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

Interpreting the Impact of a Professional Development Program: Views of EntrepreneurFellows from Kenya, South Africa, and UgandaOne Year Later

We conducted a qualitative study that examined the experiences of entrepreneurs who participated in a five-week-long professional development and cultural exchange fellowship program. The Entrepreneur Fellows represented three Sub-Saharan African nations and an array of enterprises, including agriculture and its allied fields, youth development organizations, and social ventures. The study assessed the post-fellowship experiences of the participants, especially regarding their enterprise-related goals, motivations and challenges, community-level impacts, as well as networking and communication practices. Analysis of data derived from 11 semi-structured interviews revealed three overarching themes and nine subthemes. The Entrepreneur Fellows were committed tolife-long learning and sharing information, gained new business skills, and expanded their global networks. The Fellows valued their program participation and viewed it as a mark of success. Further, participants were motivated not only by the growth of their enterprises, but also the potential to positively impact their communities. Our findings imply the need for multi-year, longitudinal research, including economic impact data from the Fellows’ enterprises. We also recommend that similar programming be supported and delivered in the future

Recipes for sparse LDA of horizontal data

Many important modern applications require analyzing data with more variables than observations, called for short horizontal. In such situation the classical Fisher’s linear discriminant analysis (LDA) does not possess solution because the within-group scatter matrix is singular. Moreover, the number of the variables is usually huge and the classical type of solutions (discriminant functions) are difficult to interpret as they involve all available variables. Nowadays, the aim is to develop fast and reliable algorithms for sparse LDA of horizontal data. The resulting discriminant functions depend on very few original variables, which facilitates their interpretation. The main theoretical and numerical challenge is how to cope with the singularity of the within-group scatter matrix. This work aims at classifying the existing approaches according to the way they tackle this singularity issue, and suggest new ones

The effect of risky debt on R&D investment

This paper investigates the interaction between investment decisions, company bankruptcy, and capital structure. We model young and innovative enterprises which face the possibility of making irreversible investments in R&D with uncertain returns, financed through risky debt. Uncertainty comes from two different sources: the technological success of the project and the return from investment. In an optimal investment setting, where uncertainty creates an incentive to delay investment decisions, we find the optimal threshold of entry (invest) and exit (bankruptcy), investigating both the case of infinite and finite debt maturity We show that the potential loss of the investment option in the event of default, reduces the value of waiting and provides equity holders with an incentive to accelerate the investment. Thus the results of the model here presented seem to imply an active role for financial institutions but traditional loans may not be the most suitable solution to finance risky investment. In line with recent recommendations of the European Investment Bank (EIB, 2013), traditional bank lending might need to be reinforced through further instruments, such as loan guarantees and securitisation

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

Banking proximity and firm performance. The role of small businesses, community banks and the credit cycle

This article analyses the link between banking geography and firm performance, i.e. whether the proximity within banks and between banks and borrowers has a positive impact on firms’ Returns on Assets (ROA).Using a unique dataset of Italian manufacturing firms and banks from 2006 to 2011 and an instrumental variable approach to account for endogeneity, we investigate whether this effect increases with the presence of community banks and small businesses and whether the relationship changes over the credit boom and bust, which preceded and followed the Lehman Brothers collapse.We show that geographical proximity matters for firm performance especially when the presence of community banks is high and when considering small (micro) firms. During the credit boom, both functional distance and operational proximity seem to matter, whereas, during the credit crunch, operational proximity has a more relevant role compared to functional distance in becoming an important driver to increase firm’s performance

The Effect of Financial Constraints on R&D Investments

This paper investigates the interaction between investment decisions, company foreclosure, and capital structure in the case of a constrained firm. We consider irreversible investments in R\&D projects with uncertain returns, financed through debt. Uncertainty comes from two different sources: the technological success of the project is probabilistic, and the return from investment evolves stochastically over time. These two elements, together with the lack of historical performance represent a substantial risk to the lenders, which will limit substantially the availability of loans. In our analysis, we first assume that the firm finances the R&D project through debt, and then, we further assume that the firm's debt capacity is limited to a certain amount. We show that leverage distorts the investment threshold and the shareholders of a levered firm accelerate investment with respect to an all equity financed firm. Moreover, when a firm is "financially constrained", it tends to overinvest compared to a non constrained levered firm. Thus, the financial constraint induces firms to play a "bird in the hand" investment strategy

Machine-learning models for bankruptcy prediction: do industrial variables matter?

We provide a predictive model specifically designed for the Italian economy that classifies solvent and insolvent firms one year in advance using the AIDA Bureau van Dijk data set for the period 2007–15. We apply a full battery of bankruptcy forecasting models, including both traditional and more sophisticated machine-learning techniques, and add to the financial ratios used in the literature a set of industrial/regional variables. We find that XGBoost is the best performer, and that industrial/regional variables are important. Moreover, belonging to a district, having a high mark-up and a greater market share diminish bankruptcy probability

Does the Past Count? Sovereign Debt during the Classical Gold Standard through the Lenses of Mover Stayer and Markov Chain Models

We study sovereign debt markets behaviour during the Classical Gold Standard (CGS) Era (1880- 1913), i.e. the first era of globalization characterized by free movement of capital and a fixed exchange rate regime. In particular we analyse both the issues of markets memory and the degree of confidence in sovereign debt markets by means of three stochastic models: Markov Chain (MC), Mover Stayer (MS) and Non Homogeneous Markov Chain (NHMC) estimated on two-state transition matrices of countries switching from sound to distressed. Markov Chain and Mover Stayer models beat the Non Homogeneous Markov Chain in fitting the data in the CGS period (1880-1913). This result implies both the short memory of the markets towards countries’ default history and an increased level of certainty which enables countries to better attract capital from lenders. The lessons learnt from the CGS period could also be relevant to understand sovereign debt markets in the Eurozone today given the striking similarities between the two periods