Cosimo Damiano Vitale

Conceptual-stochastic modeling of seasonal runoff using autoregressive moving average models and different scales of aggregation

The statistical and phenomenological aspects of the runoff process observed on different scales of aggregation are taken as a priori information for the conceptually based stochastic modeling of seasonal runoff. Runoff is considered as the sum of two groundwater components, with over-year and subannual response lag, and of a purely random component representing the direct runoff. This scheme is equivalent to a linear system, with two parallel linear reservoirs plus a zero lag linear channel. The system output is the runoff, and the input is the effective rainfall, considered proportional to the direct runoff. Assuming the effective rainfall as a non-Gaussian periodic independent process and considering nonseasonal groundwater parameters, this conceptualization leads to an autoregressive and moving average (2, 2) stochastic process with periodic independent residual. Stochastic model parameters are directly related to the linear system coefficients, and the effective rainfall structure can be determined from the estimated model residual. In order to obtain parameter estimates consistent with the conceptual constraints, two estimation stages, on an annual and a seasonal basis, and an iterative procedure are needed. The model was applied to a number of time series of monthly streamflows in the Apennine regions of Italy with promising results.

BL-GARCH models and asymmetries in volatility

In this paper the class of Bilinear GARCH (BL-GARCH) models is proposed. BL-GARCH models allow to capture asymmetries in the conditional variance of financial and economic time series by means of interactions between past shocks and volatilities. The availability of likelihood based inference is an attractive feature of BL-GARCH models. Under the assumption of conditional normality, the log-likelihood function can be maximized by means of an EM type algorithm. The main reason for using the EM algorithm is that it allows to obtain parameter estimates which naturally guarantee the positive definiteness of the conditional variance with no need for additional parameter constraints. We also derive a robust LM test statistic which can be used for model identification. Finally, the effectiveness of BL-GARCH models in capturing asymmetric volatility patterns in financial time series is assessed by means of an application to a time series of daily returns on the NASDAQ Composite stock market index.

Consumer acceptance of food nanotechnology in Italy.

Purpose – The purpose of this paper is to investigate attitudes of Italian consumers towards a set of applications of nanotechnology in the food domain. The chief goal is to identify the main factors influencing the willingness to buy nanofoods (WTBN), distinguishing between factors related to the products, in terms of perceived risks and benefits and psychological factors. Design/methodology/approach – A questionnaire was administered to a sample of about 300 people to gather information about the willingness to buy six nanofoods (namely: creamier ice cream with the same fat content; salt and sugar that do not form lumps with moisture; fruit juices enriched with bioactive molecules; bread enriched with Omega-3; plastic bottles for beer; antimicrobial food packaging for meat) and psychological characteristics, measured by several attitudinal scales. In order to study the influence of the attitudinal factors on the WTBN a simultaneous equations model was estimated, defining both its structural and reduced ...

Likelihood inference in BL-GARCH models

SummaryThe paper presents a procedure based on the EM algorithm for the indirect estimation of the parameters of BiLinear GARCH (BL-GARCH) models. BL-GARCH generalize the class of GARCH models by considering interactions of past shocks and volatilities in the conditional variance equation. In this way the response of the conditional variance to past information becomes asymmetric allowing to account for the so called leverage effect, typically characterizing the behaviour of financial time series. The results of an application to a time series of stock market returns are presented.

Temporal Aggregation and Closure of VARMA Models: Some New Results

In this paper we examine the effects of temporal aggregation on Vector AutoRegressive Moving Average (VARMA) models. It has relevant implications both in theoretical and empirical domain. Among them we focus the attention on the main consequences of the aggregation (obtained from point in time sampling) on the model identification. Further, under well defined conditions on the model parameters, we explore the closure of the VARMA class (with respect to the temporal aggregation) through theoretical results discussed in proper examples.

Statistical Properties of Threshold Models

This article focuses the attention on the Self Exciting Threshold Autoregressive Moving Average model (SETARMA) proposed in Tong (1983). The stochastic structure of the model is discussed and different specifications are presented. Starting from one of them, we give sufficient conditions for the weak stationarity of the model that are discussed and critically compared to other results given in literature. In particular, after showing that the SETARMA model belongs to the class of the Random Coefficients Autoregressive models, widely discussed in Nicholls and Quinn (1982), we give some issues on the weak stationarity of its stochastic structure that are more general than those given in the existing literature and appear not affected by the moving average component.

Vector Threshold Moving Average Models: Model Specification and Invertibility

In this chapter we propose a class of nonlinear time series models in which the underlying process shows a threshold structure where each regime follows a vector moving average model. We call this class of processes Threshold Vector Moving Average. The stochastic structure is presented even proposing alternative model specifications. The invertibility of the model is discussed detail and, in this context, empirical examples are proposed to show some features that distinguish the stochastic structure under analysis from other linear and nonlinear time series models widely investigated in the literature.

Least Squares Predictors for Threshold Models: Properties and Forecast Evaluation

The forecasts generation from models that belong to the threshold class is discussed. The main problems that arise when forecasts have to be computed from these models are presented and, in particular, least squares, plug-in and combined predictors are pointed out. The performance of the proposed predictors are investigated using simulated and empirical examples that give evidence in favor of the forecasts combination.

The Autocorrelation Functions in SETARMA Models

The dependence structure of a family of self exciting threshold autoregressive moving average (SETARMA) models, is investigated. An alternative representation for this class of models is proposed and the exact autocorrelation function is derived in the case of two regimes. Some practical implications of the theoretical results are analysed and discussed via several examples of SETARMA structures of fixed orders

Threshold Vector Arma Models

In this article, we propose the threshold vector autoregressive moving average model (TVARMA). It is a multivariate nonlinear time series model characterized by two or more regimes that follow a vector ARMA structure and where the switching among them is regulated by a latent variable. The TVARMA model represents a generalization of some nonlinear models proposed in the literature and shows interesting features that are explored. The condition for the strong and weak stationarity of the TVARMA model are presented and the moments up to order two of the process are derived.