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1
Chetverikov, Denis. "TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS." Econometric Theory 35, no. 4 (September 18, 2018): 729–76. http://dx.doi.org/10.1017/s0266466618000282.
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Monotonicity is a key qualitative prediction of a wide array of economic models derived via robust comparative statics. It is therefore important to design effective and practical econometric methods for testing this prediction in empirical analysis. This article develops a general nonparametric framework for testing monotonicity of a regression function. Using this framework, a broad class of new tests is introduced, which gives an empirical researcher a lot of flexibility to incorporate ex ante information she might have. The article also develops new methods for simulating critical values, which are based on the combination of a bootstrap procedure and new selection algorithms. These methods yield tests that have correct asymptotic size and are asymptotically nonconservative. It is also shown how to obtain an adaptive and rate optimal test that has the best attainable rate of uniform consistency against models whose regression function has Lipschitz-continuous first-order derivatives and that automatically adapts to the unknown smoothness of the regression function. Simulations show that the power of the new tests in many cases significantly exceeds that of some prior tests, e.g., that of Ghosal, Sen, and Van der Vaart (2000).
2
Kuzmin, Peter, Vitaliy Kalashnikov, Natalyia Kalashnykova, and Junzo Watada. "The Great Depression: Econometric Analysis and Fuzzy Regression." Journal of Advanced Computational Intelligence and Intelligent Informatics 24, no. 6 (November 20, 2020): 785–91. http://dx.doi.org/10.20965/jaciii.2020.p0785.
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The study examines the period of the Great Depression and analyzes several measures taken by the US President Roosevelt’s government that allowed the country to get out of the crisis. An analysis and proof of the correctness of the measures chosen to exit from the crisis was conducted using econometric models and the use of statistics. Techniques for overcoming crises are relevant for all countries. This study adapts an innovative perspective in that it used the sequence of the Cobb–Douglas type function including different types of factors, and applied fuzzy regression methods.
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Phillips, P. C. B. "Partially Identified Econometric Models." Econometric Theory 5, no. 2 (August 1989): 181–240. http://dx.doi.org/10.1017/s0266466600012408.
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This paper studies a class of models where full identification is not necessarily assumed. We term such models partially identified. It is argued that partially identified systems are of practical importance since empirical investigators frequently proceed under conditions that are best described as apparent identification. One objective of the paper is to explore the properties of conventional statistical procedures in the context of identification failure. Our analysis concentrates on two major types of partially identified model: the classic simultaneous equations model under rank condition failures; and time series spurious regressions. Both types serve to illustrate the extensions that are needed to conventional asymptotic theory if the theory is to accommodate partially identified systems. In many of the cases studied, the limit distributions fall within the class of compound normal distributions. They are simply represented as covariance matrix or scalar mixtures of normals. This includes time series spurious regressions, where representations in terms of functionals of vector Brownian motion are more conventional in recent research following earlier work by the author.
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Kozinova, A. T. "An econometric analysis of retail turnover in Russia." Economic Analysis: Theory and Practice 19, no. 6 (June 29, 2020): 1133–53. http://dx.doi.org/10.24891/ea.19.6.1133.
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Subject. The article deals with econometric analysis of retail turnover in Russia and its relationship with macroeconomic indicators, like real disposable household income, consumer prices, etc. Objectives. The purpose is to create effective models to analyze the retail turnover in Russia and its relationship with other macroeconomic indicators, taking into account the existence of periods of economic instability. Methods. I apply correlation and regression methods to analyze statistics. To quantify changes in the retail turnover of Russia during the periods of economic instability, I use dummy variables. Results. The Russia’s retail trade turnover index had a reverse and moderate relationship with the consumer price index, direct and strong relationship with the indices of real disposable household income and imports, direct relationship with the manufacturing index. I offer statistically significant regression models of Russia’s retail turnover with the said macroeconomic indicators. Conclusions. The main advantage of models of retail turnover that are built using a large number of observations is a greater number of simultaneously considered factors. The quantitative assessment of retail turnover elasticity by consumer prices confirms the need for inflation targeting by the Central Bank of the Russian Federation. The higher elasticity of retail turnover in manufacturing as compared with the imports denotes the importance of import substitution policy.
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Rudzkis, R., and E. Mačiulaitytė. "Econometrical Modelling of Profit Tax Revenue." Nonlinear Analysis: Modelling and Control 12, no. 1 (January 25, 2007): 95–112. http://dx.doi.org/10.15388/na.2007.12.1.14724.
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The aim of this article is to present a forecast of budget revenue from the profit tax using econometric models. The set of applied models has to be reduced to very simple models due to short time series used. Therefore, the profit tax regression analysis is made in two stages. In the first stage, econometric modelling of profit tax revenue with the main profit indicators (called the profit tax base) is performed on the basis of information on profit tax regulation and its changes. In the second stage, algorithms of forecasting the profit tax base are formed when the main macroeconomic indicators of Lithuanian economy are used as regressors. Crossvalidation was applied to estimate the accuracy of these algorithms.
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Angrist, Joshua D., and Jörn-Steffen Pischke. "Undergraduate Econometrics Instruction: Through Our Classes, Darkly." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 125–44. http://dx.doi.org/10.1257/jep.31.2.125.
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The past half-century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist's typical mission was to “explain” economic variables like wages or GDP growth. Applied econometrics has since evolved to prioritize the estimation of specific causal effects and empirical policy analysis over general models of outcome determination. Yet econometric instruction remains mostly abstract, focusing on the search for “true models” and technical concerns associated with classical regression assumptions. Questions of research design and causality still take a back seat in the classroom, in spite of having risen to the top of the modern empirical agenda. This essay traces the divergent development of econometric teaching and empirical practice, arguing for a pedagogical paradigm shift.
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Yao, Jia, Siqin Xiong, and Xiaoming Ma. "Comparative Analysis of National Policies for Electric Vehicle Uptake Using Econometric Models." Energies 13, no. 14 (July 13, 2020): 3604. http://dx.doi.org/10.3390/en13143604.
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As electric vehicles (EVs) have been widely discussed as a promising way to mitigate the effect of climate change, various policies have been implemented across the world to promote the uptake of EVs. Policymakers also paid attention to the density of public charging points. In this paper, we examined the impact of policies on EV markets in the post subsidy era with multiple linear regression analysis using panel data on 13 countries from 2015 to 2018. Five of the independent variables showed significantly positive effects on the 1% level in different regression models: fast/slow charger density, mandate, purchasing restriction and waiver. Subsidies showed significance only on 5% level for battery electric vehicles (BEVs). Financial stimulates have experienced a declining marginal effect, whereas a high density of fast chargers has the most significantly positive effect on EV uptake. This paper suggests policymakers can invest more in completing the public infrastructures of EVs, especially on fast charging points.
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Hausman, Jerry. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left." Journal of Economic Perspectives 15, no. 4 (November 1, 2001): 57–67. http://dx.doi.org/10.1257/jep.15.4.57.
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The effect of mismeasured variables in the most straightforward regression analysis with a single regressor variable leads to a least squares estimate that is downward biased in magnitude toward zero. I begin by reviewing classical issues involving mismeasured variables. I then consider three recent developments for mismeasurement econometric models. The first issue involves difficulties in using instrumental variables. A second involves the consistent estimators that have recently been developed for mismeasured nonlinear regression models. Finally, I return to mismeasured left hand side variables, where I will focus on issues in binary choice models and duration models.
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Palant, Oleksii Yuriiovych, Hanna Volodymyrivna Ortina, and Marharyta Mykolaivna Kucher. "STATISTICAL ASSESSMENT OF SOCIO-ECONOMIC DETERMINATION OF CRIME IN UKRAINE." SCIENTIFIC BULLETIN OF POLISSIA, no. 4(16) (2018): 14–20. http://dx.doi.org/10.25140/2410-9576-2018-4(16)-14-20.
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The problem of analyzing the socio-economic determination of crime based on the use of econometric models has been studied, the correlation-regression models have been adapted, the applied aspects of the constructed regression equation to solve the problems of crime determination analysis have been considered
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Kumar, Ajay. "Climate Change and Sugarcane Productivity in India: An Econometric Analysis." Journal of Social and Development Sciences 5, no. 2 (June 30, 2014): 111–22. http://dx.doi.org/10.22610/jsds.v5i2.811.
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This study provides an understanding for the relationship between climatic factors and sugarcane productivity in India. The main objective of this paper is to estimates the impact of climatic and non-climatic factors on sugarcane productivity. To check the consistency of empirical results, simple linear regression model, Ricardian productivity regression (non-linear) model and Cobb-Douglas production function models are employed. The data set incorporates 390 observations corresponding to thirteen states with panel data for 30 years during 1980 to 2009. These all models include sugarcane productivity as dependent variable. Irrigated area, agriculture labour, consumption of fertilizers, literacy rate, tractors and farm harvest price (at constant level) are considered as explanatory variables. Average rainfall, average maximum and average minimum temperature include as climatic factors to capture the effect of climatic conditions on cane productivity. These climatic factors are incorporate for three weather seasons such as rainy, winter and summer. Empirical results based on Prais Winsten models with panels corrected standard errors (PCSEs) estimation shows that climatic factors i.e. actual rainfall, average maximum and average minimum temperature have a statistically significant impact on sugarcane productivity. The climatic effect for various factors on cane productivity are varies within different seasons. Average maximum temperature in summer and average minimum temperature in rainy season have a negative and statistically significant effect on sugarcane productivity. While, sugarcane productivity positively get affect with increasing average maximum temperature in rainy season and winter seasons. The study concluded that there is non-linear relationship between climatic factors and sugarcane productivity in India.
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Ece, Arslan, and Güven Sayılgan. "Macroeconomic Determinants of Financial Distress in Turkey: An Econometric Analysis." Australasian Business, Accounting & Finance Journal 14, no. 5 (2020): 86–107. http://dx.doi.org/10.14453/aabfj.v14i5.6.
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Purpose The purpose of this paper is to investigate the possible links between macroeconomic factors and financial distress in Turkey. Design/methodology/approach Based on the 2009/1-2016/2 quarterly data of macroeconomic factors and the number of filings for bankruptcy postponement, econometric models are developed using forward stepwise regression and classical regression methods to determine the factors influencing financial distress. A vector error correction model is also developed using macroeconomic factors found significant in both methods to investigate the interactions of financial distress with them. Findings In the stepwise regression implementation, performed with 16 independent variables, statistically significant variables entered into the model are industrial production index with negative sign as expected and the unemployment rate with negative sign against the expectations. In the classical regression implementation, performed with 7 independent variables, statistically significant variables are ex ante real interest rate with positive sign and gross domestic product with negative sign as expected and money supply with negative sign against the expectations. The impulse response graphics of a vector error correction model involving bankruptcy postponement, industrial production index and nominal interest rate indicates that bankruptcy postponement is influenced by the shocks both in itself and in industrial production index. Originality/value This is the first study in Turkey investigates macroeconomic determinants of financial distress.
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Formánek, Tomáš, and Roman Hušek. "Spatial Aspects Of Unemployment In The Visegrad-Group Economies." Creative and Knowledge Society 6, no. 2 (December 1, 2016): 1–12. http://dx.doi.org/10.1515/cks-2016-0007.
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Abstract Purpose of the article: Most regional macroeconomic processes may not be adequately analyzed without accounting for their spatial nature: regional distances, interactions between neighbors, spill-over effects and interdependencies. This contribution focuses on various factors ruling unemployment dynamics in the Visegrad Group countries and their major economic partners: Germany and Austria. The analysis is performed at the NUTS2 level. Methodology/methods: Spatial econometrics is a unique tool for a broad range of quantitative analyses and evaluations. Spatial econometric models are based on geo-coded (spatially defined) data. Spatial econometrics and regional competitiveness paradigms are combined into different types of regression model specifications describing unemployment dynamics. Alternative spatial structures (i.e. neighbor definitions) are used for verification of stability in estimated model properties. Scientific aim: We aim to provide a detailed empirical evaluation of spatially determined factors of regional unemployment dynamics, along with insight into the robustness of such approach. Both conceptually and parametrically varying neighbor definitions are used to provide evidence for model evaluation. Findings: We find strong positive spatial dependence patterns in the estimated models, robust against varying neighborhood definitions. Our results strongly support the importance of regional and potentially cross-border (international) cooperation in macroeconomic policies addressing unemployment. The estimated models also underline the importance of using spatial models, by pointing out the bias in OLS-estimated models. Conclusions and limits: Spatial approach to econometric analysis provides important insight and robustness to a broad range of unemployment analyses that may be carried out using regional (spatial) data. At the same time, it should be noted that this article focuses mostly on the spatial and stability aspects of model estimation, while leaving out other interesting topics such as spill-over effects calculations as based on estimated models. Also, estimations provided in this article might benefit from spatial panel data-based methods - once data availability issues are sorted.
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Pośpiech, Ewa Katarzyna, and Adrianna Mastalerz-Kodzis. "Application of Spatial Regression in Employment Characteristics Modelling." Acta Universitatis Lodziensis. Folia Oeconomica 3, no. 335 (May 16, 2018): 63–74. http://dx.doi.org/10.18778/0208-6018.335.05.
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The article analyses the employment characteristics. The employment rate was studied in selected regions of Europe, and subsequently, for selected variables: total population employed, women employed and men employed, classic econometric models were constructed and the necessity of including the spatial factor in the process of modelling was verified. The demographic variables and GDP per capita were chosen as explaining variables of the model. It was analysed whether including a spatial approach in the models would improve their quality. Two basic spatial models were taken into consideration: the spatial error model and the spatial lag model, the former of which turned out to be the right tool for the analyses.
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Baranovskyi, O., M. Kuzheliev, D. Zherlitsyn, and K. Serdyukov. "CRYPTOCURRENCY MARKET TRENDS AND FUNDAMENTAL ECONOMIC INDICATORS: CORRELATION AND REGRESSION ANALYSIS." Financial and credit activity: problems of theory and practice 3, no. 38 (June 30, 2021): 249–61. http://dx.doi.org/10.18371/fcaptp.v3i38.237454.
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Abstract. The first cryptocurrency was born in 2008. Already today, virtual financial assets and tokens are a significant part of trading in global financial markets. The cryptocurrency market capitalization currently exceeds 600 billion U.S. dollars. However, there is a lot of discussion about cryptocurrency functions and the correlation between Bitcoin prices and the basic economic indices.
Therefore, the purpose of the paper is to define the statistical substantiation of the influence of fundamental economic indicators on the market of virtual financial assets and the possibility of using cryptocurrency as the investment assets.
This article is based on the theoretical principles and methods of econometric analysis; the system approach methods to define the main vehicles and trends of the international financial market. The study presents correlation analysis, regression models with paired and multiple variables. For these models, R-Studio instruments are the main tools of quality estimation and results interpretation.
The article shows the results of the correlation analysis of Bitcoin’s U.S. dollar price dynamics and changes in the main stock, monetary market indicators, cryptocurrencies market tendency, levels of the United States fundamental economic indicators for the period from 2014 to 2021. Traditional multifactorial regression models are used to determine the level and the impact of individual indicators of the world stock market at the U.S. dollar price of Bitcoin. A comparison of the level of volatility of key investment financial assets in the market of cryptocurrencies and stock markets is carried out.
The authors determine the level of correlation dependence and make a regression model of the impact of fundamental economic indicators and stock market trends on the dynamics of U.S. dollar prices for key cryptocurrencies. The article presents conclusions on trends and problems of using cryptocurrencies as an investment asset, considering volatility and profitability.
Implementation of the results allows to clarify the economic essence of cryptocurrencies as a specific financial vehicle, as well as improving the existing models of investment management, considering the statistical characteristics of the virtual financial assets.
The main direction of further research is to build models of medium-term prediction of prices for the main cryptocurrencies as an investment asset in conditions of changes in global financial markets, which must consider the fundamental economic indicators of the world economy and trends on key stock and commodity markets.
Keywords: virtual financial asset, cryptocurrency, bitcoin, econometric model, financial market, economic indicator, investment asset.
JEL Classification D53, E44, G15, C58
Formulas: 3; fig.: 3; tabl.: 3; bibl.: 31.
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Rudzkis, Rimantas, and Roma Valkavičienė. "ECONOMETRIC MODELS OF THE IMPACT OF MACROECONOMIC PROCESSES ON THE STOCK MARKET IN THE BALTIC COUNTRIES." Technological and Economic Development of Economy 20, no. 4 (December 16, 2014): 783–800. http://dx.doi.org/10.3846/20294913.2014.949901.
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The article examines the dependencies of individual sectoral stock price indices of OMX Baltic security market on macroeconomic indicators, using econometric methods. Regression models are constructed using quarterly time series of 2000–2011 years while the methodology is backed with the findings of Lithuanian and foreign scientists from an extensive overview of specific literature. Regression equations, obtained in the paper, allows us to identify the key macroeconomic and global indicators that statistically significantly affect the Baltic securities market and to quantify their impact on the stock price indices of individual sectors in the Baltic countries. Econometric analysis of OMX Baltic security market proves the hypothesis that the set of macroeconomic regressors may vary considerably depending on the individual sector's price indices, especially in the case of small open economy with immature stock markets. The paper provides investors who are shaping their portfolios taking into account the macroeconomic forecasts with additional opportunities on the basis of sectoral stock price indices regression equations.
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Chen, You-Shyang, Chien-Ku Lin, Su-Fen Chen, and Shang-Hung Chen. "Two Advanced Models of the Function of MRT Public Transportation in Taipei." Electronics 10, no. 9 (April 29, 2021): 1048. http://dx.doi.org/10.3390/electronics10091048.
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Tour traffic prediction is very important in determining the capacity of public transportation and planning new transportation devices, allowing them to be built in accordance with people’s basic needs. From a review of a limited number of studies, the common methods for forecasting tour traffic demand appear to be regression analysis, econometric modeling, time-series modeling, artificial neural networks, and gray theory. In this study, a two-step procedure is used to build a predictive model for public transport. In the first step of this study, regression analysis is used to find the correlations between two or more variables and their associated directions and strength, and the regression function is used to predict future changes. In the second step, the regression analysis and artificial neural network methods are assessed and the results are compared. The artificial neural network is more accurate in prediction than regression analysis. The study results can provide useful references for transportation organizations in the development of business operation strategies for managing sustainable smart cities.
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Басовский, Leonid Basovskiy, Басовская, and Elena Basovskaya. "Russian Regions’ Economy Research: Econometric Approach." Economics 2, no. 2 (April 17, 2014): 13–17. http://dx.doi.org/10.12737/3648.
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It is proposed a research program related to studying of economic development
of regions and country as a whole. It is suggested to build econometric models
of influence of factors which are able to affect indicators characterizing the
economic development. For this purpose it is proposed to use cross-sectional
data of the Federal State Statistics Service of Russia by region .On the basis of
obtained models it is possible to get partial regression equations. From these
equations it has been obtained equations for partial coefficients of influence of
studied factors by region. A use of described techniques has allowed estimate
an influence of capital-labor ratio, human capital, new technologies and private
property institution on labor efficiency in Russia. A higher education expansion
influence on salary level in Russia has been estimated. Estimates of private
property expansion influence on labor efficiency in the regions of Russia have
been obtained. Using these estimates it is possible to accomplish a costeffectiveness
analysis related to management of factors affecting the economic
development of country regions.
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Горидько, Нина, Nina Goridko, Татьяна Соломина, and Tatyana Solomina. "ECONOMETRIC MODELLING OF THE PASSENGER THROUGHPUT OF RUSSIAN CIVIL AVIATION." Russian Journal of Management 4, no. 1 (April 3, 2016): 54–64. http://dx.doi.org/10.12737/17903.
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The article is devoted to dynamics analysis and regression
modelling of the civil aviation passenger throughput. There has
been analyzed a relation between the passenger throughput and
two independent variables: average income per capita and cost of
economy class cabin flight per 1000 km. The authors drew substantial
conclusions on the character of the relation between the dependent
and explanatory variables, dynamics of air-passenger operations
as well as the factors affecting the passenger throughput
value. Based on constructed regression models a short-term forecast
of explanatory variables and passenger throughput value has
been made.
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Falianty, Telisa Aulia. "Tinjauan terhadap Metode Ekonometrika Lanjutan." Jurnal Ekonomi dan Pembangunan Indonesia 4, no. 1 (July 1, 2003): 59–74. http://dx.doi.org/10.21002/jepi.v4i1.133.
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Econometric models have been played an increasingly important role in empirical analysis in economics. This paper provides an overview on some advanced econometric methods that increasingly used in empirical studies.A panel data combines features of both time series and cross section data. Because of increasing availability of panel data in economic sciences, panel data regression models are being increasingly used by researcher. Related to panel data model, there are some methods that will be discussed here such as fixed effect and random effect. A new approach to panel data that developed by Im, Shin, and Pesaran (2002) for testing unit root in heterogenous panel is included in this overview.When we work with time series data, there are many problems that we must handle, most of them are unit root test, cointegration among non stationary variables, and autoregressive conditional heteroscedasticity. Provided these problems, author also review about ADF and Philips-Perron test. An approch to cointegration analysis developed by Pesaran (1999), ARCH and GARCH model are also interesting to be discussed here.Bayesian econometric, that less known than classical econometric, is includcd in this overview. The genctic algorithm, a relatively new method in econometric, has bcen increasingly employed the behavior of economic agents in macroeconomic models. The genetic algorithm is based on thc process of Darwin’s Theory of Evolution. By starting with a set of potential solutions and changing them during several iterations, the Genetic Algorithm hopes to converge on the most ‘fit’ solutions.
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Kim, Yungwook. "Measuring the Bottom-Line Impact of Corporate Public Relations." Journalism & Mass Communication Quarterly 77, no. 2 (June 2000): 273–91. http://dx.doi.org/10.1177/107769900007700204.
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This study uses econometric models to test a methodology for establishing a relationship between public relations goals and bottom-line contribution to the organization. Regression analysis showed that non-linear models tested in this study were appropriate for measuring the relationship between reputation and revenues. Results demonstrated a positive relationship between these two variables. These models indicate a meaningful landmark in evaluation research that attempts to document the bottom-line impact of public relations activities.
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Ovcharov, A. O., and A. M. Terekhov. "Econometric analysis of the use of biological assets in agricultural organizations." Statistics and Economics 17, no. 1 (March 10, 2020): 79–87. http://dx.doi.org/10.21686/2500-3925-2020-1-79-87.
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The purpose of the study. The purpose is to analysis the use of biological assets through econometric models and quantifies the relationship between economic indicators of agricultural activity. The article is devoted to the possibilities of econometric analysis in the context of limited empirical data on Russia regarding the use of biological assets.Materials and methods. The article analyzed the Russian and foreign bibliography on the problems of biological assets research. In the context of the study of biological assets, the possibilities of using econometric analysis methods based on spatial, temporal or panel data are shown. A multi-regression model based on three groups of indicators (livestock and crop indicators, as well as indicators reflecting agricultural development in general) and implemented in two phases has been built. This model allowed making estimates of the impact of biological assets on the value of agricultural products in Russia. The methods of correlation analysis investigated the closeness of the relationship between the variables in the model. The initial data for this study were the annual data for the period 2000–2018. An array of more than 150 observations was used. For the purpose of comparability, this array has been converted into relative values, i.e. econometric analysis was carried out on growth rates.Results. This article highlights the main areas of research on biological assets presented in the works of Russian and foreign scientists-economists. It is concluded that there are a wide range of issues of assessment and accounting of biological assets in Russian publications and in the context of the transition to international reporting standards. These problems are not relevant for foreign researchers – in foreign publications presented mainly the search for effective mechanisms for assessing the market value of biological assets using complex econometric models. The article substantiates the importance of using econometric analysis methods in Russian conditions, highlights a number of areas of such analysis and presents a multifactorial regression model. The implementation of the model allowed quantifying the hypothesis of the strong impact of biological asset productivity on the value of agricultural organizations. Based on the construction of the trend line and the choice of the optimal value of the value of the value of the approximation, a short-term forecast of the value of agricultural products produced by livestock and crop production was made. In addition, the correlation matrix assesses the closeness of the relationship between economic indicators.Conclusion. Economic analysis of biological assets should include a variety of areas – valuation, accounting and auditing, insurance and leasing, efficient use and management. The debate between proponents and opponents of the valuation of biological assets at fair value as an alternative to historical value accounting is of particular relevance today. Many issues relating to the valuation and use of biological assets can be addressed by econometric analysis techniques. In Russian practice, this approach is not yet widespread. However, it is it that it quantifies the complex links between economic variables (including biological assets) that characterize agricultural activities.
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Sarkodie, Samuel Asumadu, Emmanuel Ackom, Festus Victor Bekun, and Phebe Asantewaa Owusu. "Energy–Climate–Economy–Population Nexus: An Empirical Analysis in Kenya, Senegal, and Eswatini." Sustainability 12, no. 15 (July 31, 2020): 6202. http://dx.doi.org/10.3390/su12156202.
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Motivated by the Sustainable Development Goals (SDGs) and its impact by 2030, this study examines the relationship between energy consumption (SDG 7), climate (SDG 13), economic growth and population in Kenya, Senegal and Eswatini. We employ a Kernel Regularized Least Squares (KRLS) machine learning technique and econometric methods such as Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS) regression, the Mean-Group (MG) and Pooled Mean-Group (PMG) estimation models. The econometric techniques confirm the Environmental Kuznets Curve (EKC) hypothesis between income level and CO2 emissions while the machine learning method confirms the scale effect hypothesis. We find that while CO2 emissions, population and income level spur energy demand and utilization, economic development is driven by energy use and population dynamics. This demonstrates that income, population growth, energy and CO2 emissions are inseparable, but require a collective participative decision in the achievement of the SDGs.
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Baker, T. Brent, and Raymond G. Deardorf. "Development and Application of a Revenue and Ridership Forecasting Model for Ferry Service." Transportation Research Record: Journal of the Transportation Research Board 1608, no. 1 (January 1997): 40–46. http://dx.doi.org/10.3141/1608-05.
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A combination of statistical approaches is used to develop near-to mid-range ridership and revenue forecasting models for Washington State Ferries for use in quarterly budget updates. Econometric regression models use historical and forecast trends in state economic and demographic variables to project systemwide ridership by six fare categories for different fare scenarios. Time series analysis models are used to project ferry ridership at the individual route level by six fare categories. The sum of the time series route forecasts is then calibrated to the econometric systemwide totals to yield unconstrained ridership forecasts by route and fare category. A capacity constraint model handles cases where the demand for vehicle travel exceeds vessel capacity by generating ridership ceilings for different service scenarios. Finally, the appropriate fares are applied to the ridership projections to arrive at revenue forecasts over a 10-year horizon.
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Maune, Alexander. "Trade in Services-Economic Growth Nexus: An Analysis of the Growth Impact of Trade in Services in SADC Countries." Journal of Economics and Behavioral Studies 11, no. 2(J) (May 13, 2019): 58–78. http://dx.doi.org/10.22610/jebs.v11i2(j).2819.
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The article analysed the trade in services led growth in ten selected countries in the Southern African Development Community region using econometric regression models. Panel data obtained from the World Bank and United Nations Conference on Trade and Development databases for the period 1992 to 2015 was analysed. Five variables were used in the econometric analysis. The marginal effects of service and goods exports were positive while those of goods and service imports were negative and highly significant as was expected from literature. Service exports registered an impact that was almost threefold that of service imports and greater than goods exports. Policy-makers are encouraged to, clearly define their trade in service strategy and reduce or remove trade restrictions. The study is of importance to researchers, the private sector and government policymakers.
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Polo, Francisco-Jos� V�zquez, Miguel Negr�n, Xavier Bad�a, and Montse Roset. "Bayesian regression models for cost-effectiveness analysis." European Journal of Health Economics 6, no. 1 (March 2005): 45–52. http://dx.doi.org/10.1007/s10198-004-0256-z.
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Kasprzyk, Beata. "Application of econometric models to measure the satisfaction with wages and salaries." Wiadomości Statystyczne. The Polish Statistician 63, no. 4 (April 27, 2018): 33–57. http://dx.doi.org/10.5604/01.3001.0014.0644.
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The article presents the results of the research attempting to make a statistical analysis of subjective perception of wages and salaries as the main component of household income, using a specific econometric modelling. It was examined whether certain socio-demographic determinants could be identified and, if so, to what extent they could influence the subjective sense of satisfaction with wage and salaries. The research was conducted on the basis of data collected using a survey method in 2015 for a random sample of households in Podkarpackie voivodship, where wages and salaries are among the lowest in the country. The binomial models used to explain the qualitative variable depending on the level of exogenous variables (qualitative and quantitative) were compared. Logit and probit regression models made it possible to determine the probability of success as a chance of a positive response, i.e. (in relation to the scope of the research) a state of satisfaction with obtained wages and salaries.
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Shah, Syed Alamdar Ali, Raditya Sukmana, and Bayu Arie Fianto. "Stage-I Shariah compliant Macaulay’s duration model testing." Journal of Islamic Accounting and Business Research 12, no. 7 (August 18, 2021): 941–64. http://dx.doi.org/10.1108/jiabr-05-2020-0158.
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Purpose The purpose of this study is to develop, test and examine econometric methodology for Sharīʿah-compliant duration models of Islamic banks. Design/methodology/approach The research evaluates all existing duration models from Sharīʿah’s perspective and develops a four-stage framework for testing Sharīʿah-compliant duration models. The econometric methodology consists of multiple regression, Johansen co-integration, error correction model, vector error correction model (VECM) and threshold vector error models (TVECM). Findings Regressions analysis suggests that returns on earning assets and interbank offered rates are significant factors for calculating the duration of earning assets, whereas returns paid on return bearing liabilities and average interbank rates of deposits are significant factors for duration of return bearing liabilities. VECM suggests that short run duration converges into long run duration and TVECM suggests that management of assets and liabilities also plays a significant role that can bring about a change of about 15% in respective durations. Practical implications Sharīʿah-compliant duration models will improve risk and Sharīʿah efficiency, which will ultimately improve market capitalization and returns stability of Islamic banks in the long run. Originality/value Sharīʿah-compliant duration models testing provides insight into how various factors, namely, rates of return, benchmark rates and managerial skills of Islamic bank risk managers impact durations of assets and liabilities. It also explains the future course of action for Sharīʿah-compliant duration model testing.
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Valier, Agostino. "Who performs better? AVMs vs hedonic models." Journal of Property Investment & Finance 38, no. 3 (March 26, 2020): 213–25. http://dx.doi.org/10.1108/jpif-12-2019-0157.
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PurposeIn the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.Design/methodology/approachAll tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.FindingsMachine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.Practical implicationsAVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.Originality/valueAccording to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.
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Akbulaev, Nurkhodzha, Basti Aliyeva, and Shehla Rzayeva. "Analysis of the Influence of the Price of Raw Oil and Natural Gas on the Prices of Indices and Shares of the Turkish Stock Exchange." Pénzügyi Szemle = Public Finance Quarterly 66, no. 1 (2021): 151–66. http://dx.doi.org/10.35551/pfq_2021_1_8.
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This article is a review on the impact of prices and their dependence on the cost of oil and natural gas on the world stock markets. The main studies and results achieved in the field of the impact of prices on both the stock index and industrial stocks and the dependence on the level of oil prices are presented. The paper presents an econometric study on the choice of offers on the securities market that allows us to identify the main specifics of changes in prices for the stock index and industrial shares in the daily period from 13. 05. 2012 to 01. 12. 2019. The article uses methods for estimating the impact of the price of natural gas and WTI crude oil using the Gretl statistical program, taking into account the selection of the main correlation features of the price matrix. Of the 13 proposed research models, only one model showed its statistical insignificance. A paired linear model of the CocaCola share price dependence and its dependence on NGFO prices was presented and analyzed in detail. Based on the results of econometric modeling, linear regression models were constructed for the dependence of stock prices on the NGFO and WTISPOT prices. The Gretl environment allows you to evaluate the situation in the econometric environment and make a forecast based on the obtained models of the dependence of stock prices and make appropriate conclusions.
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UZLAU, MARILENA CARMEN, NICOLAE MIHAILESCU, CORINA MARIA ENE, CONSTANTIN AURELIAN IONESCU, LILIANA PASCHIA, NICOLETA LUMINITA GUDANESCU NICOLAU, MIHAELA DENISA COMAN, and SORINA GEANINA STANESCU. "STATISTICAL ANALYSIS OF THE ECONOMETRIC INDICATORS IN THE FIELD OF TAX ADMINISTRATION IN SEVEN STATES OF EUROPEAN UNION." Journal of Science and Arts 20, no. 3 (September 30, 2020): 681–96. http://dx.doi.org/10.46939/j.sci.arts-20.3-a15.
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The mathematical analysis presented in this study identifies models of the dynamics of total tax collections and social contributions per inhabitant according to the gross domestic product per inhabitant from 2009 to 2018 for seven states in Eastern Europe by linear regression equations. The models are statistically confirmed as viable models because the required conditions for formulating this assessment are met. This study has the value and usefulness of preventive information for the correction and substantiation of individual governmental and community decisions, in order to homogenize both from the point of view of the fiscal behavior of each state and the point of view of economic development, in correlation with a financial and budgetary policy to maintain macroeconomic balances and economic stability.
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Magazzino, Cosimo, and Mantovani Michela. "Counterfeiting in Italian regions: an empirical analysis based on new data." Journal of Financial Crime 21, no. 4 (September 30, 2014): 400–410. http://dx.doi.org/10.1108/jfc-01-2014-0001.
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Purpose – The purpose of this paper is to examine the counterfeiting process in Italy, at a subnational level. Design/methodology/approach – The paper uses panel data estimators and mixture models regression. Findings – The paper finds that homogeneous clusters of regions could be derived, as a result of economic and geographical reasons. Moreover, household and public administration expenditure, indirect taxation, foreigners/population ratio and the number of ports have a positive impact on the counterfeiting diffusion index. Practical implications – The paper is practical as a source of reference in contrasting counterfeiting process. Originality/value – The paper uses new data applying recent econometric techniques to find homogeneous groups of regions on counterfeiting index.
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Комарова, Anna Komarova, Басовский, and Leonid Basovskiy. "Analysis of Labour Efficiency and Human Capital Assessment in the Russian Federation on the Example of Siberian Federal District." Economics 4, no. 1 (February 18, 2016): 57–60. http://dx.doi.org/10.12737/17722.
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With the use of regression analysis, we calculated the elasticity of labor efficiency
by education level of the employed population. The analysis included regional
data from the Federal State Statistics Service of Russia.
The implemented methodology is based on econometric models of labour
productivity in the regions of the Siberian Federal District of Russia. It is shown
that the level of education of the working population is a significant factor
in determining the productivity of labour in the regions of the Siberian Federal
District of Russia.
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Thrane, Christer. "Modelling tourists’ length of stay." Tourism Economics 22, no. 6 (September 21, 2016): 1352–66. http://dx.doi.org/10.5367/te.2015.0489.
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Modelling of tourists’ length of stay (LOS) is an expanding topic of study. A common thread in this literature is the use of sophisticated statistical/econometric methods. The present study builds on and extends an article critical of the statistical craftsmanship in prior LOS modelling studies. On the basis of an updated assessment of current practice and two small-scale case studies, two main conclusions are drawn. First, the available evidence suggests that the ordinary least squares (OLS) regression model produces qualitatively similar findings to much more complicated methods, such as duration and count data models. The principle of parsimony and the so-called KISS rule thus dictate that OLS regression analysis should be the preferred estimation technique in LOS modelling studies. Second, the quality of LOS modelling studies will most likely be improved by intensifying the use of the long-established tools of the trade explicated in influential econometric textbooks rather than by testing new estimation methods.
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Vasilev, Julian A. "Duration Models in Loan Management." Folia Oeconomica Stetinensia 15, no. 1 (June 1, 2015): 114–26. http://dx.doi.org/10.1515/foli-2015-0027.
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Abstract The purpose of this study is to estimate the future duration of a loan contract on the basis of several factors. The main methodology consists of a brief explanation of a survival analysis and a thorough application of a survival analysis in loan management. A real dataset from a credit institution (situated in Varna) is used. All contracts were signed for 30 days but some contracts were ended earlier, others - later. The main research question concerns the following statement. We may try to predict future loan duration by making an econometric model describing the dependency between the loan duration (as a dependent variable) and several independent variables. The dataset is analysed by calculating life tables, applying the Kaplan-Maier method and using Cox regression within SPSS. It is has been proved that the main covariates affecting loan duration are the variables: born in the region, month of birth and age. The formulated conclusions are valid for the analysed credit institution. This work provides a methodology for adapting duration models in credit institutions. The presented methodology (in this paper) may be applied over the dataset of other credit institutions (including banks) for loan duration prediction.
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Krupa, Thomas, Kirils Farbarzevics, and Bassam Salame. "INVESTMENT EFFICIENCY OF LIFE INSURANCE COMPANIES IN GERMANY: APPLICATION OF A TWO-STAGE SBM." Współczesna Gospodarka 10, no. 1 (32) (March 31, 2019): 79–91. http://dx.doi.org/10.26881/wg.2019.1.08.
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Purpose – To prove the robustness of the efficiency-measuring model against potentially system-relevant disturbances to company variables such as SIZE, ROA, solvency and organizational form.
Methodology – In the first stage, the established model is applied using the SBM to measure insurance efficiency. The underlying data sets are from the twenty biggest life insurance companies (2008-2017) in Germany. In the second stage, the established model is examined for its robustness against disturbance variables. Several disturbance variables are introduced individually to the system and examined for their influence by three econometric methods, Tobit regression, OLS and the fixed-effect model. This approach allows a comparative analysis of the results with respect to the systemic relevance of every added variable. In the end, the accuracy of the second stage is compared through the Spearman test.
Findings – The comparative analysis of all three econometric techniques brought ROA as an efficiency-influencing variable. Furthermore, both proved econometric models Tobit and OLS are SBM-suitable with cross-sectional data. Further evidence for SBM compatibility are found for Tobit and the fixed-effect model with panel data.
JEL classification: C510, C520
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Sultonovich, Asrakulov Abdurakhmon. "Multi-Factor Modeling And Forecasting Of Employment Indicators In The National Labor Market Of Uzbekistan." American Journal of Management and Economics Innovations 02, no. 12 (December 31, 2020): 27–47. http://dx.doi.org/10.37547/tajmei/volume02issue12-06.
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In the research theoretically studied and classified the characteristics of the factors for the multi-factor analysis of the employment indicators of the population. On the basis of theoretical research, analyzed employment indicators in the labor market of the Republic of Uzbekistan on the basis of the multi-factor link and determined econometric model. On the identified regression models, developed forecast indicators of the main employment indicators for 2020-2026 years and directions for effective employment in the labor market.
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Surya, Henry Viriya, and Prastowo Cahjadi. "Komparasi Regresi Ekonometri pada Perekonomian Indonesia 2SLS, VEC, dan ARIMA." Jurnal Ekonomi dan Pembangunan Indonesia 2, no. 2 (January 1, 2002): 88–112. http://dx.doi.org/10.21002/jepi.v2i2.627.
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This paper compares three models of econometric analysis on economy, in this case the Indonesian economy. The regression models are the two stage least squares (2SLS) which has a strong support from the economic theory of aggregate expenditure, the Vector Error Correction (VEC) and Autoregressive Integrated Moving Average (ARIMA) which both comes from the time series analysis, that do not have to be economic time series. The study tries to find out which are most suitable in analyzing the time series of Indonesian economy. After all the estimation and comparison process, we finally agree that the use of those different methods must be sinchronized with the purpose of the user's study of the economic time series.
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Sukhanova, E. I., S. Y. Shirnaeva, and E. G. Repina. "Binary Choice Models-based Assessment of Company’s Financial Sustainability." SHS Web of Conferences 62 (2019): 13002. http://dx.doi.org/10.1051/shsconf/20196213002.
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Assessment of financial sustainability is a key instrument that every company should use to successfully operate in the contemporary marketplace. In this paper profit was chosen as one of the sustainability indices and binary choice model logistics regression model (logit model) was built for that index. The research data for this study is drawn from accounting statements of a textile industry business in Samara city. A combination of econometric approaches was used in the data analysis. Binary choice models were adopted in this research. Then those models were estimated for validity. Also scenario forecasts methodology was employed in this study. Several logit models with a set of explanatory variables were constructed. After the comparison of those models the preferred one was determined. Based on that model a scenario for profits was forecasted including both the worst-case and the best-case ones. The average-case scenario forecast was also made.
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McCabe, Brendan P. M., and Christopher L. Skeels. "Distributions You Can Count On …But What’s the Point?" Econometrics 8, no. 1 (March 4, 2020): 9. http://dx.doi.org/10.3390/econometrics8010009.
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The Poisson regression model remains an important tool in the econometric analysis of count data. In a pioneering contribution to the econometric analysis of such models, Lung-Fei Lee presented a specification test for a Poisson model against a broad class of discrete distributions sometimes called the Katz family. Two members of this alternative class are the binomial and negative binomial distributions, which are commonly used with count data to allow for under- and over-dispersion, respectively. In this paper we explore the structure of other distributions within the class and their suitability as alternatives to the Poisson model. Potential difficulties with the Katz likelihood leads us to investigate a class of point optimal tests of the Poisson assumption against the alternative of over-dispersion in both the regression and intercept only cases. In a simulation study, we compare score tests of ‘Poisson-ness’ with various point optimal tests, based on the Katz family, and conclude that it is possible to choose a point optimal test which is better in the intercept only case, although the nuisance parameters arising in the regression case are problematic. One possible cause is poor choice of the point at which to optimize. Consequently, we explore the use of Hellinger distance to aid this choice. Ultimately we conclude that score tests remain the most practical approach to testing for over-dispersion in this context.
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Sava, Andrii, Vitalii Biskup, Inna Petruk, Nataliia Pokotylska, and Pavlina Fuhelo. "Substantiation of models for forecasting the regional social and economic rural development." Independent Journal of Management & Production 11, no. 8 (May 1, 2020): 626. http://dx.doi.org/10.14807/ijmp.v11i8.1223.
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The areas of application of mathematical and statistical methods for determining the prognostic model of rural development on the example of all Ukraine taking into account the economic, social and environmental component are explored in this article. Econometric modelling is the basis of the research methodology in this work. As a tool for analysis, a specific system of evaluation of selected indicators and their correlation and regression processing is applied. The results showed that the most influential factors for the level of development of rural territories of Ukraine are ten among the 60 indicators, grouped by the three components. They characterize the economic, social and environmental component and determine some impact on the functioning of the country's territorial system as a whole. The mathematical expression of the prognostic model of rural development is established using the regression analysis. A set of measures for regulation of rural development is developed on the basis of these results, which envisages the implementation of measures in the areas of software, normative and legal support. It is expected that this study will help public authorities make more effective decisions on addressing key issues of social and economic processes in rural areas of individual regions, Ukraine as a whole and other countries.
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Habeeb Hashim, Luay, and Ahmad Naeem Flaih. "Selecting the best model to fit the Rainfall Count data Using Some Zero Type models with application." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (August 26, 2019): 28–41. http://dx.doi.org/10.29304/jqcm.2019.11.2.555.
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Counts data models cope with the response variable counts, where the number of times that a certain event occurs in a fixed point is called count data, its observations consists of non-negative integers values {0,1,2,…}. Because of the nature of count data, the response variables are usually considered doing not follow normal distribution. Therefore, linear regression is not an appropriate method to analysis count data due to the skewed distribution. Hence, using linear regression model to analysis count data is likely to bias the results, under these limitations, Poisson regression model and “Negative binomial regression” are likely the appropriate models to analysis count data. Sometimes researchers may Counts more zeros than the expected. Count data with many Zeros leads to a concept called “Zero-inflation”. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the “zero- altered Poisson” (ZAP) model and the “negative binomial” model. In this paper, the models, Poisson, Negative Binomial, ZAP, and ZANB were been used to analyze rainfall data.
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Boiko, Vitalii, Olha Mulska, Ihor Baranyak, and Olha Levytska. "Ukrainian Migration Aspirations towards Germany: Analysis and Development Scenarios." Comparative Economic Research. Central and Eastern Europe 24, no. 1 (March 30, 2021): 65–84. http://dx.doi.org/10.18778/1508-2008.24.04.
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Based on the multiple regression model and scenario approach to forecasting, the article estimates the Ukrainian migration aspirations towards Germany (the scale of migration, the economic activity of migrants, and their economic benefits). It is argued that major transformations in the gender-age structure of the German population may cause a demographic crisis and labour market imbalances. Our projections indicate the growing role of foreign human resources in the German economy. When modeling the scale of emigration from Ukraine, an integrated approach is applied, considering not only trends of pull-push factors but also special aspects of the German migration policy and the outflow of 8–10 million Ukrainian migrant workers. Given the poor statistical data on the scale of labour emigration needed for constructing reliable econometric models, the use of expert forecasting method remains the most optimal technique for assessing potential migration flows and migration systems.
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Sterlacchini, Alessandro, and Francesco Venturini. "Knowledge Capabilities and Regional Growth: an Econometric Analysis for European Developed Regions." SCIENZE REGIONALI, no. 2 (July 2009): 45–70. http://dx.doi.org/10.3280/scre2009-002003.
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- By means of different econometric techniques, this paper estimates the relationship between the knowledge capabilities (i.e. the extent of R&D activities and higher education) and the GDP per capita growth of European developed regions. Along with structural features and initial income levels, our estimations account for the presence of spatial dependence. We find that regional growth is positively affected by the intensity of R&D and the share of adults with tertiary education. These findings are robust to alternative estimation procedures, as they arise from both OLS regressions with country demeaned variables and ML estimations of different spatial models. Keywords: Regional growth, knowledge capabilities, spatial dependence.Keywords: Crescita economica regionale, disuguaglianze regionali, regioni italiane.Parole chiave: Crescita regionale, capacitŕ tecnologiche, dipendenza spaziale.JEL classification: R11, O33, C31
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Li, Xiaona, Xiaosheng Wang, Sundaram Sampath, Mingchao Li, and Jiawei Wang. "A Regression Model Based on Uncertain Set." Asian Business Research 2, no. 3 (November 10, 2017): 33. http://dx.doi.org/10.20849/abr.v2i3.215.
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Traditional regression analysis is a method of statistical data analysis based on probability theory. Regression models play crucial roles in various branches of statistics including design of experiments, econometrics etc. In regression models, the dependent variable is assumed to be of stochastic nature where randomness enters via errors. Further, the independent variables are assumed to be of deterministic nature. The regression coefficients which explain the interdependency between the variables are assumed to be crisp quantities. Whenever, difficulty arises in expressing the values taken by the dependent variable in terms of crisp quantities, traditional regression models become irrelevant. This paper provides a framework for dealing with such situations on using the notion of uncertain sets of various forms. In this paper, a solution for this problem obtained via linear programming technique is introduced along with an illustrative example.
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Zholudeva, Vera V. "Econometric modeling of the higher education system in Yaroslavl region." Open Education 22, no. 4 (August 28, 2018): 12–20. http://dx.doi.org/10.21686/1818-4243-2018-4-12-20.
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The objective of the study is to analyze the models that describe the processes, running in the education. The article concludes that currently there are important changes and new trends in the sphere of higher education in Russia: the development of higher education is carried out in the conditions of the effective use of modern information technologies. The author emphasized the analysis of the use of distance learning technologies in the higher education system, which is especially important for our country because of the vast territory, the remoteness of many regions from the centers of educational services, due to the growing high cost of these services.The development of Internet technologies, multimedia in conjunction with the growing popularity, the Internet makes it possible to promote education to a new level. That is why today the demand for distance learning in Russia is equal, and in some universities has exceeded the demand for full-time education. In the near future distance learning will take on the main burden in the system of professional training and retraining of specialists due to its mobility, mass, availability and relative cheapness.Also in this article the basic quantitative regularities of the market of higher education of the Yaroslavl region in relation to the economy are determined. In the article, econometric modeling is chosen as a tool for management in the field of vocational education. This is due to the fact that it is able to identify trends and patterns of changes in the indicators of education development in the region, to determine the consequences of a development strategy that contributes to the understanding of the processes taking place in the higher education system. Econometric models, used for forecasting in the education system are analyzed; their advantages and disadvantages are revealed. Some of them are disclosed in the paper on the example of modeling the system of higher education in the Yaroslavl region.As the result of analyzing the statistical data of the regional office of Federal State Statistics Service in Yaroslavl region the following models were developed: a model that shows how the application of distance technologies in higher education is related to socio-economic indicators; the regression model of correlation between the system of higher education and the economy (GRP); the model of forecasting the number of students in different educational categories; the econometrical model of connectivity between the education expenditures and economic factors. The paper evaluates the impact of educational and demographic indicators on the education level index of the Yaroslavl region. The econometrical models, constructed in the research, represent the informational basis for modernization of regional higher education system and elaboration of social-economic strategies of the regional development. The proposed statistical tools of evaluation and forecasting education system development can be used for decision-making and planning on the regional level.
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Mathes, Tim, and Oliver Kuss. "Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics." Research Methods in Medicine & Health Sciences 2, no. 2 (February 17, 2021): 82–89. http://dx.doi.org/10.1177/2632084321996225.
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Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.
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Zrailo, Ivan, and Svitlana Hynkevych. "Modern Trends in the Functioning of Grain-Product Subcomplex of the Agricultural Sector of Ukraine in the Context of Implementing Its External Economic Potential." Economic and Regional Studies / Studia Ekonomiczne i Regionalne 13, no. 3 (September 1, 2020): 328–41. http://dx.doi.org/10.2478/ers-2020-0024.
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SummarySubject and purpose of work: The main purpose of the article is to research the state of grain-product subcomplex of the agro-industrial complex and identify the main trends in its functioning as a prerequisite for the implementation of the external economic potential of Ukraine.Materials and methods: The research used methodical tools for analysis, construction of econometric models, as well as open information sources of the State Statistics Service of Ukraine.Results: A set of factors (independent variables) were determined and the existence of theoretically their relationship with the production profitability of cereals and legumes (dependent variable) was substantiated. The regression equations for the investigated factors dependence were formed. The reliability of the econometric model was proved using Fisher’s criterion and Student’s t-criterion test.Conclusions: To increase the external economic potential of the grain-product subcomplex of the agro-industrial complex, it is advisable to focus on building rational mechanisms for managing the identified determinants of efficiency ensuring of the latter.
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Success Ikechi, Kanu, and Nwadiubu Anthony. "Global Oil Price Shocks and Effects on Economic Growth: An Econometric Investigation of Nigeria." INTERNATIONAL JOURNAL OF INNOVATION AND ECONOMIC DEVELOPMENT 6, no. 4 (2020): 7–26. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.64.2001.
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This study is necessitated for the reason that global oil price shocks are bound to affect the pace of economic growth in Nigeria. Given that Nigeria is a net oil-exporting country makes it particularly vulnerable to oil price fluctuations. The study made use of secondary data covering the period from 1990 to 2019. While the Augmented Dickey-Fuller unit root test was used for preliminary analysis; ordinary least square (OLS) regression analysis was used for short-run estimates. A combination of Johansen Co-integration test, Vector Auto Regression analysis, Granger causality test, Variance Decomposition, Impulse Response tests and the ARCH/ GARCH modelling techniques were used for long run estimation All the tests helped to confirm the integrity of our models. The findings of the study indicate that, in the short run, there was sufficient evidence to show that oil price changes have a significant effect on economic growth. For the long run test, the Trace statistics and Max Eigenvalue tests point to a case of non-integration. At a ten year horizon, 71.31% of the variance in economic growth is explained by shocks; while the balance of 28.69% was accounted for by the changes in the global price of crude oil. In other words, the growth of the Nigerian economy has to do with the economy itself and to some extent, fluctuations or instability associated with the global prices of oil shocks. The ARCH/GARCH analysis indicates that there exists a first-order ARCH effect and that the GARCH in mean term was also significant. Succinctly put, the above results suggest that though erratic, there is evidence of volatility clustering of oil price on economic growth in Nigeria. The study, therefore, recommends that Nigeria splay down on the continued dominance of primary production and export and low-value addition. There is a need for a paradigm shift. Nigeria’s economic growth should be driven by a diversified production structure, essentially driven by growth in manufacturing as it would increase job offer, raise productivity and incomes. Otherwise, the Nigerian economy will remain trepid, fragile and susceptible to shocks emanating from global oil price fluctuations. Poverty is likely to persist in Nigeria without a robust manufacturing sector where innovation and technology would improve value addition and raise productivity. Lastly, since an average economy is cyclical, whence the Nigerian economy can pull through the present economic recession occasioned by the Coronavirus pandemic, she must learn to save for the rainy day. Nigeria should draw lessons from history and from past mistakes in order to avert the vagaries associated with oil price volatilities and consequent budget alignment and re-alignments.
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Mostafa, Mohamed M., and Mohaned Al-Hamdi. "Kuwaiti consumers’ willingness to pay for environmental protection in Failaka island: a contingent valuation analysis." Tourism Review 71, no. 3 (August 15, 2016): 219–33. http://dx.doi.org/10.1108/tr-05-2016-0012.
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Purpose Evidence suggests that a growing number of consumers across the world are becoming more environmentally responsible in terms of their personal habits and lifestyles. In this paper, the authors aim to use both parametric and non-parametric econometric models to estimate Kuwaiti consumers’ willingness to pay (WTP) for environmental protection in Failaka island. Design/methodology/approach Contingent valuation methods based on log-logistic and log-normal regression models revealed that consumers in Kuwait are willing to pay a price premium of approximately 40 Kuwaiti dinars for environmental protection in Failaka island based on the double-bound dichotomous choice model. Findings Socio-economic variables have no significant influence on the respondent’s WTP. As expected income has a positive relationship with WTP and bid price has negative relationship with WTP to protect the environment in Failaka island. Originality/value This study highlight the fact that understanding consumers’ environmental-friendly behaviors may play an important role in formulating environmental policy changes to face complex problems as diverse as environmental pollution or environmental degradation.
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Dragutinovic-Mitrovic, Radmila. "Ogranicenja gravitacionog modela u ekonometrijskoj analizi spoljnotrgovinske razmene." Ekonomski anali 50, no. 166 (2005): 149–78. http://dx.doi.org/10.2298/eka0566149d.
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This work deals with econometric modeling of bilateral trade flows based on gravity model. Standard approach in most of previous empirical researches consisted of estimating bilateral trade potentials using gravity model and analysis of differences between the observed and predicted (potential) trade flows. Large differences were interpreted as the unexhausted foreign trade potentials. This work considers some limitations and problems of such approach mostly based on cross-section data. We consider alternative gravity model specifications with panel data and estimating procedures, as appropriate base for more precise estimates and conclusions. Furthermore, both theoretical and empirical analysis of econometric problems in panel data gravity model are carried out. Some of those problems have considered partially in previous empirical researches (for example autocorrelation in panels), but some of them have not considered at all, such as double endogenous regressors. Empirical results show that mentioned problems cause biased regression parameters estimates and consequently systematic variations of gravity model residuals (large systematic differences between observed and predicted). This makes conclusions on trade potentials between countries imprecise and unreliable.