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Many economic and business problems require a set of random variates from the posterior density of the unknown parameters. The set of random variates can be used to integrate numerically many forms of functions. Since a closed form of the posterior density of models in time series analysis is not usually well known, it is not easy to generate a set of random variates. As a sampling scheme based on the probabilities proportional to sizes of the sample space, sampling importance resampling (SIR) method can be applied to generate a set of random variates from the posterior density. Application of SIR to signal extraction model of time series analysis is illustrated and given a set of random variates, the procedures to compute the Monte Carlo estimator of the component of signal extraction model are discussed. The procedures are illustrated with simulated data.

This chapter synthesizes two complementary streams in the economic thought of David M. Gordon, and explores their shared relevance to the rise of the “gig” economy in modern economies. Gordon made lasting contributions to the radical political-economic analysis of work and employment. At the microeconomic level of individual workplaces, he and his collaborators originally explained the factors affecting employers’ labor extraction strategies, through which they seek maximum work effort from waged employees while minimizing unit labor costs. At the macroeconomic or structural level, he linked that conflictual process to the broader institutional and structural features of the overall accumulation regime which is essential to any successful incarnation of capitalism. Employment practices and social structures have evolved considerably since Gordon’s passing, but his insights are still useful in understanding the rise of, and limits to, modern work arrangements. In particular, Gordon’s dual portrayal of the parameters of labor extraction, and their positioning within a broader structural and institutional context, provides a convincing explanation of both the recent rise of gig economy practices, and their potential limits.

Cleats are considered one of the significant permeability-related parameters in coalbed methane (CBM) growth. As critical parameters for CBM extraction, a complete characterisation of cleat distributions and orientation can provide a better tool to indirectly estimate porosity and permeability in coal reservoirs. This chapter presents the outcomes of the production of comprehensive research cleats within Miocene coal seams as part of CBM exploration and development. The majority of data (cross-section view measurement) were collected on mine’s walls. Cleat data were gathered from 16 windows measurement locations with hundreds of cleats were measured from outcrops for several coal seams. Two primary cleat orientations; for face cleats, NNE-SSW and for butt cleats, ESE-WNW. The ratio of low permeability coals appears to have a smaller cleat aperture than high permeability coals. As critical parameters for CBM extraction, a complete characterisation of cleat distributions and orientation can provide a better tool to indirectly estimate porosity and permeability in coal reservoirs.

This chapter focuses on whether perceived emotional intensity and help need is possible to discriminate in expressions of fear and neutrality in brief authentic emergency calls. Extraction of acoustic parameters of fear and neutrality was done prior to letting participants listen to a low-pass-filtered stimuli set. Participants discriminated fear and neutrality in both the intensity and help need condition. In turn, judged intensity and judged help need correlated strongly, with partial correlations indicating that participants use acoustically measured intensity (mean dB) as information to infer the intensity/help need relationship. We also discuss the implications of emotional expression in the call centre domain.

Climate change will affect tourism at several temporal and spatial levels. This chapter focuses on the quantification of effects and the development of strategies to reduce extremes and frequencies as well as thresholds in tourism areas. Knowledge about possibilities for mitigation and adaptation of current and expected climate conditions requires interdisciplinary approaches and solutions. Several examples are presented, including the effects of trees against climate change and extreme events (heat waves), behavior adaptations, urban and regional planning measures, bioclimatic conditions in the Mediterranean and human–biometeorological conditions under climate change conditions, and user-friendly computer tools for the quantification of urban bioclimate conditions.

In the context of Dynamic Factor Models, we compare point and interval estimates of the underlying unobserved factors extracted using small- and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the maximum likelihood Kalman filter and smoother factor estimates are highly correlated with those obtained using hybrid procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

The authors analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measurements. The authors study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators (MLE) of the dynamics parameters and reductions in the precision of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. The authors also develop an R² measure of common trend observability that determines the severity of misspecification. Finally, the authors apply their framework to US quarterly data on GDE and GDI, obtaining an improved aggregate output measure.

We present a computable general equilibrium model of the interface between the Great Salt Lake (GSL) ecosystem and both the international and regional economy that impacts the ecosystem. International trade is accounted for in the simplest of terms, involving the export of each of the ecosystem's main commodities and importation of a composite good, as well as equilibrium balances in the savings-investment and current accounts. With respect to the ecosystem, the model treats the various representative species as net energy maximizers and bases population dynamics on the period-by-period sizes of surplus net energy. Energy markets – where predators and prey exchange biomass – determine equilibrium energy prices. With respect to the regional economy, we model five production sectors (at the aggregate industry level) – brine cyst harvesters, the mineral-extraction industry, agriculture, recreation, and a composite-good industry – as well as the household sector. By performing dynamic simulations of the joint ecosystem–regional economy model, we isolate the effects of period-by-period stochastic changes in salinity levels and an initial shock to species-population levels on the ecological and economic variables of the model.