ESTIMATING MULTICOUNTRY VAR MODELS*

This article presents a method to estimate the coefficients, to test specification hypotheses, and to conduct policy exercises in multicountry Vector Autoregressive (VAR) models with cross‐unit interdependencies, unit‐specific dynamics, and time variations in the coefficients. The framework of analysis is Bayesian: A prior flexibly reduces the dimensionality of the model and puts structure on the time variations, Markov chain Monte Carlo (MCMC) methods are used to obtain posterior distributions, and marginal likelihoods to check the fit of various specifications. Impulse responses and conditional forecasts are obtained with the output of an MCMC routine. The transmission of certain shocks across countries is analyzed.     

Forward and backward dynamics in implicitly defined overlapping generations models

In dynamic economic models derived from optimization principles, the forward equilibrium dynamics may not be uniquely defined, while the backward dynamics is well defined. We derive properties of the global forward equilibrium paths based on properties of the backward dynamics. We propose the framework of iterated function systems (IFSs) to describe the set of forward equilibria and apply the IFS framework to a one- and a two-dimensional version of the overlapping generations (OLG)-model. We show that if the backward dynamics is chaotic and has a homoclinic orbit (a “snap-back repeller”), the set of forward equilibrium paths converges to a fractal attractor. Forward equilibria may be interpreted as sunspot equilibria, where a random sunspot sequence determines equilibrium selection at each date.     

Nature of innovation and technology management in system companies

Competition in the industries that develop complex products (such as aircraft engines and radar) depends on the integration of organizational systems as well as the pace of technological change.
Companies in these industries can be regarded as 'system companies'. A framework and taxonomy are developed to indicate the characteristics and types of companies and to understand how organizational learning and integration take place. This makes it easier for companies to anticipate strategic change and some implications of this kind are described.     

Quantile Aggregation of Density Forecasts

Quantile aggregation (or ‘Vincentization’) is a simple and intuitive way of combining probability distributions, originally proposed by S.B. Vincent in 1912. In certain cases, such as under Gaussianity, the Vincentized distribution belongs to the same family as that of the individual distributions and it can be obtained by averaging the individual parameters. This article compares the properties of quantile aggregation with those of the forecast combination schemes normally adopted in the econometric forecasting literature, based on linear or logarithmic averages of the individual densities. Analytical results and Monte Carlo experiments indicate that the properties of quantile aggregation are between those of the linear and the logarithmic pool. Larger differences among the combination schemes occur when there are biases in the individual forecasts: in that case quantile aggregation seems preferable on the whole. The practical usefulness of Vincentization is illustrated empirically in the context of linear forecasting models for Italian GDP and quantile predictions of euro area inflation.     

Has the Euro‐Mediterranean Partnership Affected Mediterranean Business Cycles?

We date turning points of the reference cycle for 19 Mediterranean countries and analyze their structure and interdependencies. Fluctuations are volatile and not highly correlated across countries; recessions are deep but asynchronous, the distribution of output losses in recessions spread out. Heterogeneities across countries and regions are substantial. Cyclical fluctuations are poorly related to trade and financial linkages. Mediterranean cycles are time varying but their evolution is not linked with the Euro‐Mediterranean partnership process. Copyright © 2013 John Wiley & Sons, Ltd.     

Convergence of Prices and Rates of Inflation*

We consider how unit‐root and stationarity tests can be used to study the convergence of prices and rates of inflation. We show how the joint use of these tests in levels and first differences allows the researcher to distinguish between series that are converging and series that have already converged, and we set out a strategy to establish whether convergence occurs in relative prices or just in rates of inflation. Special attention is paid to the issue of whether a mean should be extracted in carrying out tests in first differences and whether there is an advantage to adopting a (Dickey–Fuller) unit‐root test based on deviations from the last observation. The asymptotic distribution of this last test statistic is given and Monte Carlo simulation experiments show that the test yields considerable power gains for highly persistent autoregressive processes with ‘relatively large’ initial conditions. The tests are applied to the monthly series of the consumer price index in the Italian regional capitals over the period 1970–2003.     

Employment And Mobility Of Workers In Industries With Different Intensity Of Innovation: Evidence On Italy From A Panel Of Workers And Firms

In this paper a panel of workers and firms is used to investigate employment composition and dynamics in industries which differ by innovation intensity. To define the latter industry-wide statistics were used (for a subset of 2,800 firms, individual data on R&D expenditures and investments in innovative processes were available from a survey on manufacturing). Firms and workers are observed over the period 1985–1991. The paper document an high rate of labour turnover. Annual separation rates are high in all size-classes, but they decline from 50% in small firms (less than 20 employees) to 13% in large ones (with more than 1,000 employees). Separations are inversely related to an industry's innovative intensity (from 18% in the highly innovative industries to 31% in the traditional industries). A logit model, which controlled for the characteristics of workers and firms, showed that the probability of separation is higher among manual and young workers and decreases monotonically with the firm size. The probability of separation declines as job tenure and, perhaps more importantly, the individual's wage increases. After controlling for these factors, the evidence suggests that the highest probability of separation is in traditional industries, the lowest is in the more innovative industries. The result is strengthened when firm-level data on R&D and other innovative expenditures are used. Other things being equal, firms that invest in R&D have a more stable labour force, and firms that invest in non-innovative processes have a less stable labour force. We therefore find empirical evidence to support the hypothesis that more innovative firms cultivate more durable employer-employee relationships. The fraction of job-to-job moves (with no intervening period of unemployment) on total separations qualifies the turnover of workers. Controlling for firm size, the percentage of job-to-job moves increases fairly regularly with worker's skills and with the industry's innovative intensity. Thus the innovative intensity of he industry appears to have a positive effect on the share of job-to-job moves, while there is some evidence that it lowers the chances of separation. This result may be linked to the skills and specialisations of the workforce; it is certainly related to the higher demand for labour in the High Tech Sectors (where employment is growing) relative to the less innovative sectors.     

Emerging market sovereign bond spreads: Estimation and back-testing

We estimate sovereign bond spreads of 28 emerging economies over the period January 1998–December 2011 and test the ability of the model in generating accurate in-sample predictions for bond spreads. The impact and significance of explanatory variables on spreads vary across regions and periods. During crisis times, good macroeconomic indicators are helpful in containing spreads, but less than in non-crisis times, possibly reflecting the impact of extra-economic forces on spreads when a financial crisis occurs. For some economies, in-sample predictions of the monthly changes in spreads obtained with rolling regression routines are more accurate than those obtained with random guessing.