Journal articles: 'Econometric modeling'

1

Jorgenson, Dale W. "Econometric general equilibrium modeling." Journal of Policy Modeling 38, no. 3 (May 2016): 436–47. http://dx.doi.org/10.1016/j.jpolmod.2016.02.004.

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QIN, Duo. "BAYESIAN ECONOMETRICS: The First Twenty Years." Econometric Theory 12, no. 3 (August 1996): 500–516. http://dx.doi.org/10.1017/s0266466600006836.

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This paper sketches the history of how Bayesian inference was adopted and utilized in econometrics during its first 20 years. It focuses on the causes of the Bayesian movement, the ways in which Bayesian inference was applied, the problems that the application was intended to solve, and the results achieved. It shows that Bayesian research has largely followed mainstream econometric development as far as the major econometric ideas and methods are concerned and that Bayesian reformulation of mainstream econometrics has nevertheless helped in deepening econometricians' understanding of many modeling problems by presenting them from a different angle.

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Lady, George M., and Carlisle E. Moody. "Econometric Modeling and Model Falsification." Advances in Pure Mathematics 09, no. 09 (2019): 762–76. http://dx.doi.org/10.4236/apm.2019.99036.

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Yusov, Anatoly B., and Antonina A. Kasatkina. "MODELING CYCLES IN ECONOMETRIC MODELS." Statistics and Economics, no. 1 (January 1, 2015): 176–78. http://dx.doi.org/10.21686/2500-3925-2015-1-176-178.

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Klein, Lawrence R., and Fyodor I. Kushnirsky. "Econometric modeling at mixed frequencies." Journal of Economic and Social Measurement 30, no. 4 (December 29, 2005): 251–77. http://dx.doi.org/10.3233/jem-2005-0258.

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Cai, Zongwu, Yongmiao Hong, and Shouyang Wang. "Econometric Modeling and Economic Forecasting." Journal of Management Science and Engineering 3, no. 4 (December 2018): 178–82. http://dx.doi.org/10.3724/sp.j.1383.304010.

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Jin, Hui, and Dale W. Jorgenson. "Econometric modeling of technical change." Journal of Econometrics 157, no. 2 (August 2010): 205–19. http://dx.doi.org/10.1016/j.jeconom.2009.12.002.

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McFadden, Daniel. "Econometric modeling of locational behavior." Annals of Operations Research 18, no. 1 (December 1989): 1–15. http://dx.doi.org/10.1007/bf02097792.

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Klein, L. R. "Econometric Modeling at Mixed Frequencies." Journal of Mathematical Sciences 133, no. 4 (March 2006): 1445–48. http://dx.doi.org/10.1007/s10958-006-0059-0.

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Balash, O. S. "Econometric Modeling of Spatial Interaction." Izvestiya of Saratov University. Economics. Management. Law 12, no. 3 (2012): 30–35. http://dx.doi.org/10.18500/1994-2540-2012-12-3-30-35.

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Watts, Michael, William E. Becker, and William B. Walstad. "Econometric Modeling in Economic Education Research." Journal of Economic Education 20, no. 2 (1989): 223. http://dx.doi.org/10.2307/1182736.

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Shakkaliyev, Arman A. "Econometric Modeling of the Markets Boundaries." Journal of Modern Competition 16, no. 4 (August 31, 2022): 133–43. http://dx.doi.org/10.37791/2687-0657-2022-16-4-133-143.

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The relevance of the study is due to the special importance of correctly determining the product boundaries of the cross-border markets of the Eurasian Economic Union (EAEU) in the process of antimonopoly regulation. An erroneous definition of such parameters leads to an incorrect definition of the product, and, as a result, to a narrowing or expansion of the scope and geography of its circulation, an increase or blurring of the shares of entities operating in the markets, a false level of concentration, as well as a distortion of the criteria for delineating the competence of the EEC and national authorities in terms of abuse of dominance. This process can be much more complicated when it comes to goods that are subject to technical regulations, and due to their specificity, there are a significant number of types of goods that are generally similar in some respects, but differ in others. In this regard, within the framework of the study, an attempt was made to assess the possibilities of using certain methods of econometric modeling when establishing product boundaries for cross-border markets for such goods using the example of non-electric initiation systems. The research hypothesis is based on the following assumptions. First: technical regulations and standards not only act as market entry barriers, but also have a decisive influence on the definition of a market within which goods equivalent to each other circulate. Second: when determining the product boundaries of markets operating in the system of technical regulation, along with traditional and normatively fixed approaches, multiple regression models can be used and a correlation between the cost of a product and its characteristics, as well as empirical analysis data that allows segmenting goods according to specific boundaries, can be used.

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Jarque, Carlos M. "Sample Splitting and Applied Econometric Modeling." Journal of Business & Economic Statistics 5, no. 2 (April 1987): 267. http://dx.doi.org/10.2307/1391907.

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Carlevaro, Fabrizio, Jean‐Luc Bertholet, Jean‐Paul Chaze, and Patrick Taffé. "Engineering‐Econometric Modeling of Energy Demand." Journal of Energy Engineering 118, no. 2 (August 1992): 109–21. http://dx.doi.org/10.1061/(asce)0733-9402(1992)118:2(109).

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Jarque, Carlos M. "Sample Splitting and Applied Econometric Modeling." Journal of Business & Economic Statistics 5, no. 2 (April 1987): 267–74. http://dx.doi.org/10.1080/07350015.1987.10509585.

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Fu, Xinkai, Stian M. Ueland, and Elsa Olivetti. "Econometric modeling of recycled copper supply." Resources, Conservation and Recycling 122 (July 2017): 219–26. http://dx.doi.org/10.1016/j.resconrec.2017.02.012.

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17

Richard, Jean-François. "Book Review: Econometric Modeling and Inference." Econometric Reviews 30, no. 5 (April 25, 2011): 577–81. http://dx.doi.org/10.1080/07474938.2011.553565.

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Phillips, Peter C. B., and Donggyu Sul. "Transition Modeling and Econometric Convergence Tests." Econometrica 75, no. 6 (November 2007): 1771–855. http://dx.doi.org/10.1111/j.1468-0262.2007.00811.x.

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Bień-Barkowska, K. "Econometric Modeling of Inter-Order Durations." Acta Physica Polonica A 127, no. 3a (March 2015): A—7—A—12. http://dx.doi.org/10.12693/aphyspola.127.a-7.

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Orlova, I. V. "USING FREE SOFTWARE FOR ECONOMETRIC MODELING." Фундаментальные исследования (Fundamental research), no. 1 2023 (2023): 81–89. http://dx.doi.org/10.17513/fr.43424.

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21

Spanos, Aris. "On Rereading Haavelmo: A Retrospective View of Econometric Modeling." Econometric Theory 5, no. 3 (December 1989): 405–29. http://dx.doi.org/10.1017/s0266466600012597.

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The main aim of the paper is to reevaluate the methodological contributions of Tinbergen and Haavelmo in the context of the current discussions on econometric modeling and propose a reformulation of the Haavelmo methodology. The paper argues that the textbook methodology constitutes a less flexible version of Tinbergen's approach and apart from the probabilistic language, it has little in common with the methodology in Haavelmo's 1944 monograph, commonly acknowledged as having founded modern econometrics. The methodology in this monograph includes several important elements which have either been discarded or never fully integrated within the textbook approach. A re-synthesis of these elements gives rise to an alternative methodological framework. This framework can be used to meet most of the objections to the textbook methodology and provides a framework in the context of which the recent methodological controversies can be evaluated.

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Stock, James H., and Mark W. Watson. "Twenty Years of Time Series Econometrics in Ten Pictures." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 59–86. http://dx.doi.org/10.1257/jep.31.2.59.

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This review tells the story of the past 20 years of time series econometrics through ten pictures. These pictures illustrate six broad areas of progress in time series econometrics: estimation of dynamic causal effects; estimation of dynamic structural models with optimizing agents (specifically, dynamic stochastic equilibrium models); methods for exploiting information in “big data” that are specialized to economic time series; improved methods for forecasting and for monitoring the economy; tools for modeling time variation in economic relationships; and improved methods for statistical inference. Taken together, the pictures show how 20 years of research have improved our ability to undertake our professional responsibilities. These pictures also remind us of the close connection between econometric theory and the empirical problems that motivate the theory, and of how the best econometric theory tends to arise from practical empirical problems.

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23

Goldberger, Arthur S. "Interviewed by Nicholas M. Kiefer." Econometric Theory 5, no. 1 (April 1989): 133–60. http://dx.doi.org/10.1017/s0266466600012299.

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Econometrics as practiced by Arthur (Art) Goldberger demonstrates extraordinary sensitivity to issues of measurement and model specification, and unusual care and caution in interpretation of results, as well as a thorough and comprehensive mastery of econometric theory. His landmark 1964 book, Econometric Theory, set a new standard of rigor in econometrics, and at the same time treated the important problems posed by limited and qualitative dependent variables years before any other text. Art Goldberger's work ranges from early contributions to macro modeling through demand analysis, multivariate modeling with latent variables, and models for sample selectivity, to his highly regarded work on important social issues of heritability of IQ, effectiveness of public versus private schools, and measurement of salary discrimination. Goldberger's influential work, especially on modeling latent or unobservable variables, is widely known and applied in sociology and psychology as well as in economics. Art has been at Wisconsin for many years, and this association is an important reason for Wisconsin's continuing reputation as a leading center for quantitative social sciences.The quality and influence of Art Goldberger's work has earned him many professional honors. He is a Fellow of the Econometric Society, the American Statistical Association, the American Academy of Arts and Sciences, and the American Association for the Advancement of Science, and has twice been a Guggenheim Fellow. He is a Distinguished Fellow of the American Economic Association and a member of the National Academy of Sciences. Art gave the Woytinsky Lecture at the University of Michigan in 1985.This interview took place on May 5 in Art Goldberger's office overlooking Lake Mendota. Art's remarks cover a wide range of topics, and I hope they are of interest to social scientists generally as well as to econometricians.

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Skrypnik, Dmitry. "The economic modeling development. An econometric aspect." Economics and the Mathematical Methods 58, no. 2 (2022): 32. http://dx.doi.org/10.31857/s042473880020013-3.

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The experience of development of applied predominantly macroeconomic modeling is considered. A comparison is made of the so-called non-structural approach, which is not strictly substantiated by economic theory, with the structural approach, where such a justification is the basis. It is shown that the achievements of structural and non-structural modeling have largely contributed to the blurring of the clear line between these two approaches. An analysis of researches also allows us to conclude that the rapid development of non-structural modeling led to the emergence of effective modeling methods that demonstrate good predictive properties, often surpassing the quality of structural models. In addition, it is shown that the difficulties arising at a certain stage in the development of the non-structural approach were often eliminated within the framework of the same approach, but with the advent of new methods. The presence of a close connection between the structural and non-structural approaches and the need for a hybrid approach to modeling real economic systems and processes, – that is, an approach in which some behavioral mechanisms and their corresponding structural parameters are explicitly introduced into the model, while the remaining elements are identified on the basis of methods of non-structural approach.

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Mamonov, Konstantin, Volodymyr Velychko, Evgeny Grуtskov, Sergey Haidenko, Valentina Prasol, and Alireza Abolhacanzad. "Econometric modeling of investment attractiveness of enterprises." Ukrainian Metrological Journal, no. 4 (December 30, 2020): 57–63. http://dx.doi.org/10.24027/2306-7039.4.2020.224305.

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Campos, Julia, and Neil R. Ericsson. "Econometric Modeling of Consumers' Expenditure in Venezuela." International Finance Discussion Paper 1988, no. 325 (June 1988): 1–67. http://dx.doi.org/10.17016/ifdp.1988.325.

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Çetin, Hüseyin. "Econometric Modeling of Turkish Participatory Banks Deposits." International Journal of Trade, Economics and Finance 5, no. 4 (August 2014): 322–26. http://dx.doi.org/10.7763/ijtef.2014.v5.391.

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Osipov, Alexander Leonidovich, and Veronika Pavlovna Trushina. "Econometric modeling of job satisfaction in education." KANT 40, no. 3 (March 2021): 58–62. http://dx.doi.org/10.24923/2222-243x.2021-40.11.

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The purpose of the study is to establish a causal relationship between job satisfaction in the field of education in the Russian Federation, depending on the satisfaction with: working conditions; opportunities for professional growth; remuneration. The article deals with the problem of modeling socio-economic satisfaction with work in the field of education. Based on the correlation analysis, linear and nonlinear models of the interrelationships of factors related to this problem are formed. Econometric models have been developed to effectively assess the level of job satisfaction of employees employed in the field of education. The scientific novelty lies in the study of new models that are statistically superior to domestic developments in this field of activity. As a result, the most significant factors affecting the level of job satisfaction were identified.

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Tkachenko, Irina N., and Alexandr A. Zlygostev. "Stakeholder risk modeling: An econometric analysis experience." Izvestiya of Saratov University. New Series. Series Economics. Management. Law 21, no. 3 (August 25, 2021): 271–87. http://dx.doi.org/10.18500/1994-2540-2021-21-3-271-287.

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Introduction. The article is devoted to the study of stakeholder risks and the possibilities of their assessment. The methodological framework of the study was formed on the basis of the corporate governance theory and its stakeholder model. Theoretical analysis. On the basis of the the Russian-language and English-language publications’ general review, carried out by the authors, it is concluded that there is no clear generally accepted toolkit for assessing stakeholder risk. Assessment of stakeholder risks was most developed in project approaches. Methods. The authors propose an approach for assessing the stakeholder risks of an organization as a whole based on the search for a balance of interests through modeling the contributions and benefits of stakeholders and the gaps between contributions and benefits. The empirical base of the study was made up of data on flour-grinding and bakery enterprises of the Sverdlovsk region for 2010–2019. The unbalanced dashboard for analysis included 130 observations across 28 companies. Results. The methodology has been tested on the example of a comparative analysis of “more successful” and “less successful” companies in terms of revenue. Econometric models of the stakeholder contribution to revenue have been built. Stakeholder risk maps were built, which made it possible to identify the most influential stakeholders in terms of their contribution to revenue and the most risky ones in terms of the gap level. Conclusions. When assessing stakeholder risks, a broad outlook and a system of various methods for assessing stakeholder risks are required, as well as a willingness to take into account factors that may go beyond the framework of the models. The results and conclusions of the article can become a theoretical platform for further research. Further research on this topic can be related to expanding the understanding of the imbalance of interests by taking into account the subjective assessments of stakeholders and experts. For these purposes, it is necessary to adapt existing corporate surveys to accounting for stakeholder value and stakeholder risks.

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Renfro, Charles G. "The early development of econometric modeling languages." Journal of Economic and Social Measurement 29, no. 1-3 (July 20, 2004): 145–66. http://dx.doi.org/10.3233/jem-2004-0228.

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Stavytskyy, Andriy, and Daria Martynovych. "THE ECONOMETRIC MODELING OF UKRAINIAN MACROECONOMIC TENDENCIES." Ekonomika 91, no. 1 (January 1, 2012): 79–92. http://dx.doi.org/10.15388/ekon.2012.0.906.

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Econometric models are widely used in economic policies of many states. They help to build a great variety of econometric systems for every country and take into account the specifics of each economy.In this article, the structural macroeconomic models that describe the main aspects of the economic policy were applied. The interdependence between the level of inflation, the value of investment, savings, consumption, export and import transactions, taxes on the foreign trade were defined based on the analysis of the key macroeconomic parameters of Ukraine. After investigating all economic indicators, they were transformed into stationary time series for a correct use in the model. In addition, heteroscedasticity and autocorrelation of residuals were excluded in all econometric equations.As a result, the research shows that a large share of black economy leads to a rather high level of inflation in the state, because its value is primarily determined by expectations of the population under such circumstances. The paper indicates that the further export growth leads to a lower consumption growth and also to a lower growth of savings. Such a situation indicates an insufficient development of the domestic market. Investment growth has been fund not to be directly linked to consumption increase and economic development in general. Unfortunately, the main sources of investment in Ukraine are the funds of enterprises and foreign sources. The analysis shows a need to encourage public involvement into investment processes. For example, the creation of public–private partnerships is especially useful while implementing infrastructural projects.

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Moore, Elvin J. "On System-Theoretic Methods and Econometric Modeling." International Economic Review 26, no. 1 (February 1985): 87. http://dx.doi.org/10.2307/2526529.

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Mietule, Iveta, and Gajane Gukasjan. "ECONOMETRIC MODELING OF THE ECONOMY OF LATVIA." Latgale National Economy Research 1, no. 5 (October 21, 2013): 167. http://dx.doi.org/10.17770/lner2013vol1.5.1158.

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The article is devoted to the estimation of econometric models of the Latvian economy. The Klein's simplified macroeconomic model of the Latvian economy is discussed. The endogenous variables are consumption, net investment, gross domestic product (excluding net exports and additions to reserves). An exogenous variable is the government spending. The model is just-identified, and Two-stage least squares (2SLS) method provides consistent estimates of the parameters of a structural equation. The modified Keynеsian model was also considered, where the lagged variable - gross domestic product of the previos period is presented. It is proved that the model is over-identified, and the Two-stage least squares (2SLS) method provides estimates of the parameters of a structural equation. We have estimated the models with annual time-series data of the Latvia economy for the years 1995 through 2011 (at basic prices in 2000).

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Pokharel, Shaligram, Aalieh Ahmade, Fajr Al-Ansari, Hana Al Allaf, Mina Daneshvar, and Ahmad Mohd AbdalQadir. "Analysis of energy demand through econometric modeling." Qatar Foundation Annual Research Forum Proceedings, no. 2012 (October 2012): AHP28. http://dx.doi.org/10.5339/qfarf.2012.ahp28.

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Li, Gang, Haiyan Song, and Stephen F. Witt. "Recent Developments in Econometric Modeling and Forecasting." Journal of Travel Research 44, no. 1 (August 2005): 82–99. http://dx.doi.org/10.1177/0047287505276594.

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Reynolds, Dennis, Imran Rahman, and William Balinbin. "Econometric modeling of the U.S. restaurant industry." International Journal of Hospitality Management 34 (September 2013): 317–23. http://dx.doi.org/10.1016/j.ijhm.2013.04.003.

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37

Lesage, James P., and Michael Magurat. "ECONOMETRIC MODELING OF INTERREGIONAL LABOR MARKET LINKAGES*." Journal of Regional Science 26, no. 3 (August 1986): 567–77. http://dx.doi.org/10.1111/j.1467-9787.1986.tb01060.x.

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LeSage, James P., and R. Kelley Pace. "SPATIAL ECONOMETRIC MODELING OF ORIGIN-DESTINATION FLOWS*." Journal of Regional Science 48, no. 5 (December 2008): 941–67. http://dx.doi.org/10.1111/j.1467-9787.2008.00573.x.

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Dolk, Daniel R., and Donald J. Kridel. "An active modeling system for econometric analysis." Decision Support Systems 7, no. 4 (November 1991): 315–28. http://dx.doi.org/10.1016/0167-9236(91)90061-f.

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40

Diebold, Francis X., Eric Ghysels, Per Mykland, and Lan Zhang. "Big data in dynamic predictive econometric modeling." Journal of Econometrics 212, no. 1 (September 2019): 1–3. http://dx.doi.org/10.1016/j.jeconom.2019.04.017.

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Palm, F. C. "Structural econometric modeling and time series analysis." Applied Mathematics and Computation 20, no. 3-4 (November 1986): 349–64. http://dx.doi.org/10.1016/0096-3003(86)90011-1.

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Tuskov, Andrey A., and Daria A. Goldueva. "ECONOMETRIC MODELING OF THE INTERNATIONAL HAPPINESS INDEX." Krasnoyarsk Science 11, no. 4 (December 29, 2022): 77–95. http://dx.doi.org/10.12731/2070-7568-2022-11-4-77-95.

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Purpose – offer different variants of building multiple regression models to describe real economic processes. Method or methodology of the work: the article uses the econometric method of data analysis. Results: it was shown that econometric methods are effective in describing the hidden dependencies of the economic system. The study proposes several ways to build regression models: the classical version using the matrix of pairwise correlation coefficients (correlation pleiades method), the method of inflation factors, denying the presence of “moderately strong” dependence, after which the removal of “unnecessary” factors was performed using Gretl tool “Test for excess variables”, as well as using the principal components method to consider the influence of all factors on the dependent variable. In order to facilitate the interpretation of the results obtained, the article shows the transition from the principal components to the original data. The model is built on a publicly available dataset available to the research community. The sphere of application of the results: in practice, the results are useful for planning effective strategies for the development of individual states, as well as a manual for beginner econometricians wishing to learn different approaches to econometric modeling with the use of effective tools.

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ZEUG-ŻEBRO, Katarzyna. "Spatial modeling of investment activity of enterprises in service sector." Scientific Papers of Silesian University of Technology. Organization and Management Series 2021, no. 150 (2021): 357–68. http://dx.doi.org/10.29119/1641-3466.2021.150.27.

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Purpose: Due to the visible disproportions, the problem of innovation is increasingly often perceived regionally. These inequalities result from the concentration of knowledge, resources and the amount of expenditure on innovation in a few regions. The aim of the paper is to study the spatial dependence between Polish voivodships in terms of expenditure on innovative activities in enterprises in the service sector. Design/methodology/approach: For the selected variable conditioning the innovative activity of the enterprise, a classical econometric model will be built and the necessity to include the spatial factor in the modeling process will be verified. For this purpose, two spatial models will be considered: the spatial error model and spatial lag model. Findings: During the study, the hypothesis on the legitimacy of introducing spatial relationships to the econometric model describing the shaping of the amount of expenditure on innovative activities in enterprises in the service sector in Polish voivodeships was verified. The hypothesis has been verified positively – there are spatial relationships between the examined objects. Research limitations/implications: The need to take into account the spatial factor in the econometric model results in taking into account spatial estimation methods. The research used selected spatial models. Due to the limitations resulting from the availability of source data, the analysis was conducted only for voivodships in selected years. The analysis should be further deepened, e.g., by even more precise identification of the models and taking into account other neighborhood matrices - only the first-order neighborhood matrix was included in the study. Practical implications: Modeling the phenomenon of innovation. Social implications: An essential condition influencing the innovative activity of enterprises is their environment. It is the regional factors that largely influence innovation and faster development of enterprises. Originality/value: Introducing spatial relationships to the econometric model of outlays on innovative activities of enterprises in the service sector.

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Callan, Scott J., William A. Barnett, Ernst R. Berndt, and Halbert White. "Dynamic Econometric Modeling: Proceedings of the Third International Symposium in Economic Theory and Econometrics." Southern Economic Journal 56, no. 2 (October 1989): 541. http://dx.doi.org/10.2307/1059236.

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Zapata, Hector O., and Supratik Mukhopadhyay. "A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing." Journal of Risk and Financial Management 15, no. 11 (November 17, 2022): 535. http://dx.doi.org/10.3390/jrfm15110535.

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Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for regularization, modeling nonlinearity, and improved out-of-sample prediction. This article conducted a comprehensive, objective, and quantitative bibliometric analysis of this growing literature using Web of Science (WoS) data. We identified trends in the literature over the past decade, the geographical distribution of articles, authorship, and institutional contributions worldwide. The paper also identifies the dominant literature using citations in WoS and discusses computational algorithms that are expanding the econometric frontiers in asset pricing. The top cited papers were reviewed, highlighting their contribution. The limitations of ML learning methods and recent advances in ML were used to provide a conic view to future ML econometric practice.

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Chen, Shu-Heng, Chia-Ling Chang, and Ye-Rong Du. "Agent-based economic models and econometrics." Knowledge Engineering Review 27, no. 2 (April 26, 2012): 187–219. http://dx.doi.org/10.1017/s0269888912000136.

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AbstractThis paper reviews the development of agent-based (computational) economics (ACE) from an econometrics viewpoint. The review comprises three stages, characterizing the past, the present, and the future of this development. The first two stages can be interpreted as an attempt to build the econometric foundation of ACE, and, through that, enrich its empirical content. The second stage may then invoke a reverse reflection on the possible agent-based foundation of econometrics. While ACE modeling has been applied to different branches of economics, the one, and probably the only one, which is able to provide evidence of this three-stage development is finance or financial economics. We will, therefore, focus our review only on the literature of agent-based computational finance, or, more specifically, the agent-based modeling of financial markets.

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Zbyrowski, Rafał. "Valuation of immovables based on an econometric models." Equilibrium 4, no. 1 (June 30, 2010): 241–52. http://dx.doi.org/10.12775/equil.2010.019.

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The main purpose of this analysis is to judge the practical utility of mass-valuation methods – particularly the econometric modelling for valuation of dwellings. One of the most important part of this article is a research of value of flats in Warsaw based on a database provided by LOCUS real estate agency. The analysis have been made in three different ways. The first approach concerns the value of flats during the boom in real estate market. In this part there were verified the major factors and trends that have impact on the market value in nine Warsaw districts. Then there were showed exemplary econometric models of value of flats in Warsaw – created for sample just before the recession in real estate market. The new idea of valuation by econometric methods is to apply cross-section models. In the second part of this article database from the period of recession was analyzed. It was mentioned that statistical features of the sample had changed and the precision of the modeling had decreased. The author presents a cross-section model that would be applied for valuation of flat in current real estate market. In the third part there were presented suggestions and proposals for application of econometric models to estimate the value of flats during the recession and before. Moreover considerations have been supported by a group of practical problems connected with econometric modeling of value in real estate market.

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Richards, Timothy J., Paul M. Patterson, and Pieter Van Ispelen. "Modeling Fresh Tomato Marketing Margins: Econometrics and Neural Networks." Agricultural and Resource Economics Review 27, no. 2 (October 1998): 186–99. http://dx.doi.org/10.1017/s106828050000650x.

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Abstract:

This study compares two methods of estimating a reduced form model of fresh tomato marketing margins: an econometric and an artificial neural network (ANN) approach. Model performance is evaluated by comparing out-of-sample forecasts for the period of January 1992 to December 1994. Parameter estimates using the econometric model fail to reject a dynamic, imperfectly competitive, uncertain relative price spread margin specification, but misspecification tests reject both linearity and log-linearity. This nonlinearity suggests that an inherently nonlinear method, such as a neural network, may be of some value. The neural network is able to forecast with approximately half the mean square error of the econometric model, but both are equally adept at predicting turning points in the time series.

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Batrancea, Ioan, Ioan-Dan Morar, Ema Masca, Sabau Catalin, and Liviu Bechis. "Econometric Modeling of SME Performance. Case of Romania." Sustainability 10, no. 1 (January 17, 2018): 192. http://dx.doi.org/10.3390/su10010192.

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50

Watts, Martin. "Occupational Gender Segregation: Index Measurement and Econometric Modeling." Demography 35, no. 4 (November 1998): 489. http://dx.doi.org/10.2307/3004016.

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Journal articles on the topic 'Econometric modeling'

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