Riccardo Lucchetti

The Adoption of ICT among SMEs: Evidence from an Italian Survey

In this paper, we carry out an econometric analysis on the adoption and effective use of Information and Communication Technologies (ICTs) among a sample of Italian SMEs. An important result is that, in order to understand the main factors that drive SMEs to the adoption of ICTs, it is useful to divide the available technologies according to a taxonomy based on their typical function. Along with general-use ICTs (e-mail and Internet access), we identify two groups of technologies (labelled production-integrating and market-oriented ICTs) the use of which is associated with different firm characteristics. In both cases, however, the availability of highly educated workers turns out to be a key factor. Policy implications are also discussed.

Identification of covariance structures

The issue of identification of covariance structures, which arises in a number of different contexts, has been so far linked to conditions on the true parameters to be estimated. In this paper, this limitation is removed. As done by Johansen (1995) in the context of linear models, the present paper provides necessary and sufficient conditions for the identification of a covariance structure that depend only on the constraints, and can therefore be checked independently of estimated parameters. A sufficient condition is developed, which only depends on the structure of the constraints. It is shown that this structure condition, if coupled with the familiar order condition, provides a sufficient condition for identification. In practice, since the structure condition holds if and only if a certain matrix, constructed from the constraint matrices, is invertible, automatic software checking for identification is feasible even for large-scale systems.

Intertemporal remittance behaviour by immigrants in Germany

In this paper, we use data from the German Socio-Economic Panel (SOEP) in the 1997-2009 period for a large sample of migrants from 84 countries in order to develop an empirical model for the propensity by migrants to remit. Our model takes into full account the intertemporal aspects of the problem, which has been ignored by a large part of the applied literature, despite its theoretical and empirical importance. We find that most results already established in the empirical literature are confirmed; however, the intertemporal nature of the remittance behaviour emerges very clearly, giving rise to individual patterns which are difficult to synthesize by a simple description. Building on our framework, we find also support for theoretical models which predict different remittance time paths between return and permanent migrants.

Nonlinear adjustment in US bond yields: An empirical model with conditional heteroskedasticity

Starting from the work by Campbell and Shiller (Campbell, J.Y. and Shiller, R.J. (1987). Cointegration and tests of present value models. Journal of Political Economy, 95(5):1062-1088.), empirical analysis of interest rates has been conducted in the framework of cointegration. However, parts of this approach have been questioned recently, as the adjustment mechanism may not follow a simple linear rule; another line of criticism points out that stationarity of the spreads is difficult to maintain empirically. In this paper, we analyse data on US bond yields by means of an augmented VAR specification which approximates a generic nonlinear adjustment model. We argue that nonlinearity captures macro information via the shape of the yield curve and thus provides an alternative explanation for some findings that recently appeared in the literature. Moreover, we show how conditional heteroskedasticity can be taken into account via GARCH specifications for the conditional variance, either univariate or multivariate.

Artificial regression testing in the GARCH-in-mean model

The issue of finite-sample inference in Generalised Autoregressive Conditional Heteroskedasticity (GARCH)-like models has seldom been explored in the theoretical literature, although its potential relevance for practitioners is obvious. In some cases, asymptotic theory may provide a very poor approximation to the actual distribution of the estimators in finite samples. The aim of this paper is to propose the application of the so-called double length regressions (DLR) to GARCH-in-mean models for inferential purposes. As an example, we focus on the issue of Lagrange Multiplier tests on the risk premium parameter. Simulation evidence suggests that DLR-based Lagrange Multiplier (LM) test statistics provide a much better testing framework than the more commonly used LM tests based on the outer product of gradients (OPG) in terms of actual test size, especially when the GARCH process exhibits high persistence in volatility. This result is consistent with previous studies on the subject. Copyright 2005 Royal Economic Society

A test for bivariate normality with applications in microeconometric models

In this paper, we propose a test for bivariate normality in imperfectly observed models, based on the information matrix test for censored models with bootstrap critical values. In order to evaluate its properties, we run a comprehensive Monte Carlo experiment, in which we use the bivariate probit model and Heckman sample selection model as examples. We find that, while asymptotic critical values can be seriously misleading, the use of bootstrap critical values results in a test that has excellent size and power properties even in small samples. Since this procedure is relatively inexpensive from a computational viewpoint and is easy to generalise to models with arbitrary censoring schemes, we recommend it as an important and valuable testing tool.

Can you do the wrong thing and still be right? Hypothesis testing in I(2) and near-I(2) cointegrated VARs

ABSTRACT In this paper, we investigate the small-sample performance of LR tests on long-run coefficients in the I(2) model; we focus on a comparison between I(2) and near-I(2) data, i.e. I(1) data with a second root very close to unity, and report the results of some Monte Carlo experiments. With near-I(2) data, the finite-sample properties of the tests are (i) similar to those found with genuine I(2) data, (ii) systematically superior to those of the analogous tests constructed in the I(1) model, even if the latter is, in principle, correctly specified and the former is not. Therefore, there seems to be strong support to the idea that, in practice, modelling near-I(2) data using the I(2) model may be a good idea, despite the inherent misspecification.