A Tutorial on Palm Distributions for Spatial Point Processes

This tutorial provides an introduction to Palm distributions for spatial point processes. Initially, in the context of finite point processes, we give an explicit definition of Palm distributions in terms of their density functions. Then we review Palm distributions in the general case. Finally, we discuss some examples of Palm distributions for specific models and some applications.     

Pure meat – Public perceptions of risk reduction strategies in meat production

Experience has shown that both the assessment and implementation of new technologies in food production are challenged by negative assessments of the technologies by the public. This article seeks to deepen our understanding of the concerns that may underlie negative attitudes to various kinds of risk reduction strategy in meat production, with the aim of distinguishing between forms of risk reduction in terms of their acceptability. The paper reports the result of a focus-group study. Six focus groups with Danish citizens (N: 5–9) were conducted during May 2006. The design of the groups took a bottom-up approach and included elements of meat quality, meat safety and risk reduction strategies. The study shows the dilemma risk reduction presents to members of the public. On the one hand, people want safe meat; on the other, the study showed that with the exception of hygiene practices, people generally have an aversion to risk reduction strategies. Some variation was found, however, in the rejection of the strategies. Thus, more acceptable strategies are characterised by a low degree of technological interference, and by being close to the consumer’s experience in everyday life and/or familiar to the participants’ picture of meat production. It is also important that the strategy does not alter the quality of the end-product (meat) in an unfavourable way. The implications of the results and the inherent dilemma for meat safety policy formation are discussed.     

Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model

We extend the fractionally integrated exponential GARCH (FIEGARCH) model for daily stock return data with long memory in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH-M) effect in the return equation. The filtering of the volatility-in-mean component thus allows the co-existence of long memory in volatility and short memory in returns. We present an application to the daily CRSP value-weighted cum-dividend stock index return series from 1926 through 2006 which documents the empirical relevance of our model. The volatility-in-mean effect is significant, and the FIEGARCH-M model outperforms the original FIEGARCH model and alternative GARCH-type specifications according to standard criteria.     

A Model for Studying the Effect of EMU on European Yield Curves

In January 1999, the European monetary union (EMU) was formally launched with 11 member countries. However, before May 1998 there was considerable uncertainty about who would join EMU, and whether the project would start on time. When a monetary union is formed, exchange rates between the member countries are irrevocably fixed, and yield spreads stemming from exchange-rate risk are eliminated. As a direct consequence, EMU affected the prices of long-term bonds well before 1999, but quantifying this effect can be difficult when there is uncertainty about the monetary union. We address these issues and develop a bond-pricing model which explicitly takes into account that a country may join a monetary union at a future, unspecified date. The empirical results show that a narrow EMU, consisting of Germany, France and the Benelux countries, has been priced with almost 100% probability throughout the period 1995–1998, whereas, on average, the implied probability of joining EMU has been somewhat lower for the other EU countries. However, in the period leading up to May 1998, the estimated probabilities have increased considerably for the countries that joined EMU in January 1999.     

An environmental performance index for products reflecting damage costs

Several studies have demonstrated how to use DEA (Data Envelopment Analysis) based techniques to estimate environmental performance indices. None of these studies, however, are taking information on the environmental damage costs of the pressure types considered into account. This study is bridging a gap between environmental indices founded in physical pressures and damage costs founded in welfare economics. The aim of the paper is twofold: first, to demonstrate how to implement the information on environmental damage costs within a DEA based environmental performance index, and second, to estimate these indices at product level by using Danish input-output data and environmental data from 1997.     

Output and expected returns

This paper shows for 1929–2003 U.S. data and also for international G-7 data that the ratio of share prices to GDP tracks a large fraction of the variation over time in expected returns on the aggregate stock market, capturing more of that variation than do price–earnings and price–dividend ratios and often also providing additional information about excess returns. The price–output ratio tracks long-term U.S. cumulative stock returns almost as well as the cay-ratio of Lettau and Ludvigson [2001a. Journal of Finance 56, 815–849, 2005. Journal of Financial Economics 76, 583–626], although the cay-ratio tracks variation in U.S. excess returns better. The price–output ratio, however, involves no parameter estimation and is easily constructed for non-U.S. countries.     

Exploring interactions between real activity and the financial stance

In this paper we empirically study interactions between real activity and the financial stance. Using aggregate data we examine a number of candidate measures of the financial stance of the economy. We find strong evidence for substantial spillover effects on aggregate activity from our preferred measure. Given this result, we use a large micro-data set for corporate firms to develop a macro–micro-model of the interaction between the financial and real economy. This approach implies that the impulse responses of a given aggregate shock will depend on the portfolio structure of firms at any given point in time.     

The long-run determinants of inequality: What can we learn from top income data?

This paper studies determinants of income inequality using a newly assembled panel of 16 countries over the entire twentieth century. We focus on three groups of income earners: the rich (P99–100), the upper middle class (P90–99), and the rest of the population (P0–90). The results show that periods of high economic growth disproportionately increases the top percentile income share at the expense of the rest of the top decile. Financial development is also pro-rich and the outbreak of banking crises is associated with reduced income shares of the rich. Trade openness has no clear distributional impact (if anything openness reduces top shares). Government spending, however, is negative for the upper middle class and positive for the nine lowest deciles but does not seem to affect the rich. Finally, tax progressivity reduces top income shares and when accounting for real dynamic effects the impact can be important over time.     

Bayesian estimation of an open economy DSGE model with incomplete pass-through

In this paper, we develop a dynamic stochastic general equilibrium (DSGE) model for an open economy, and estimate it on Euro area data using Bayesian estimation techniques. The model incorporates several open economy features, as well as a number of nominal and real frictions that have proven to be important for the empirical fit of closed economy models. The paper offers: i) a theoretical development of the standard DSGE model into an open economy setting, ii) Bayesian estimation of the model, including assessments of the relative importance of various shocks and frictions for explaining the dynamic development of an open economy, and iii) an evaluation of the model's empirical properties using standard validation methods.