The Purchase of Equipment in Consumer Production of Outdoor Experiences

Consumers use combinations of goods to produce experiences. In order to produce outdoor experiences, consumers include environmental amenities as production factors in addition to purchasable instruments such as equipment. While amenities are not marketed, equipment is. Inspecting consumer purchases of the latter may uncover attitudes towards the former. Purchases of equipment are realized money outlays in a market and constitute lower bounds for the willingness to pay for one factor in outdoor experience production. This study investigates income and demographic effects in the demand for outdoor equipment over time in Norway, and examines the proportion of purchasing households for each year. Results show that equipment is a luxury item, and that it becomes more popular over time. The findings are relevant to policymakers in two ways since use of equipment may entail environmental degradation, but purchases of equipment may reflect a willingness to pay for environmental standards.     

Robust portfolio choice with stochastic interest rates

We determine the optimal investment strategy for an ambiguity-averse investor in a setting with stochastic interest rates. The investor has access to stocks, bonds, and a bank account and he is ambiguous about the expected rate of return of both bonds and stocks. The investor can have different levels of ambiguity aversion about the two types of risky assets. We find that it is more important to take model uncertainty about the stock dynamics than model uncertainty about the bond dynamics into account. Furthermore, the investor’s ambiguity increases his hedging demand. Consequently, the bond/stock ratio increases with his ambiguity and implies less extreme positions in the bank account. Altogether, our model yields portfolio allocations which are more in line with what is implementable in practice. Finally, we demonstrate that neglecting model uncertainty implies significant losses for the investor.     

Influence of Social Media over the Stock Market

This research analyzes investors’ activity through social media and these media's influence over the Chicago Board Options Exchange Market Volatility Index (VIX) using a logit model and a fuzzy‐set qualitative comparative analysis (fsQCA). The logit results show that social media sentiment influences stock markets. Meanwhile, the fsQCA results show that the investors’ profile is important for explaining how social media influence the stock market. Particularly, holding period combined with experience in technical investors contributes to avoiding a raise in market risk, whereas for nontechnical investors message sentiment and experience form the combination that contributes to avoid a raise in market risk.     

Taxing Large Businesses: Cooperative Compliance in Action

Intereconomics - Cooperative compliance represents a shift in thinking for tax administrations, away from a deterrence approach where taxpayers are coerced to comply with tax rules and threatened...     

A review of recent developments and applications of partial equilibrium models of the forest sector

Recent history has seen an increase in the utilization of partial equilibrium based forest sector models to identify potential impacts of various policies or timber market shocks. These models are particularly useful in that they employ economic theory to capture the interaction of supply and demand in a framework where commodity prices are endogenous to the policy or shock simulated. We present recent developments and linkages between models and review applications of these models to forest policy questions over the previous decade. We conclude with a discussion of potential future directions for such research.     

A Performance Comparison Between Cross-Sectional Stochastic Dominance and Traditional Event Study Methodologies

In this study, the performance of cross-sectional stochastic dominance (SD), first proposed by Falk and Levy (FL) (1989), is compared with three traditional event study methodologies: the Mean Adjusted model, the Market Adjusted model, and the Market and Risk Adjusted Returns model. The comparison technique we use is a simulations approach similar to that of Brown and Warner (BW) (1980). BW show that the Mean Adjusted and Market Adjusted Returns models perform as well as the more sophisticated Market and Risk Adjusted Returns model. FL, however, provide a very compelling argument against the three traditional event study methodologies. The problem, they note, is not the theoretical need for risk adjustment; it is the definition and measurement of risk. FL assert that the observed abnormal returns (or lack thereof) may be due to omitted variables, a market proxy effect, or other specification errors in implementing the traditional event study methodologies.The present research finds that SD analysis without the bootstrap method for statistical testing is not very useful at any level of abnormal return. However, when the bootstrap method of statistical testing is employed, SD is found to perform as well as, and sometimes better than, the three traditional models in detecting simulated abnormal performance at all test levels. The results are consistent with FL's assertion that the improved performance may result from the SD methodology being free from the specification errors inherent in the three traditional event study models.