Nowcasting Indian GDP

Nowcasting has become a useful tool for making timely predictions of gross domestic product (GDP) in a data‐rich environment. However, in developing economies this is more challenging due to substantial revisions in GDP data and the limited availability of predictor variables. Taking India as a leading case, we use a dynamic factor model nowcasting method to analyse these two issues. Firstly, we propose to compare nowcasts of the first release of GDP to those of the final release to assess differences in their predictability. Secondly, we expand a standard set of predictors typically used for nowcasting GDP with nominal and international series, in order to proxy the variation in missing employment and service sector variables in India. We find that the factor model improves over several benchmarks, including bridge equations, but only for the final GDP release and not for the first release. Also, the nominal and international series improve predictions over and above real series. This suggests that future studies of nowcasting in developing economies which have similar issues of data revisions and availability as India should be careful in analysing first‐ vs. final‐release GDP data, and may find that predictions are improved when additional variables from more timely international data sources are included.     

R&D Investment timing,default and capital structure

Review of Quantitative Finance and Accounting - This paper investigates the interaction between R&D investment timing, probability of default, and capital structure. In particular we are...     

Gini’s transvariation analysis: an application on financial crises in developing countries

The damage and the recurrence of financial crises have increased the concern of investors and policymakers on one hand and the interest of macroeconomists on the other. This paper presents an original non parametric methodology, whose aim is to give a very intuitive and rigorous method for variable selection in order to analyse financial crises. Transvariation analysis compares the distributions of two different groups of countries (sound and distressed) with respect to a single macroeconomic variable and selects the indicators on the basis of a low transvariation probability index. The current account deficit to GDP ratio, differently from other studies on financial crises, seems to be a suitable variable in discriminating distressed countries from sound ones, and the case of Argentina and Turkey confirms this finding.