Unit roots versus other types of time heterogeneity,parameter time dependence and superexogeneity

This paper stresses the restrictive nature of the standard unit root/cointegration assumptions and examines a more general type of time heterogeneity, which might characterize a number of economic variables, and which results in parameter time dependence and misleading statistical inference. We show that in such cases ‘operational’ models cannot be obtained, and the estimation of time‐varying parameter models becomes necessary. For instance, economic processes subject to endemic change can only be adequately modelled in a state space form. This is a very important point, because unstable models will break down when used for forecasting purposes. We also discuss a new test for the null of cointegration developed by Quintos and Phillips (1993), which is based on parameter constancy in cointegrating regressions. Finally, we point out that, if it is possible to condition on a subset of superexogenous variables, parameter instability can be handled by estimating a restricted system. Copyright © 2002 John Wiley & Sons, Ltd.     

Conjugated action of two species-specific invasion proteins for fetoplacental listeriosis

The ability to cross host barriers is an essential virulence determinant of invasive microbial pathogens. Listeria monocytogenes is a model microorganism that crosses human intestinal and placental barriers, and causes severe maternofetal infections by an unknown mechanism. Several studies have helped to characterize the bacterial invasion proteins InlA and InlB. However, their respective species specificity has complicated investigations on their in vivo role. Here we describe two novel and complementary animal models for human listeriosis: the gerbil, a natural host for L. monocytogenes, and a knock-in mouse line ubiquitously expressing humanized E-cadherin. Using these two models, we uncover the essential and interdependent roles of InlA and InlB in fetoplacental listeriosis, and thereby decipher the molecular mechanism underlying the ability of a microbe to target and cross the placental barrier.     

Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas

This paper investigates the implications of time‐varying betas in factor models for stock returns. It is shown that a single‐factor model (SFMT) with autoregressive betas and homoscedastic errors (SFMT‐AR) is capable of reproducing the most important stylized facts of stock returns. An empirical study on the major US stock market sectors shows that SFMT‐AR outperforms, in terms of in‐sample and out‐of‐sample performance, SFMT with constant betas and conditionally heteroscedastic (GARCH) errors, as well as two multivariate GARCH‐type models. Copyright © 2016 John Wiley & Sons, Ltd.     

The routing and cost of the information flow in a system

A study of information flows in different types of procedures used for the management of the agricultural credit in the Agricultural Bank of Greece is discussed in this paper. Systems and subsystems are structured using new diagrammatical notation in order to identify explicitly the system. The communication network of the information flow with certain nodes (individuals) and types of links (types of communication) is also considered. In addition to the normal link cost of such diagrams, a node cost, which depends on the types of links involved in the network, is included. An optimal routing from a given node to its destination node can thus be sought. The conditions of stability and change of the system are examined from the information flow cost point of view through the procedures associated with short-term agricultural loans.     

Causality and forecasting in incomplete systems

In this paper we examine how causality inference and forecasting within a bivariate VAR, consisting of y(t) and x(t), are affected by the omission of a third variable, w(t), which causes (a) none, (b) one, and (c) both variables in the bivariate system. We also derive conditions under which causality inference and forecasting are invariant to the selection of a bivariate or a trivariate model. The most general condition for the invariance of both causality and forecasting to model selection is shown to require the omitted variable not to cause any of the variables in the bivariate system, although it allows the omitted variable to be caused by the other two. We also show that the conditions for one-way causality inference to be invariant to model selection are not sufficient to ensure that forecasting will also be invariant to the model selected. Finally, we present a numerical illustration of the potential losses, in terms of the variance of the forecast, as a function of the forecast horizon and for alternative parameter values—they can be rather large, as the omission of a variable can make the incomplete model unstable. © 1997 John Wiley & Sons, Ltd.     

Estimation and forecasting in first‐order vector autoregressions with near to unit roots and conditional heteroscedasticity

This paper investigates the effects of imposing invalid cointegration restrictions or ignoring valid ones on the estimation, testing and forecasting properties of the bivariate, first‐order, vector autoregressive (VAR(1)) model. We first consider nearly cointegrated VARs, that is, stable systems whose largest root, l_max, lies in the neighborhood of unity, while the other root, l_min, is safely smaller than unity. In this context, we define the ‘forecast cost of type I’ to be the deterioration in the forecasting accuracy of the VAR model due to the imposition of invalid cointegration restrictions. However, there are cases where misspecification arises for the opposite reasons, namely from ignoring cointegration when the true process is, in fact, cointegrated. Such cases can arise when l_max equals unity and l_min is less than but near to unity. The effects of this type of misspecification on forecasting will be referred to as ‘forecast cost of type II’. By means of Monte Carlo simulations, we measure both types of forecast cost in actual situations, where the researcher is led (or misled) by the usual unit root tests in choosing the unit root structure of the system. We consider VAR(1) processes driven by i.i.d. Gaussian or GARCH innovations. To distinguish between the effects of nonlinear dependence and those of leptokurtosis, we also consider processes driven by i.i.d. t(2) innovations. The simulation results reveal that the forecast cost of imposing invalid cointegration restrictions is substantial, especially for small samples. On the other hand, the forecast cost of ignoring valid cointegration restrictions is small but not negligible. In all the cases considered, both types of forecast cost increase with the intensity of GARCH effects. Copyright © 2009 John Wiley & Sons, Ltd.