The Paternity of the Price-Quality "Value Map"

In the literature on firm strategy and product differentiation, consumer price-quality trade-offs are sometimes represented using consumer “value maps”. These involve the geometric representation of indifferent price and quality combinations as points along curves that are concave to the “quality” axis. In this paper, it is shown that the value map for price-quality tradeoffs may be derived from a Hicksian compensated demand curve for product quality. The paper provides the theoretical link between analytical methods employed in the existing literature on firm strategy and competitive advantage with the broader body of economic analysis.Value map; competitive advantage; quality; price; strategy

Does Inflation Targeting Matter? A Reassessment

This paper uses a number of identification approaches (using instrumental variables, assumptions about heteroscedasticity and panel fixed effects) to estimate the effect of inflation targeting on inflation. Generally, it finds the effect is small and insignificant.Inflation; Monetary policy

Buying Time 2000: Television Advertising in the 2000 Federal Elections

Summarizes a study of political television advertising in the 2000 federal primaries and elections with a focus on the use of the issue ad loophole to evade campaign finance laws. Questions the standard used to differentiate issue ads from election ads

The Paternity of the Price-Quality "Value Map"

In the literature on firm strategy and product differentiation, consumer price-quality trade-offs are sometimes represented using consumer “value maps”. These involve the geometric representation of indifferent price and quality combinations as points along curves that are concave to the “quality” axis. In this paper, it is shown that the value map for price-quality tradeoffs may be derived from a Hicksian compensated demand curve for product quality. The paper provides the theoretical link between analytical methods employed in the existing literature on firm strategy and competitive advantage with the broader body of economic analysis

The Variational Homoencoder: Learning to learn high capacity generative models from few examples

Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables. To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class. This technique, which we call a Variational Homoencoder (VHE), produces a hierarchical latent variable model which better utilises latent variables. We use the VHE framework to learn a hierarchical PixelCNN on the Omniglot dataset, which outperforms all existing models on test set likelihood and achieves strong performance on one-shot generation and classification tasks. We additionally validate the VHE on natural images from the YouTube Faces database. Finally, we develop extensions of the model that apply to richer dataset structures such as factorial and hierarchical categories.Comment: UAI 2018 oral presentatio