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.     

The Global Financial Crisis,Family Control,and Dividend Policy

Using newly collected data on the ultimate ownership structure of publicly traded firms in nine East Asian economies, we find that family control is negatively related to the dividend payout ratio. Family firms are less (more) likely to increase (omit) dividends than non‐family firms. These negative associations between family firms and dividend policy are more pronounced during the recent global financial crisis, suggesting that controlling families have incentives to expropriate more firm resources during crises than in normal times.     

Research output and economic growth in G7 countries: new evidence from asymmetric panel causality testing

Recent studies have shown increasing interest on the relationship between research output and economic growth. The study of such a relationship is not only of theoretical interest, but it can also influence specific policies to improve the quality, and probably the quantity of research output. This article has studied this relationship in G7 countries using the asymmetric panel causality test of Hatemi-J (2011). Our results show that only the UK shows a causal relationship from the output of research to real GDP. However, when the signs of variations are taken into account, there is an asymmetric causality running from negative research output shocks to negative real GDP shocks.     

Corporate Social Responsibility and Credit Ratings

This study provides evidence on the relationship between corporate social responsibility (CSR) and firms’ credit ratings. We find that credit rating agencies tend to award relatively high ratings to firms with good social performance. This pattern is robust to controlling for key firm characteristics as well as endogeneity between CSR and credit ratings. We also find that CSR strengths and concerns influence credit ratings and that the individual components of CSR that relate to primary stakeholder management (i.e., community relations, diversity, employee relations, environmental performance, and product characteristics) matter most in explaining firms’ creditworthiness. Overall, our results suggest that CSR performance conveys important non-financial information that rating agencies are likely to use in their evaluation of firms’ creditworthiness, and that CSR investments—particularly those that extend beyond compliance behavior to reflect what is desired by society—can lead to lower financing costs resulting from higher credit ratings.     

A comparison of treatment patterns,healthcare resource utilization,and costs among young adult Medicaid beneficiaries with schizophrenia treated with paliperidone palmitate or oral atypical antipsychotics in the US

Abstract

Background: Much of the burden associated with schizophrenia is attributed to its early onset and chronic nature. Treatment with once monthly paliperidone palmitate (PP1M) is associated with lower healthcare utilization and better adherence as compared to oral atypical antipsychotics (OAAs). This study aimed to evaluate real-world effectiveness of PP1M and OAA therapies among US-based adult Medicaid patients with schizophrenia, overall and among young adults aged 18–35 years.

Methods: Adult patients with a diagnosis of schizophrenia and at least two claims for PP1M or OAA between January 1, 2010 and December 31, 2014 were selected from the IBM Watson Health MarketScan Medicaid Database. Treatment patterns and healthcare resource utilization and costs were compared between PP1M and OAA treatment groups following inverse probability of treatment (IPT) weighting to adjust for potential differences. Utilization and cost outcomes were estimated using OLS and weighted Poisson regression models.

Results: After IPT weighting, the young adult PP1M and OAA cohorts were comprised of 3,095 and 3,155 patients, respectively. PP1M patients had a higher duration of continuous treatment exposure (168.2 vs 132.5 days, p?=?.004) and better adherence on the index medication (proportion of days covered ≥80%: 19.0% vs 17.1%, p?<?.049). Young adults treated with PP1M were 37% less likely to have an all-cause inpatient admission (odds ratio [OR]?=?0.63, 95% confidence interval [CI]?=?0.53–0.74) and 33% less likely to have an ER visit (OR?=?0.67, 95% CI?=?0.55–0.81) compared to OAA young adult patients, but 27% more likely to have an all-cause outpatient office visit (OR?=?1.27, 95% CI?=?1.02–1.56). PP1M patients incurred significantly lower medical costs as compared to OAA patients.

Conclusions: Medicaid patients with schizophrenia treated with PP1M have higher medication adherence and have fewer hospitalizations as compared to patients treated with OAAs. PP1M may lead to reduced healthcare utilization and improved clinical outcomes.     

Minimum wage is efficient wage in Turkish labor market: TAR–cointegration analysis

This study has two main purposes. First one is the necessity of taking minimum wages into account, if there is a purpose to analyze the relationship between wages and productivity in an economy which has high unemployment rates and informal employment. Second one is about the analyzing method of this relationship. We choose TAR cointegration analysis for this relation. First step of this analysis is testing for stationarity of the variables. However the low power of traditional unit root tests is examined and proved in many studies but not taken into account in TAR cointegration studies in literature. This study shows that traditional unit root tests are unfavorable for the variables which have TAR structures. Because of this shortcoming of traditional unit root tests, these results must be supported with TAR unit root tests.     

Macroeconomic expectations and time varying heterogeneity:evidence from individual survey data

ABSTRACT

The goal of this paper is to investigate forecast heterogeneity and time variability in the formation of expectations using disaggregated monthly survey data on macroeconomic indicators provided by Bloomberg from June 1998 to August 2017. We show that our panel of forecasters are not rational and are moderately heterogeneous and thus confirm that previously well-established results on asset prices hold for macroeconomic indicators. We propose a flexible hybrid forecast model defined at any time as a combination of the extrapolative, regressive, adaptive and interactive heuristics. Controlling for endogenous structural breaks, we find that experts adjust their forecast behaviour at any time with some inertia in extrapolative and adaptive profiles. Changes in the formation of expectations are triggered mostly by financial shocks, and uncertainty is dealt with by using complex processes in which the fundamentalist component overweighs chartist activity. Forecasters whose models combine different relevant rules and display high temporal flexibility provide the most accurate forecasts. Authorities can then stabilize the domestic markets by encouraging fundamentalists’ forecasts through increased transparency policy.