Factors influencing hedging decision: evidence from Brazilian citrus growers

The purpose of this study was to analyse the hedging behaviour of 98 citrus growers from the State of Sao Paulo, Brazil. Marketing behaviour was modelled as a choice between spot market, short and long‐term forward contracts. A multinomial logistic regression model was used to evaluate the role of behavioural, personal and managerial variables in the choice. Results indicated that the factors which explain the use of forward contracts by citrus growers are the following: risk propensity; trade with juice processing companies; farming diversification; overconfidence in management; participation in pools; use of management tools; and technical assistance. The results can be useful for farmers, policymakers, government agencies, traders and extension agents.     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

Key Factors that Influence Consumer Preferences for Natural-Hyped Products

Natural-hyped products are receiving greater attention from and acceptance by consumers worldwide. Environmental factors that foster the demand for natural-hyped products, specifically hemp-based products include the deregulation of the cannabis industry and greater consumer desire for natural foods. Adding to this, the strategic use of stimulant type of cues (e.g., a cannabis leaf) included in product logos, ads, and packaging, seems to create hype associations when evaluating hemp-based products. In this context, this study presents empirical evidence (three experiments and two qualitative studies) that illustrates consumer preference for hemp-based products over ones that do not include hemp as an ingredient (hemp-free). The research focuses on identifying the psychological determinant that orients consumers towards hemp-based products. Findings suggest that the perceived naturalness is the psychological mechanism behind consumers positive evaluation of hemp-based products. Moreover, this study presents evidence that this evaluation is enhanced by the consumer's need for stimulation. Implications of the findings for the role of perceived naturalness and the need for stimulation in marketing strategies are discussed.     

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.     

Interpretable machine learning for real estate market analysis

Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle.     

Introduction to the special issue on the 24th annual Workshop on Economic science with Heterogeneous Interacting Agents,London, 2019 (WEHIA 2019)

Journal of Economic Interaction and Coordination -