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Impact of Institutional Factors on Economic Growth in the United States in the Years 1979–2007

100%
Kuder D.
Oeconomia Copernicana
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2015
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vol. 6
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issue 1
137-159
EN
The purpose of this article is to isolate and determine the importance of institutional arrangements in shaping the dynamics of the U.S. GDP in the years 1979–2007. The research hypothesis which has been verified here can be summa-rized as follows: institutions in the U.S. economy have a positive influence on economic growth through a significant impact on improving the business environment. Having regard to the division of the economy into institutional areas: economic system, labor market, financial market, education and R&D, the author selected these institutional factors which indicated that the operation could be important for the process of economic growth in the United States, and then measured the impact in the years 1979–2007. To verify the thesis about the impact of institutions on economic growth the author used one of the most popular tools in this kind of econometric research – the multiple regression analysis. The analysis revealed that during the period of all the analyzed institutional factors it was the proportion of the working population and the degree of unionization that most strongly influenced the economic growth of the United States – an increase in one of these factors was associated with a much more than proportional increase in the rate of the economic growth.
2
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SOME REMARKS ON GENERALIZED REGRESSION METHODS

100%
Fałda B., Zając J.
Metody Ilościowe w Badaniach Ekonomicznych
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2016
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vol. 17
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issue 4
31-39
EN
As a result of studying certain phenomena gained on the plane, the unit circle and on the earth sphere we present here some introductory notations and remarks, concerning the problems in question.
3
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Some Nonparametric Estimators of Regression Function

88%
Baszczyńska A., University of Łódź C. o. S. M.
Acta Universitatis Lodziensis. Folia Oeconomica
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2007
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vol. 206
EN
In the paper some nonparametric estimators of regression function are studied: Nadaraya-Watson estimator and k-nearest neighbour one. Properties of these estimators and possibilities of using them in practice are taken into consideration. A comparative study of the two estimators is presented. Different techniques of choosing method’s parameters (kernel function, smoothing parameter h and parameter k) are used in this study to choose the optimal ones. Some practical rules are proposed and they are used in this study.
PL
W pracy przedstawiono wybrane dwa nieparametryczne estymatory funkcji regresji: estymator jądrowy Nadaraya-Watsona oraz estymator k-najbliższego sąsiada. Podano ich własności, możliwości wykorzystania oraz dokonano porównania tych estymatorów. Przedstawiono również przykład zastosowania estymatora jądrowego regresji z uwzględnieniem właściwego doboru parametrów metody (funkcji jądra i parametru wygładzania h) oraz estymatora k-najbliższego sąsiada z uwzględnieniem właściwego doboru parametru k. Zaproponowano również praktyczne zasady wyboru parametrów estymacji funkcji regresji i wykorzystano je w przykładzie.
4
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Modele ekonometryczne jako narzędzie sterowania procesami technologicznymi

88%
Wołkowicz A.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2015
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issue 2 (48)
67-77
EN
This paper presents a proposal for process control applications based on econo-metric models. They are a tool which aim is to determine short-term forecasts, which are the basis to control the devices of production infrastructure. The article describes the application of the method of forecast errors corrective device in a real production process. Econometric models are presented: the exponential smoothing model and creeping trend adaptive model with harmonic scales. The calculations are used and the regression function is indicated by the linear programming problem. The method is presented on the example of classical tech-nological process used in the energy sector. The study indicates the possibility of another perspective on the control processes, not necessarily based on the existing methods of regu-lation. The idea of this study is to demonstrate the possibility of using econometrics in the industry.
5

Modele wskaźnikowe rynku kapitałowego wykorzystujące funkcję regresji wektorów losowych

63%
Tatar J.
Zeszyty Naukowe Uniwersytetu Ekonomicznego w Krakowie
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2013
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issue 923
37-45
PL
Jedną z ważniejszych kategorii modeli rynku kapitałowego stanowią modele wskaźnikowe. Wyrażają one liniową zależność stóp zwrotu z konkretnych (pojedynczych) aktywów od wybranego zestawu czynników. Czynnikami tymi są na ogół stopy zwrotu odpowiednio konstruowanych portfeli; mogą nimi być np. wybrane indeksy giełdowe. Kluczowe znaczenie w modelach wskaźnikowych mają współczynniki wrażliwości modelowanej stopy zwrotu na zmiany wybranych czynników. Współczynniki te znane są w teorii i praktyce finansów jako tzw. współczynniki β, a jedną z metod ich wyznaczania jest analiza regresji. We wcześniejszych pracach autor wykazał, że możliwa jest jednoznaczna konstrukcja funkcji regresji dla dwóch wektorów losowych niekoniecznie o tych samych wymiarach. Wynik ten w niniejszym opracowaniu stanowi dogodny punkt wyjścia do uogólnienia postaci modeli wskaźnikowych, na przypadek gdy wektor wybranych stóp zwrotu jest funkcją innego wektora czynników (np. wektora stóp zwrotu z innych aktywów). Uzyskany w ten sposób współczynnik β z oczywistych powodów będzie miał postać macierzową.
EN
One of the more important categories of the capital market, indicatory models show the linear dependence of rate from specific (individual) assets on a selected set of factors. These factors are usually the profitability of appropriately constructed portfolios; these can include, for example, specific stock indexes. The coefficients of the sensitivity of the rates on the changes of specific factors are essential for the indicatory models. In financial theory and practice those coefficients are known as beta coefficients and one method of determining them is regression analysis. In the previous works the author showed that construction of the regression function of two random vectors – not necessarily of the same dimensions – is possible. That result – in this paper – is a convenient starting point to generalising the indicatory model form for cases where the vector of selected repayment rates is a function of other vector factors (e.g. the repayment rate vector of other assets). The beta coefficient obtained in this way, for obvious reasons, will have a matrix form.
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