Convergence in household credit demand across euro area countries: evidence from panel data

This article contributes to the literature on the convergence of financial systems in the euro area by estimating household credit demand in individual countries. Using the ARDL framework advocated notably by Pesaran et al. (1999 Pesaran, MH, Shin, Y and Smith, RP. 1999. Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94: 621–34. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), the article provides evidence on the convergence of long-run credit demand determinants (interest rates, investment and house prices) in the largest euro area countries, while short run-dynamics remain heterogenous across countries. The article also demonstrates that the equation uncovers demand rather than supply behaviour.     

Institutional investment horizon and investment–cash flow sensitivity

This paper examines the relevance of institutional investors’ investment horizon, as reflected in the response of firm investment to internal cash flows. We argue that institutional investors with longer investment horizons have greater incentives and efficiencies to engage in effective monitoring. This improved monitoring mitigates asymmetric information and agency problems, and in turn reduces the wedge between the costs of internal and external funds. As a result, the sensitivity of firms’ investment outlays to internal cash flows decreases in the presence of institutional investors with long-term investment horizons. Using a sample of 8402 US firms over the period 1981–2008, we provide empirical evidence consistent with these arguments.     

Does Zero Tillage Improve the Livelihoods of Smallholder Cropping Farmers?

The biophysical benefits of zero tillage (ZT) are well documented in the literature. However, the literature on its economic benefits, especially in the context of small and medium‐scale farmers in the temperate developing world is scanty. Using a study of 621 wheat farmers in Syria, we provide empirical evidence on the impacts of adoption of ZT on farm income and wheat consumption. We use propensity score matching (PSM) and endogenous switching regression (ESR) approaches to account for potential selection biases. After controlling for confounding factors, we find that adoption of the ZT technology leads to a US$ 189/ha (33%) increase in net crop income and a 26 kg (34%) gain in per capita wheat consumption per year (adult equivalent) – an indication of meaningful changes in the livelihoods of the farm households. Besides the biophysical and environmental benefits documented elsewhere, our results suggest that adoption of ZT can also be justified on economic and food security grounds. Therefore, ZT can have sizeable impacts in transforming the agricultural sector in the temperate developing world provided that the technology is well promoted and adopted.     

Comparing the Forecasting Performance of Futures Oil Prices with Genetically Evolved Neural Networks

In this paper, a hybrid system combining neural networks and genetic training is designed to forecast future oil prices. The architectural design is that of the multilayer back propagation network that is fed monthly prices for West Texas Intermediate covering the period 1986–2014. The model’s predictions are compared to those of the one, two, three, and four-month futures prices and are evaluated both on their level of accuracy as well as correctness. While accuracy measures the degree of error, correctness tests the model’s ability to predict the direction of the movement. By processing information more efficiently, and identifying patterns that may be ill-defined as a result of pronounced price volatility, this paper aims to improve the accuracy of oil price forecasts.     

ROBUST UTILITY MAXIMIZATION IN NONDOMINATED MODELS WITH 2BSDE: THE UNCERTAIN VOLATILITY MODEL

The problem of robust utility maximization in an incomplete market with volatility uncertainty is considered, in the sense that the volatility of the market is only assumed to lie between two given bounds. The set of all possible models (probability measures) considered here is nondominated. We propose studying this problem in the framework of second‐order backward stochastic differential equations (2BSDEs for short) with quadratic growth generators. We show for exponential, power, and logarithmic utilities that the value function of the problem can be written as the initial value of a particular 2BSDE and prove existence of an optimal strategy. Finally, several examples which shed more light on the problem and its links with the classical utility maximization one are provided. In particular, we show that in some cases, the upper bound of the volatility interval plays a central role, exactly as in the option pricing problem with uncertain volatility models.     

TYPES OF FARMING IN NORTH WALES*

This paper presents the results of a classification of farming undertaken for the four counties of North Wales.? Essentially a type-of-farming classification results in the subdivision of a large area into regions within which a relatively homogeneous association of factors influence the prevailing types of agriculture. Ideally, a type-of-farming area would consist of all that territory within which the choice of products and the possible combinations of factors tend to be similar. The nature of any classification will depend, however, on the main purpose for which it is made. In the present study this was to group parishes according to the type of agricultural activity occurring within them. A further objective was to map the location of the different farming types and thereby to define the predominant type-of-farming areas in North Wales. Among other uses of this classification is an attempt to calculate the contribution to production of each type of farming in the area.     

Roads out of poverty? Assessing the links between aid,public investment,growth, and poverty reduction

This paper presents a dynamic macroeconomic model that captures key linkages between foreign aid, public investment, growth, and poverty. Public capital is disaggregated into education, core infrastructure, and health. Dutch disease effects associated with aid are accounted for by endogenizing changes in the relative price of domestic goods. The impact of shocks on poverty is assessed through partial elasticities and household survey data. The model is calibrated for Ethiopia and changes in the level of nonfood aid are simulated. The amount by which (nonfood) aid should increase to reach the poverty targets of the Millennium Development Goals is also calculated, under alternative assumptions about the degree of efficiency of public investment.     

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.     

Shrinkage estimation of <Emphasis Type="Italic">P</Emphasis>(<Emphasis Type="Italic">X</Emphasis><<Emphasis Type="Italic">Y</Emphasis>) in the exponential case with common location parameter

We consider the problem of estimating R=P(X<Y) where X and Y have independent exponential distributions with parameters and respectively and a common location parameter . Assuming that there is a prior guess or estimate R₀, we develop various shrinkage estimators of R that incorporate this prior information. The performance of the new estimators is investigated and compared with the maximum likelihood estimator using Monte Carlo methods. It is found that some of these estimators are very successful in taking advantage of the prior estimate available.Acknowledgments. The authors are grateful to the editor and to the referees for their constructive comments that resulted in a substantial improvement of the paper.