEGRATING VAR MODELS AND ECONOMIC POLICY

This paper surveys some relevant contributions to the economic literature on co‐integrating vector autoregressive (VAR) models [vector error correction mechanisms (VECMs)], emphasizing their usefulness for economic policy. It further discusses some theoretical aspects that are necessary for a complete understanding of their potential. The theoretical introduction of the co‐integrating VAR model is followed by an illustration of its applications to monetary policy, fiscal policy and exchanges rates as well as in establishing the effects of structural bilateral shocks between countries (the so‐called global VAR, or GVAR, models). Special attention is paid to the VECM capacities of being used in conjunction with dynamic stochastic general equilibrium models and of jointly specifying the short‐ and long‐run dynamics, thus representing the steady‐state of economic systems (by means of the co‐integration relations) and the short‐run dynamics around it.     

A number‐of‐modes reference rule for density estimation under multimodality

We consider kernel density estimation for univariate distributions. The question of interest is as follows: given that the data analyst has some background knowledge on the modality of the data (for instance, ‘data of this type are usually bimodal’), what is the adequate bandwidth to choose? We answer this question by extending Silverman's idea of ‘normal‐reference’ to that of ‘reference to a Gaussian mixture’. The concept is illustrated in the light of real data examples.     

When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods

This paper uses three classes of univariate time series techniques (ARIMA type models, switching regression models, and state-space/structural time series models) to forecast, on an ex post basis, the downturn in U.S. housing prices starting around 2006. The performance of the techniques is compared within each class and across classes by out-of-sample forecasts for a number of different forecast points prior to and during the downturn. Most forecasting models are able to predict a downturn in future home prices by mid 2006. Some state-space models can predict an impending downturn as early as June 2005. State-space/structural time series models tend to produce the most accurate forecasts, although they are not necessarily the models with the best in-sample fit.