The essay presents an original application of using the coolhunting method to discover new trends in architecture and design. The ability to identify trends is tied in with the possibility of attaining an advantage over the competition with the use of new designs that can become hits on the market, gaining the favor of customers. The term coolhunting can be broadly defined as the pursuit of inspiration and the forecasting of the directions of development. Initially, the term was applied to fashion, but quickly spread to other spheres of activity, like music, the arts, lifestyle and finally, to architecture and design. The essay is a slightly altered and improved rendition of the author's article published in Zastosowania ergonomii. Wybrane kierunki badań ergonomicznych w roku 2014 . (ed. Charytonowicz J.), Publ. Polskie Towarzystwo Ergonomiczne PTErg, o/Wrocław, 2014, p. 289-304. The method outlined therein is the result of research conducted under the author's supervision at the Institute of Architecture and Spatial Planning of the Poznań University of Technology between the years 2012 and 2014.

In this paper we show that in the lognormal discrete-time stochastic volatility model with predictable conditional expected returns, the conditional expected value of the discounted payoff of a European call option is infinite. Our empirical illustration shows that the characteristics of the predictive distributions of the discounted payoffs, obtained using Monte Carlo methods, do not indicate directly that the expected discounted payoffs are infinite.

In order to prepare a coal company for the development of future events, it is important to predict how can evolve the key environmental factors. This article presents the most important factors influencing the hard coal demand in Poland. They have been used as explanatory variables during the creation of a mathematical model of coal sales. In order to build the coal sales forecast, the authors used the ARMAX model. Its validation was performed based on such accuracy measures as: RMSE, MAPE and Theil’s index. The conducted studies have allowed the statistically significant factors out of all factors taken into account to be identified. They also enabled the creation of the forecast of coal sales volume in Poland in the coming years. To maintain the predictability of the forecast, the mining company should continually control the macro environment. The proper demand forecast allows for the flexible and dynamic adjustment of production or stock levels to market changes. It also makes it possible to adapt the product range to the customer’s requirements and expectations, which, in turn, translates into increased sales, the release of funds, reduced operating costs and increased financial liquidity of the coal company. Creating a forecast is the first step in planning a hard coal mining strategy. Knowing the future needs, we are able to plan the necessary level of production factors in advance. The right strategy, tailored to the environment, will allow the company to eliminate unnecessary costs and to optimize employment. It will also help the company to fully use machines and equipment and production capacity. Thanks to these efforts, the company will be able to reduce production costs and increase operating profit, thus survive in a turbulent environment.

While personality is strongly related to experienced emotions, few studies examined the role of personality traits on affective forecasting. In the present study, we investigated the relationships between extraversion and neuroticism personality traits and affective predictions about academic performance. Participants were asked to predict their emotional reactions two months before they will get their results for one important exam. At the same time, personality was assessed with the Big Five Inventory. All the participants were contacted by a text message eight hours after that the results were available, and they were requested to rate their experienced affective state. Results show moderate negative correlations between neuroticism and both predicted and experienced feelings, and that extraversion exhibits a weak positive correlation with predicted feelings, but not with experienced feelings. Taken together, these findings confirm that extraversion and neuroticism shape emotional forecasts, and suggest that affective forecasting interventions based on personality could probably enhance their efficiencies.

In this article, we review the research state of the bullwhip effect in supply chains with

stochastic lead times. We analyze problems arising in a supply chain when lead times are

not deterministic. Using real data from a supply chain, we confirm that lead times are

stochastic and can be modeled by a sequence of independent identically distributed random

variables. This underlines the need to further study supply chains with stochastic lead times

and model the behavior of such chains.

Bayesian VAR (BVAR) models offer a practical solution to the parameter proliferation concerns as they allow to introduce a priori information on seasonality and persistence of inflation in a multivariate framework. We investigate alternative prior specifications in the case of time series with a clear seasonal pattern. In the empirical part we forecast the monthly headline inflation in the Polish economy over the period 2011‒2014 employing two popular BVAR frameworks: a steady-state reduced-form BVAR and just-identified structural BVAR model. To evaluate the forecast performance we use the pseudo real-time vintages of timely information from consumer and financial markets. We compare different models in terms of both point and density forecasts. Using formal testing procedure for density-based scores we provide the empirical evidence of superiority of the steady-state BVAR specifications with tight seasonal priors.

This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.

In many research studies it is argued that it is possible to extract useful information about future real economic activity from the performance of financial markets. However, this study goes further and shows that it is not only possible to use expectations derived from financial markets to forecast future economic activity, but that data about the financial system can be used for this purpose as well. This paper sheds light on the ability to forecast real economic activity, based on additional and different financial variables than what have been presented so far.

The research is conducted for the Polish emerging economy on the basis of monthly data. The results suggest that, based purely on the data from the financial system, it is possible to construct reasonable measures that can, even for an emerging economy, effectively forecast future real economic activity. The outcomes are proved by two different econometric methods, namely, by a time series analysis and by a probit model. All presented models are tested in-sample and out-of-sample.

The main aim of this research is to compare the results of the study of demand’s plan and

standardized time based on three heuristic scheduling methods such as Campbell Dudek

Smith (CDS), Palmer, and Dannenbring. This paper minimizes the makespan under certain

and uncertain demand for domestic boxes at the leading glass company industry in Indonesia.

The investigation is run in a department called Preparation Box (later simply called PRP)

which experiences tardiness while meeting the requirement of domestic demand. The effect

of tardiness leads to unfulfilled domestic demand and hampers the production department

delivers goods to the customer on time. PRP needs to consider demand planning for the

next period under the certain and uncertain demand plot using the forecasting and Monte

Carlo simulation technique. This research also utilizes a work sampling method to calculate

the standardized time, which is calculated by considering the performance rating and

allowance factor. This paper contributes to showing a comparison between three heuristic

scheduling methods performances regarding a real-life problem. This paper concludes that

the Dannenbring method is suitable for large domestic boxes under certain demand while

Palmer and Dannenbring methods are suitable for large domestic boxes under uncertain

demand. The CDS method is suitable to prepare small domestic boxes for both certain and

uncertain demand.

The aim of the paper is to point out that the Monte Carlo simulation is an easy and flexible approach when it comes to forecasting risk of an asset portfolio. The case study presented in the paper illustrates the problem of forecasting risk arising from a portfolio of receivables denominated in different foreign currencies. Such a problem seems to be close to the real issue for enterprises offering products or services on several foreign markets. The changes in exchange rates are usually not normally distributed and, moreover, they are always interdependent. As shown in the paper, the Monte Carlo simulation allows for forecasting market risk under such circumstances.

Weather forecasting requires knowledge of the laws of atmospheric movement. Apart from classic fluid mechanics, we must consider the rotational motion of our planet, the differential heating of its surface through the absorption of solar radiation, as well as water evaporation and condensation processes.

Changes in capacity of water reservoir Cedzyna during its exploitation since 1972 till 2003 are presented in the paper. Analyses were based on cross sections of the reservoir’s basin from before its fulfillment (1967) and those measured with the echo sounder Ceeducer in 2003. Silting of reser-voir was predicted based on empirical methods. The volume of reservoir was found to decrease by 112.8 thousand m3 during 31 years of its exploitation and reservoir’s life span was assessed at 685 years. An error analysis was additionally made of calculating the surface area of a cross section at varying number of sounding sites. It was found that there was no need to note too many coordinates and depths and for the Cedzyna reservoir the distance between measurement sites up to 16 m was sufficient.