C10 - Econometric and Statistical Methods and Methodology: GeneralReturn

O. R. Lange, a Polish economist, statistician, econometrician, sociologist, politician and diplomat was classified as a reformist Marxist. He became famous for studies on planning and socialist economies. Lange demonstrates and proves the possibility of the existence of an effective socialist system. He also analyzed capitalist economy, e.g., the issue of cyclical fluctuations and unemployment.

With the development of the Internet in the last two decades, its use in all phases of field survey is growing very quickly. Indeed, it reduces costs while allowing exploration of relatively large files and enables effective use of a variety of research tools. The academic research is more reserved towards developing online surveys. Demands on the quality of data are the main cause; Internet surveys do not meet them and thus do not allow drawing objective conclusion about the populations surveyed.
Unqualified use of the Internet may significantly influence data and information obtained from their analysis. The problematic definition of the population that is under investigation may result in a fault of its coverage. Its existence can be shown, for example, on a confrontation of the total and Internet population of the Czech Republic, the total and Internet population of the Czech households, etc. Representation of the population through an online panel may cause bias, depending on how the panel is created. A relatively new source of error in an online survey is the existence of "professional" respondents.
The sampling method from a population or an online panel can lead to the emergence of such a sample that is not representative and does not allow inference to the population at all, or only in a very limited way. Even probability sampling, however, can be problematic if it is affected by a higher rate of non-responses. The aim of this paper is to summarise the possible sources of bias associated with any sample survey, but also to draw attention to those that are relatively new and are associated with the implementation of just quantitative surveys online.

Almost any data set can be encountered to the problem of missing data; it is well known in the phenomena relating to people populations and researched in sample surveys. In recent decades, the issue of missing data received considerable attention, because the simple omission of units, for which data are lacking, from the analysis may lead to erroneous conclusions. The approach that accepts the existence of missing data through the modification of the probabilities of units selection with probabilities of obtaining data on them, leads to the construction and use of the weights. Different solution lies in filling in missing data. Using the arithmetic mean or a regression function, recommended for this purpose before, leads at the relevant variables at least to an underestimation of variability; furthermore, it is applicable only for measurable variables. Alternative approaches to missing data are based on the likelihood of collected data assuming some model. Two directions of their development can be distinguished again, estimating population parameters without imputation of missing data on the one hand (EM algorithm) and multiple imputation methods on the other.

The paper deals with the issues of gravity models applied in the field of trade flow analysis. Applied variables, assumptions of their impacts, problems of gravity equations and their resolving (namely the endogeneity problem, the heterogeneity problem, and the identification problem) are defined in this paper. The paper provides a detailed overview of selected gravity model publications in scientific papers with an impact factor since 2000. According to the document analysis, it is clear that panel data are the most commonly used type of data in the 21st century. Fixed effects models are recommended. The choice of variables depends on the subject being examined and on the aim of the analysis. Correct model specification and inclusion of relevant variables are highly important. The results were elaborated as part of the research project IGA 20121050 "Spatial regional market integration with agricultural raw materials".

Farmers earn their main income from selling agricultural commodities. Stability as well as the level of income are determined by the amount of production and market prices. Prices and price policies on the agricultural market could have a great impact on the success of programmes themselves. This paper focuses on research of relationships between prices at the regional level. The aim of this paper is to evaluate the relationship between prices at the regional level for selected regions of Bohemia and try to confirm that the Law of One Price is valid there. Agricultural prices for pork producers were used for the analysis. The data set has 157 observations in total. The first observation is from 27th June 2002 and the last observation is from 27th December 2008. No data have been published by the State Agricultural Intervention Fund since 2009. The investigated groups of regions are the Central Bohemian (CB), North-Eastern (N_E) and North-Western (N_W) regions of the Czech Republic. Econometric methods are used, and the primary tools for research are the ADF test, KPSS test, Johansen cointegration test and Vector Error Correction Model (VECM). On the basis of the VECM model, it was found that prices in the North-Eastern regions have an effect on prices in Central Bohemia. It was also discovered that prices in the Central Bohemian and North-Western regions have a mutual effect. The Law of One Price is valid with a high probability in the investigated regions. Prices in the long run are identical with a high probability. The results were elaborated as part of the research project MSM 6046070906 "Economics of resources of Czech agriculture and their efficient use in frame of multifunctional agri-food systems".

Weather conditions influence the health of humans. Changing weather patterns may also cause considerable increase or decrease in the number of deaths. In this paper, we use daily data for Prague, Czech Republic, and the maximum overlap discrete wavelet transform to explore the time scale patterns in the relationship between the average ambient temperature and the number of deaths due to cardiovascular diseases. We summarize several well-known facts, give a short introduction to the maximum overlap discrete wavelet transform and study the relationship between several pairs of wavelet coefficients of various levels for the temperature time series and the number of deaths. The results are suggestive of a positive linear relationship between respective wavelet coefficients of the 4th level during summer. More specifically, we have estimated that a one-degree-Celsius increase in the weekly average temperature during summer is accompanied by an increase in the weekly average number of deaths by 0.37 on average. The odds in favor of an increase in the weekly average number of deaths during summer have been estimated to be 4.22 times higher if an increase in the weekly average temperature occurs when compared to the situation when a decrease in the weekly average temperature occurs. Further research might be desirable to verify and interpret the results.