Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset

Many recent papers have found that atheoretical forecasting methods using many predictors give better predictions for key macroeconomic variables than various small-model methods. The practical relevance of these results is open to question, however, because these papers generally use ex post revised data not available to forecasters and because no comparison is made to best actual practice. We provide some evidence on both of these points using a new large dataset of vintage data synchronized with the Fed's Greenbook forecast. This dataset consists of a large number of variables, as observed at the time of each Greenbook forecast since 1979. Thus, we can compare real-time large dataset predictions to both simple univariate methods and to the Greenbook forecast. For inflation we find that univariate methods are dominated by the best atheoretical large dataset methods and that these, in turn, are dominated by Greenbook. For GDP growth, in contrast, we find that once one takes account of Greenbook's advantage in evaluating the current state of the economy, neither large dataset methods nor the Greenbook process offers much advantage over a univariate autoregressive forecast.

Some competition and consumer issues in the Indigenous visual arts industry

This paper focuses on how competition and consumer protection issues might be relevant to the Indigenous visual arts industry. The structure of the industry is complex: the majority of producers reside in remote localities; there are a variety of functional levels; the industry encompasses both the 'fine' and 'tourist' art markets and includes works made in collaboration with non-Indigenous people. There is an emphasis in the paper on government funded community art centres which collect, document and market Indigenous art. However, the objectives of most art centres are mixed and the roles that they play in remote communities extend well beyond these tasks. Perhaps most importantly, art centres act as cultural mediators between artists and the market. If they are to act in the artists' best interests, art centres may operate most effectively as monopolies. This is primarily because of market failure associated with remoteness, their small size, dispersed artist populations and the poor track record of private dealers. Notwithstanding the fact that a few art centres have exclusive access to some geographically defined art styles, the nature of competition within the industry appears healthy. However, there is concern about competition from imported as well as locally produced 'fakes' in the tourist market. Authorship and issues of authenticity emerge as considerations with potential for future industry impacts, though labelling and other documentation strategies by art centres and other outlets have improved markedly in recent years. Those sections of the Trade Practices Act 1974 that may be relevant to the industry include unconscionable conduct, no and misleading representation and coercion or harassment. A significant amount of anecdotal evidence has emerged in the course of research in relation to the unethical practices of some private dealers. The production of a producer and consumer education charter is put forward as a strategy which may be of benefit to this growing industry

Credit Spreads as Predictors of Real-Time Economic Activity: A Bayesian Model-Averaging Approach

Employing a large number of real and financial indicators, we use Bayesian Model Averaging (BMA) to forecast real-time measures of economic activity. Importantly, the predictor set includes option-adjusted credit spread indexes based on bond portfolios sorted by maturity and credit risk as measured by the issuer’s “distance-to-default.” The portfolios are constructed directly from the secondary market prices of outstanding senior unsecured bonds issued by a large number of U.S. corporations. Our results indicate that relative to an autoregressive benchmark, BMA yields consistent improvements in the prediction of the growth rates of real GDP, business fixed investment, industrial production, and employment, as well as of the changes in the unemployment rate, at horizons from the current quarter (i.e., “nowcasting”) out to four quarters hence. The gains in forecast accuracy are statistically significant and economically important and owe exclusively to the inclusion of our portfolio credit spreads in the set of predictors—BMA consistently assigns a high posterior weight to models that include these financial indicators.

Identifying the Effects of Monetary Policy Shocks on Exchange Rates Using High Frequency Data

This paper proposes a new approach to identifying the effects of monetary policy shocks in an international vector autoregression. Using high-frequency data on the prices of Fed Funds futures contracts, we measure the impact of the surprise component of the FOMC-day Federal Reserve policy decision on financial variables, such as the exchange rate and the foreign interest rate. We show how this information can be used to achieve identification without having to make the usual strong assumption of a recursive ordering.

Identifying the effects of monetary policy shocks on exchange rates using high frequency data

This paper proposes a new approach to identifying the effects of monetary policy shocks in an international vector autoregression. Using high-frequency data on the prices of eurodollar contracts, we measure the impact of the surprise component of the FOMC-day Federal Reserve policy decision on financial variables, such as the exchange rate and the foreign interest rate. We show how this information can be used to achieve identification without having to make the usual strong assumption of a recursive ordering. JEL Classification: C32, E52, F30Exchange Rates, High Frequency Data, Identification, monetary policy, Vector autoregression

Active Learning Using Student Recorded Monologues

This paper will detail the planning, execution and feedback of an initiative to promote active learning and improve student speaking and writing using student recorded monologues. After determining the goals of the project, a rubric was created to clarify these goals and assessment criteria for both students and instructors. A 15-lesson unit was then developed based on four personal narratives. For each narrative, students completed a series of preparation activities before producing both written and spoken versions of their narratives and uploading these in the form of online assignments. Students were encouraged to review the rubric before, during, and after work on their texts. Feedback from students and instructors on completion of the program indicated that the goals were clear and relevant, there was a high level of student engagement with the unit, and that the rubric was an effective tool for guiding the learning process

Evaluating the influence of venue on experiential, project-based learning

Experiential, project-based learning is widely endorsed as an effective pedagogy for enhancing the understanding of theory, increasing critical thinking skills, and preparing students for future roles as contributing employees. Likewise, the use of online classes at universities is becoming popular. A logical evolution of course venues would be the appearance of project-based, online courses. However, there is scant research on whether online, experiential learning is as effective as face-to-face courses. In this study, comparisons are made between online and face-to-face classes of a project-based, strategic management course at a regional university. No statistically significant differences were found in the course measurements of student grades, teammate evaluations, or client project evaluations

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