The Discovery of Long-Run Causal Order: A Preliminary Investigation

The relation between causal structure and cointegration and long-run weak exogeneity is explored using some ideas drawn from the literature on graphical causal modeling. It is assumed that the fundamental source of trending behavior is transmitted from exogenous (and typically latent) trending variables to a set of causally ordered variables that would not themselves display nonstationary behavior if the nonstationary exogenous causes were absent. The possibility of inferring the long-run causal structure among a set of time-series variables from an exhaustive examination of weak exogeneity in irreducibly cointegrated subsets of variables is explored and illustrated.

A new structural break test for panels with common factors

Summary This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement—more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing generalized method of moments methodology and develops Distance-type and Lagrange Multiplier-type tests for detecting a structural break, both when the break point is known and when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002–2014.

Cointegration and Adjustment in the CVAR(∞) Representation of Some Partially Observed CVAR(1) Models

A multivariate CVAR(1) model for some observed variables and some unobserved variables is analysed using its infinite order CVAR representation of the observations. Cointegration and adjustment coefficients in the infinite order CVAR are found as functions of the parameters in the CVAR(1) model. Conditions for weak exogeneity for the cointegrating vectors in the approximating finite order CVAR are derived. The results are illustrated by two simple examples of relevance for modelling causal graphs.

Innovation, Productivity and Environmental Performance of Technology Spillovers Effects: Evidence from European Patents within the Triad

The aim of this paper is that of investigating whether the integration process between environmental activities is important in the Spillovers flows analysis. For this reason, we explore the role of knowledge externalities for large international firms engaged both in environmental and in non-environmental activities. In particular, we develop a theoretical framework and an empirical analysis of the United States, Japan and Europe based upon a dataset composed of worldwide R&D-intensive firms. In order to deal with the firms’ unobserved heterogeneity and the weak exogeneity of the regressors, we implement the Generalized Method of Moments (GMM) method. The results show a differentiated impact of environmental spillovers on firms’ productivity and green performance, by suggesting some interesting policy implications in terms of actions to favor full sustainability of firms’ production.

The relative contribution of alternative capital flows to South Africa: An empirical investigation

The need for external capital flows to developing countries to supplement domestic savings for investment and growth cannot be over-emphasised, especially in Africa, where there are high levels of poverty and low domestic capacity to save. To achieve sustainable economic growth, countries require other sources of capital from outside the domestic economy. This study uses co-integration and error correction modelling techniques together with tests for weak exogeneity to analyse the effects of four major capital flows into South Africa. This is done for the period 1970 to 2012 in order to determine the relative contribution of these capital flows to South Africa