It Is Time to Reexamine the Role of Stock Markets in Developing Economies

Although the many perceived benefits of a stock market may have led to their rapid formation and development aspirations across economies, emerging findings that stock markets may not be as useful as previously thought, even a detraction in the case of some developing economies, raises the question of how practical stock markets may be in different situations. In light of the foregoing and using Fiji as an example, this study argues that it is indeed time to reexamine the role of stock markets in developing economies with a view to restructuring the financial system for improved efficiency and effectiveness.     

Which way does water flow? An econometric analysis of the global price integration of water stocks

Around US$600 billion of investment is desperately needed to address forecasted huge shortages in water supply globally. A number of worldwide investors – so-called water funds – have started to take up this challenge. For these global water investors, knowledge about the extent of integration between the water sectors of financial markets is highly important. According to international portfolio diversification theory, the less (more) integrated markets are, the more (less) benefits there are from international diversification. In this study, we investigate the extent and manner of interdependence among the US, European and Asian water sector of the equity markets based on Vector Autoregression (VAR), Granger causality and impulse response analyses. We find that world water stock market prices are indeed significantly interdependent although this interdependence varies across time periods. Each market quickly responds to shocks from each other and completes its response within 3 days. Hence, for water investors, international diversification that is undertaken just within the water sector will not be beneficial. The result also implies that there is the risk of crossmarket contagion – that is, price volatility spill over across water sectors of different financial markets, and therefore, water authorities in one market should take cognisance of events in other markets.     

The Political Economy of Policy Reform in the Philippines: 1992–1998

Accurately measuring Turkey's informal sector is important for policymaking. We utilize household income‐expenditure surveys to examine this sector's income underreporting. The Pissarides‐Weber approach hypothesizes that data would reflect such underreporting as “excess food consumption”. Our results suggest informal sector members spend more than their formal sector counterparts with comparable reported income levels. Using this information, we estimate the average size of the true informal sector to be about 1.25 times the official estimate. The informal sector accounts for around 83% of officially reported disposable income. Therefore, true Turkish disposable income is (25%)?(83%), roughly 21% larger than the officially estimated magnitude.     

Discretionary accruals: signalling or earnings management in Australia?

We investigate the signalling effect of discretionary accruals (DAC). Although we find that discretionary accruals are insignificantly related to contemporaneous stock returns, we uncover that income‐increasing discretionary accruals of GAAP‐complying growth firms are significantly and positively related to contemporaneous stock returns. Furthermore, we find that this positive effect is stronger among firms with better corporate governance mechanisms, such as Board of Directors Independence, Audit Committee Independence and Large Shareholders’ Ownership. In addition to contemporaneous stock returns, we also find similar results with the future increase in dividends. Our findings are consistent with the argument that corporate governance can enhance the signalling effect of reported earnings of GAAP‐complying growth firms.     

Foreign Aid in the Aftermath of Large Natural Disasters

This paper examines Official Development Assistance (ODA) in the aftermath of large natural disasters between 1970 and 2008. Using an event‐study approach, the paper finds that while the median increase in ODA is 18% compared with pre‐disaster flows, the typical surge is small in relation to the size of the affected economies. Moreover, aid surges typically cover only 3% of the total estimated economic damages caused by the disasters. The main determinants of post‐disaster aid surges are found to be the intensity of the event itself and the recipient country's characteristics such as the level of development, country size and the stock of foreign reserves. The paper does not find evidence that political considerations or strategic behavior on the part of donors determine the size of post‐disaster aid surges.     

Mortgage Prepayment and Default Decisions: A Poisson Regression Approach

This paper uses an extensive and geographically dispersed sample of single-family fixed rate mortgages to assess the prepayment and default behavior of individual homeowners. We make use of Poisson regression to efficiently estimate the parameters of a proportional hazards model for prepayment and default decisions. Poisson regression for grouped survival data has several advantages over partial likelihood methods. First, when dealing with time-dependent covar-iates, it is considerably more efficient in terms of computations. Second, it is possible to estimate full-hazard models which include, for example, functions of time as well as multiple time scales (i.e., age of the loan and calendar time), in a much more straightforward manner than partial likelihood methods for un-grouped data. Third, Poisson regression can be used to estimate non-proportional hazards models such as additive excess risk specifications. Taken together, our data and estimation methodology allow us to obtain a better understanding of the economic factors underlying prepayment and default decisions.     

Monetary,fiscal, and structural drivers of inflation in Ethiopia: new empirical evidence from time series analysis

This study empirically investigates the drivers of inflation in Ethiopia using monthly data over the period July 1998 to September 2020. It explores short-run and long-run effects of domestic and external determinants of inflation—including demand-side, supply-side, and structural factors—using the cointegration and vector error-correction methodology. Four measures of inflation are considered: cereals, food, nonfood, and all-items Consummer Price Index (CPI) inflation. A key contribution to the existing literature is the investigation of the role of the fiscal sector in modeling inflation, a topic that has been neglected in the existing studies on inflation in Ethiopia. The empirical results show that disequilibria in the monetary sector, grains sector, and food markets have long-run effects on inflation. In the short run, inflation is driven by structural factors (notably, cereal output gaps and imported inflation) as well as demand-side factors (notably, money growth and public sector borrowing). The results hold when analysis is limited to the high growth period from 2005 onward, following the end of the International Monetary Fund (IMF) program in the country. The evidence provides valuable insights in the context of ongoing macroeconomic policy reforms in Ethiopia.     

Machine learning advances for time series forecasting

In this paper, we survey the most recent advances in supervised machine learning (ML) and high-dimensional models for time-series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.     

“When Life Gives You Lemons”: Using Cross-Sectional Surveys to Identify Chronic Poverty in the Absence of Panel Data

At the core of poverty eradication is the need to eliminate that poverty that is persistent over time (chronic poverty). Unfortunately, traditional approaches to identifying chronic poverty require longitudinal data that is rarely available. In its absence, this paper proposes an alternative approach that only requires 1 year of cross-sectional data on monetary and non-monetary poverty. It puts forth two conjectures and contends that the combined profile of a household as both income poor and multidimensionally poor can be used as a proxy of that household being chronically income poor. To explore the viability of this approach, we use a probit model and longitudinal data for three Latin American countries to estimate households’ probabilities of remaining in income poverty based on their past income and multidimensional poverty statuses. We find empirical support for the approach that is significant, consistent across countries, and robust to various controls and periods of analysis.