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Whether changes in animal behavior allow for short‐term earthquake predictions has been debated for a long time. Before, during and after the 2016/2017 earthquake sequence in Italy, we deployed bio‐logging tags to continuously observe the activity of farm animals (cows, dogs, and sheep) close to the epicenter of the devastating magnitude M6.6 Norcia earthquake (Oct–Nov 2016) and over a subsequent longer observation period (Jan–Apr 2017). Relating 5,304 (in 2016) and 12,948 (in 2017) earthquakes with a wide magnitude range (0.4 ≤ M ≤ 6.6) to continuously measured animal activity, we detected how the animals collectively reacted to earthquakes. We also found consistent anticipatory activity prior to earthquakes during times when the animals were in a building (stable), but not during their time on a pasture. We detected these anticipatory patterns not only in periods with high, but also in periods of low seismic activity. Earthquake anticipation times (1–20 hr) are negatively correlated with the distance between the farm and earthquake hypocenters. Our study suggests that continuous bio‐logging of animal collectives has the potential to provide statistically reliable patterns of pre‐seismic activity that could yield valuable insights for short‐term earthquake forecasting. Based on a priori model parameters, we provide empirical threshold values for pre‐seismic animal activities to be used in real‐time observation stations. [ABSTRACT FROM AUTHOR]

The presence of weak instruments is translated into a nearly singular problem in a control function representation. Therefore, the [ABSTRACT FROM AUTHOR]

Zöller et al. (Ethology, 2020) criticize our original publication (Wikelski et al., Ethology, 126(9), 2020, 931) for obvious reasons: we only observed the behavior of one group of farm animals before, during and after one earthquake series in one area of the world. It is clear that no earthquake predictions are possible, and should not be attempted, from this data set. However, what we show is that there is important information within this animal collective pertaining to potential future local forecasting of earthquakes when combined with traditional data sources. We maintain that combining Zöller et al.'s (2020) modeling tools with the adequate use of our data can stimulate novel ways of earthquake forecasting. Future studies should combine both approaches. [ABSTRACT FROM AUTHOR]

In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain ℤ, n ∈ ℕ. Our modelling framework is based on a copula approach and can be used for a broad set of applications where the primary characteristics of the data are: (i) discrete domain; (ii) the tendency to cluster at certain outcome values; and (iii) contemporaneous dependence. These kinds of properties can be found for high- or ultra-high-frequency data describing the trading process on financial markets. We present a straightforward sampling method for such an inflated multivariate density through the application of an independence Metropolis-Hastings sampling algorithm. We demonstrate the power of our approach by modelling the conditional bivariate density of bid and ask quote changes in a high-frequency setup. We show how to derive the implied conditional discrete density of the bid-ask spread, taking quote clusterings (at multiples of 5 ticks) into account. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Extends literature on labor utilization and nonwage labor costs to incorporate approaches to macroeconomic disequilibrium modeling based on the 'smoothing by aggregation' principle. Reformulation of the basic Sneessens-Dreze type model; Treatment of employment and working hours as separate inputs; Evidence from model simulation.