Organic Crowding Out? - A Study of Danish Organic Food Demand

Only a handful of studies have estimated organic food demand. These all focus on specific food sub-markets assuming separability from other food consumption. However, consumers typically associate attributes such as e.g. healthiness and environment friendliness with organic variants of most types of food. If such general organic attributes are important for consumer behaviour then separability may not hold and what could be termed organic crowding out might result. In this paper we utilize a unique Danish micro panel where all food demand is registered on a disaggregated level with an organic/non-organic indicator to estimate a general food demand system with organic variants. We clearly reject the usual separability assumption and find that our data is consistent with organic crowding out in the Danish food market. In addition estimation of a general demand system makes calculation of economy wide organic price elasticities and other insights into the structure of organic food demand possible

Beliefs, Doubts and Learning: Valuing Economic Risk

This paper explores two perspectives on the rational expectations hypothesis. One perspective is that of economic agents in such a model, who form inferences about the future using probabilities implied by the model. The other is that of an econometrician who makes inferences about the probability model that economic agents are presumed to use. Typically it is assumed that economic agents know more than the econometrician, and econometric ambiguity is often withheld from the economic agents. To understand better both of these perspectives and the relation between them, I appeal to statistical decision theory to characterize when learning or discriminating among competing probability models is challenging. I also use choice theory under uncertainty to explore the ramifications of model uncertainty and learning in environments in which historical data may be insufficient to yield precise probability statements. I use both tools to reassess the macroeconomic underpinnings of asset pricing models. I illustrate how statistical ambiguity can alter the risk-return tradeoff familiar from asset pricing; and I show that when real time learning is included risk premia are larger when macroeconomic growth is lower than average.

Aversion to ambiguity and model misspecification in dynamic stochastic environments

Preferences that accommodate aversion to subjective uncertainty and its potential misspecification in dynamic settings are a valuable tool of analysis in many disciplines. By generalizing previous analyses, we propose a tractable approach to incorporating broadly conceived responses to uncertainty. We illustrate our approach on some stylized stochastic environments. By design, these discrete time environments have revealing continuous time limits. Drawing on these illustrations, we construct recursive representations of intertemporal preferences that allow for penalized and smooth ambiguity aversion to subjective uncertainty. These recursive representations imply continuous time limiting Hamilton–Jacobi–Bellman equations for solving control problems in the presence of uncertainty.Published versio

Examining macroeconomic models through the lens of asset pricing

Dynamic stochastic equilibrium models of the macro economy are designed to match the macro time series including impulse response functions. Since these models aim to be structural, they also have implications for asset pricing. To assess these implications, we explore asset pricing counterparts to impulse response functions. We use the resulting dynamic value decomposition (DVD) methods to quantify the exposures of macroeconomic cash flows to shocks over alternative investment horizons and the corresponding prices or compensations that investors must receive because of the exposure to such shocks. We build on the continuous-time methods developed in Hansen and Scheinkman (2010), Borovicka et al. (2011) and Hansen (2011) by constructing discrete-time shock elasticities that measure the sensitivity of cash flows and their prices to economic shocks including economic shocks featured in the empirical macroeconomics literature. By design, our methods are applicable to economic models that are nonlinear, including models with stochastic volatility. We illustrate our methods by analyzing the asset pricing model of Ai et al. (2010) with tangible and intangible capital.Asset pricing ; Macroeconomics ; Markov processes

Adaptive Regularization in Neural Network Modeling

. In this paper we address the important problem of optimizing regularization parameters in neural network modeling. The suggested optimization scheme is an extended version of the recently presented algorithm [24]. The idea is to minimize an empirical estimate -- like the cross-validation estimate -- of the generalization error with respect to regularization parameters. This is done by employing a simple iterative gradient descent scheme using virtually no additional programming overhead compared to standard training. Experiments with feed-forward neural network models for time series prediction and classification tasks showed the viability and robustness of the algorithm. Moreover, we provided some simple theoretical examples in order to illustrate the potential and limitations of the proposed regularization framework. 1 Introduction Neural networks are flexible tools for time series processing and pattern recognition. By increasing the number of hidden neurons in a 2-layer architec..

Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!

How does missing data affect our ability to learn signal structures? It has been shown that learning signal structure in terms of principal components is dependent on the ratio of sample size and dimensionality and that a critical number of observations is needed before learning starts (Biehl and Mietzner, 1993). Here we generalize this analysis to include missing data. Probabilistic principal component analysis is regularly used for estimating signal structures in datasets with missing data. Our analytic result suggests that the effect of missing data is to effectively reduce signal-to-noise ratio rather than - as generally believed - to reduce sample size. The theory predicts a phase transition in the learning curves and this is indeed found both in simulation data and in real datasets.Comment: Accepted to ICML 2019. This version is the submitted pape

The optimal legal retirement age in an OLG model with endogenous labour supply

The long run welfare implications of the legal retirement age are studied in a perfect foresight overlapping-generations model where agents live for two periods. Agents’ lifetime is divided between working life and retirement by a legal retirement age controlled by the government whereas agents, besides savings, control the intensive margin or "yearly" labour supply. The legal retirement age is utilized to dampen distortionary effects of payroll taxes and public pension annuities and promote capital accumulation. We show that a social optimal legal retirement age exists and how it depends on whether payroll taxes or benefit annuities ensures budget balance of the PAYG pension system.Optimal legal retirement age; pay-as-you-go-pension systems; overlapping-generations model

The Dynamics of Farm Land Allocation – Short and Long Run Reactions in a Long Micro Panel

This study develops a dynamic multi-output model of farmers’ crop allocation decisions that allows estimation of both short-run and long-run adjustments to a wide array of economic incentives. The method can be used to inform decision-makers about a number of issues including agricultural policy reform and environmental regulation. The model allows estimation of dynamic effects relating to price expectations adjustment, investment lags and crop rotation constraints. Estimation is based on micro-panel data from Danish farmers that includes acreage, output and variable input utilisation at the crop level. Results indicate that there are substantial differences between the shortrun and long-run land allocation behaviour of Danish farmers and that there are substantial differences in the time lags associated with different crops. Since similar farming conditions are found in northern Europe and parts of the USA and Canada, this result may have more general interest.land allocation, crop rotation, system of dynamic equations, micro panel data, GMM

Long Term Risk: An Operator Approach

We create an analytical structure that reveals the long run risk-return relationship for nonlinear continuous time Markov environments. We do so by studying an eigenvalue problem associated with a positive eigenfunction for a conveniently chosen family of valuation operators. This family forms a semigroup whose members are indexed by the elapsed time between payoff and valuation dates. We represent the semigroup using a positive process with three components: an exponential term constructed from the eigenvalue, a martingale and a transient eigenfunction term. The eigenvalue encodes the risk adjustment, the martingale alters the probability measure to capture long run approximation, and the eigenfunction gives the long run dependence on the Markov state. We establish existence and uniqueness of the relevant eigenvalue and eigenfunction. By showing how changes in the stochastic growth components of cash flows induce changes in the corresponding eigenvalues and eigenfunctions, we reveal a long-run risk return tradeoff.