We estimated a non-Stationary dynamic factor model and used it to generate artificial episodes of disinflation (permanent changes in the mean inflationrate). These datasets were used to test the forecasting abilities of alternative underlying inflation indicators (i.e. measures that capture sustained movements in inflation extracted from information in a disaggregated set of price data).We found that the out of sample forecast errors of the benchmark underlying inflation measures (based on unobserved trend extraction) are more severely affected by disinflation than the alternative simpler methods (based on exclusionor re-weighting approaches). We also show that a non-stationary dynamic factor model may be employed for the extraction of the unobserved trend to be usedas an underlying inflation measure.

Inflation targeting is nowadays used by around 40 countries, with each of them tailoring some features of the strategy to its own needs. This holdse specially for deciding on the level of inflation targets. The analysis conducted in the paper aims at identifying factors affecting the choice of the target levels, with macroeconomic, structural and institutional characteristics of the reviewed economies being investigated. The main conclusion is that both backward- and forward-looking models can help to explain how inflation targets are set. Evidently inflation and GDP growth (past and forecast) together within formation on a possibly on going disinflation process are of key importance,but – especially for emerging market economies – also inflation variance and the level of economic development seem to influence the target levels. Moreover,many of the institutional features related, among others, to transparency and accountability of the reviewed central banks, were found significant in the analysis.

We provide a detailed analysis of a unifying theoretical framework forinnovation and corporate dynamics that encompasses the Gibrat’s Law of Proportionate Effect and the Simon growth process as particular instances. The predictions of the model are derived in terms of (i) firm size distribution, (ii) the distribution of firm growth rates, and (iii-iv) the relationships between firm size and the mean and variance of firm growth rates. We test the model against data from the worldwide pharmaceutical industry and find its predictions tobe in good agreement with empirical evidence on all four dimensions. Our unifying stochastic framework can also be used to describe corporate dynamics in different industries and as a benchmark for discriminating among alternative data generating processes.