Using R to teach econometrics

R, an open‐source programming environment for data analysis and graphics, has in only a decade grown to become a de‐facto standard for statistical analysis against which many popular commercial programs may be measured. The use of R for the teaching of econometric methods is appealing. It provides cutting‐edge statistical methods which are, by R's open‐source nature, available immediately. The software is stable, available at no cost, and exists for a number of platforms, including various flavours of Unix and Linux, Windows (9x/NT/2000), and the MacOS. Manuals are also available for download at no cost, and there is extensive on‐line information for the novice user. This review focuses on using R for teaching econometrics. Since R is an extremely powerful environment, this review should also be of interest to researchers. Copyright © 2002 John Wiley & Sons, Ltd.     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.     

GOVERNANCE AND CHARITIES: AN EXPLORATION OF KEY THEMES AND THE DEVELOPMENT OF A RESEARCH AGENDA

The concept of governance has been widely discussed in both the business and non-business sectors. The debate has also been entered into within the charity sector, which comprises over 169,000 organizations in the UK. The UK-based Charity Commission, which describes itself as existing to 'promote sound governance and accountability', has taken a lead in this debate by promoting greater regulation and producing numerous recommendations with regard to the proper governance of charitable organizations. However, the concept of what is meant by governance is unclear and a myriad of ideas are placed under the umbrella of 'good governance'. This paper explores the major themes that form the basis of much of this discussion, examining both the theoretical underpinnings and empirical investigations relating to this area (looking from the perspective of the key stakeholders in the charity sector). Based on an analysis of the extant literature, this paper presents a broad definition of governance with respect to charities and outlines a future research agenda for those interested in adding to knowledge in this area     

An experiment of social learning with endogenous timing

In this paper, we experimentally investigate a social learning model with endogenous timing. Specifically, we focus on a model, in which two subjects are supposed to make a binary decision. One alternative is a safe action with a fixed payoff, while the other alternative is a risky action. The subjects can make their decisions in three stages. The safe action is reversible, but the risky action is not. A subject who delays his decision can observe the decision of the other subject in the earlier stages, and as a result, acquire more information. We show that players do delay their decisions in order to obtain more information. Furthermore, they delay especially when their private information does not particularly support the risky action. We also find evidence which suggests that risk aversion plays an important role in timing decisions, often leading to ex post inefficient outcomes.     

Modern Strategies for Time Series Regression

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.     

Accounting for the Public Sector at a Time of Crisis

This Special Issue is based on papers initially presented at the ‘Accounting for the Public Sector at a Time of Crisis’ Conference at the Centre for Not-for-profit and Public-sector Research, Queen's University Belfast, UK in 2018. The public sector consists of organizations that are owned and operated by government; organizations that exist to provide goods and services for a country's citizens. What is particularly distinctive about such organizations, and what makes them different from businesses, is that they are (or, at least perhaps, should be) not-for-profit. In addition, they frequently have wide social and cultural goals that are central to what they do. They are pervasive in most societies. Yet, it is argued, they face crisis on a number of fronts: in terms of the influence of potentially inappropriate, business-like new public management ideas related to performance management and the embracing of related accounting and budgeting approaches; and in terms of the impact of austerity, following the Great Recession in 2008. In such a context it is suggested that public sector governance, accounting, and accountability systems are heavily involved. The papers included in this Special Issue present an opportunity to reflect on aspects of this crisis in terms of how it connects with accounting systems and accounting changes. Key arguments of these papers, and overarching themes of this Special Issue, are explored in this editorial.     

Forecast reconciliation: A geometric view with new insights on bias correction

A geometric interpretation is developed for so-called reconciliation methodologies used to forecast time series that adhere to known linear constraints. In particular, a general framework is established that nests many existing popular reconciliation methods within the class of projections. This interpretation facilitates the derivation of novel theoretical results. First, reconciliation via projection is guaranteed to improve forecast accuracy with respect to a class of loss functions based on a generalised distance metric. Second, the Minimum Trace (MinT) method minimises expected loss for this same class of loss functions. Third, the geometric interpretation provides a new proof that forecast reconciliation using projections results in unbiased forecasts, provided that the initial base forecasts are also unbiased. Approaches for dealing with biased base forecasts are proposed. An extensive empirical study of Australian tourism flows demonstrates the theoretical results of the paper and shows that bias correction prior to reconciliation outperforms alternatives that only bias-correct or only reconcile forecasts.