Investigating the failure to best respond in experimental games

Experimental Economics - We examine strategic sophistication using eight two-person 3?×?3 one-shot games. To facilitate strategic thinking, we design a ‘structured’...     

Modeling time-varying higher-order conditional moments: A survey

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model, the literature on modeling the time-varying second-order conditional moment has become increasingly popular in the last four decades. Its popularity is partly due to its success in capturing volatility in financial time series, which is useful for modeling and predicting risk for financial assets. A natural extension of this is to model time variation in higher-order conditional moments, such as the third and fourth moments, which are related to skewness and kurtosis (tail risk). This leads to an emerging literature on time-varying higher-order conditional moments in the last two decades. This paper outlines recent developments in modeling time-varying higher-order conditional moments in the economics and finance literature. Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) framework as a foundation, this paper provides an overview of the two most common approaches for modeling time-varying higher-order conditional moments: autoregressive conditional density (ARCD) and autoregressive conditional moment (ARCM). The discussion covers both the theoretical and empirical aspects of the literature. This includes the identification of the associated skewness–kurtosis domain by using the solutions to the classical moment problems, the structural and statistical properties of the models used to model the higher-order conditional moments and the computational challenges in estimating these models. We also advocate the use of a maximum entropy density (MED) as an alternative method, which circumvents some of the issues prevalent in these common approaches.     

A pulse check on recent developments in time series econometrics

This article motivates and summarizes the contributions of the special issues on Recent Developments in Time Series Econometrics.     

Green Alliances: Are They Beneficial when Regulated Firms are Asymmetric?

Journal of Industry, Competition and Trade - In this paper, we analyze the collaboration between an environmental group (EG) and polluting firms when they are asymmetric in their abatement costs....     

Best practices for implementing experimental research methods

Experimental research methods have a long history across a number of different disciplines—including consumer research. Although experiments are just one of many alternative research methods, experiments are notable because they are the best way to establish causation. This makes experiments a powerful tool when researchers need to show cause and effect relationships. In this article, we provide best practices for implementing experimental research methods in consumer studies. Specifically, we discuss several important topics researchers need to consider when designing experiments, including developing hypotheses, operationalizing the variables (manipulated or measured), deciding on the research design (between-subjects, within-subjects, or mixed), selecting the research setting (laboratory, field, or online), understanding the main effect (via moderation, mediation, or moderated mediation), including manipulation and attention checks, determining the sample size, and choosing participants. We provide recommendations that researchers can use to conduct high-quality experiments in a consumer context.     

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.     

New perspectives on the contribution of sanitary investments to mortality decline in English cities, 1845–1909

Health improved in English cities in the last third of the nineteenth century, in tandem with substantial increases in public spending on water supplies and sanitation. However, previous efforts to measure the contribution of public expenditures to mortality improvements have been hampered by difficulties in quantifying public health investments and the lack of mortality data for specifically urban populations. We improve upon the existing evidence base by (1) creating measures of the stock of urban district sanitary capital, by type, on the basis of capital expenditure flows, rather than loan stocks; (2) using mortality and capital stock data that relate to the same administrative units (urban districts), and (3) studying the period 1880–1909 as well as the earlier period from 1845. The stock of sewerage capital was robustly related to improvements in all-cause mortality after 1880. The size of this effect varied with the extent of public investment in water supplies, suggesting complementarity between the two assets. For the period 1845–84, investments in water were associated with declines in infant and child mortality but the effect was much smaller and less precisely estimated in later decades. Our results suggest that improvements in water and sewerage targeted different transmission pathways for faecal–oral diseases.     

Drivers of autonomous vehicles—analyzing consumer preferences for self-driving car brand extensions

Autonomous cars are considered to be the next disruptive innovation that will affect consumers. It can be expected that not only traditional automakers will enter this market (e.g., Ford) but also technology companies (e.g., Google) and newer companies dedicated to self-driving cars (e.g., Tesla). We take a brand extension perspective and analyze to what extent consumers prefer autonomous cars from these brand categories. Our empirical study is based on discrete choice experiments about adopting autonomous vehicles in a purchase scenario and in a renting context. Our findings show that brands play a central role when making autonomous driving decisions. Brand preferences differ systematically when buying versus renting a self-driving car. While technology brands are most preferred overall, consumers favor automaker brands over new brands only when purchasing, not when renting. We further disentangle the brand strength into the marginal effects of image associations. For example, Google’s strong brand positioning can be explained by experiences with the parent brand, but it could still improve brand strength by highlighting the relevance of the associated brand portfolio for self-driving cars. The effect of these brand extension success factors differs between parent-brand categories and also between the renting and purchasing scenarios, which requires a dedicated brand management.