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Academic literature on the topic 'Econometric models'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Econometric models"

1

Hozer, Józef, and Mariusz Doszyń. "Econometric Models of Propensities." Folia Oeconomica Stetinensia 6, no. 1 (January 1, 2007): 15–25. http://dx.doi.org/10.2478/v10031-007-0008-1.

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Econometric Models of Propensities Human being is one of the most important sources of causative forces of events that assemble economical processes. Working out the effective tools that enable measurement of the impact of people on socio-economic processes is necessary in analyzing, troubleshooting and forecasting. In the article the issues of calculating propensities by means of properly specified econometrics models were presented. The definition of propensity was introduced. Questions connected with topic of propensities were presented in context of concepts promoted by Szczecin school of econometrics (pentagon of sources of causative forces, types of relationships in economics, geometric interpretation of personality, broom of events). Econometric models, useful in analyzing propensities, were classified on primary models, econometrics models of average propensities and econometrics models of marginal propensities. Connections between the models were described. Settlement of analytical shapes of characterized models was mentioned. In an empirical example the presented methods were used to analyze average and marginal propensity to consumption of alcoholic beverages and tobacco in the households of employees in manual labour positions in Poland in years 1993-2005.

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Silahtaroğlu, Yenilmez Oğuz. "Machine Learning Integration in Econometric Models." Next Generation Journal for The Young Researchers 8, no. 1 (November 11, 2024): 77. http://dx.doi.org/10.62802/8c33p210.

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The integration of machine learning (ML) into econometric models represents a transformative advancement in the field of econometrics, enabling researchers to tackle complex, high-dimensional datasets while maintaining the interpretability and rigor of traditional econometric approaches. This research investigates the synergies between machine learning and econometrics, focusing on how ML techniques can enhance model flexibility, predictive accuracy, and causal inference in economic analysis. By leveraging methods such as regularization, ensemble learning, and deep learning, the study explores applications in macroeconomic forecasting, policy evaluation, and market analysis. Furthermore, it addresses the challenges of balancing interpretability with predictive performance, emphasizing the need for hybrid frameworks that merge machine learning's adaptability with econometrics' theoretical foundation. The findings demonstrate the potential of ML-enhanced econometric models to revolutionize economic research and policy-making by providing robust, data-driven insights.

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Gruszczyński, Marek. "Accounting and Econometrics: From Paweł Ciompa to Contemporary Research." Journal of Risk and Financial Management 15, no. 11 (November 4, 2022): 510. http://dx.doi.org/10.3390/jrfm15110510.

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This paper examines the little-known connection between econometrics and accounting invoked by Paweł Ciompa, who first introduced the term econometrics in 1910. Since then, research in accounting and in statistical (econometric) analysis has developed in parallel. It is argued that contemporary accounting research is methodologically closer to econometrics than ever before. This paper concentrates on the accounting origins of econometrics and on the econometric methodologies currently in use in accounting research, beginning with Paweł Ciompa’s introduction of the term econometrics in accounting. The major contribution of this paper is a review of the occurrence of econometric methods in five leading journals in accounting research. The author identified 246 papers, and these were examined regarding the use of econometric methods. Two-thirds of the papers used methodologies that belong to econometrics—specifically, to financial microeconometrics. The most common methods were panel data models, qualitative variables models, and causality models.

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Domínguez, Manuel A., and Ignacio N. Lobato. "A SIMPLE OMNIBUS OVERIDENTIFICATION SPECIFICATION TEST FOR TIME SERIES ECONOMETRIC MODELS." Econometric Theory 31, no. 4 (October 27, 2014): 891–910. http://dx.doi.org/10.1017/s0266466614000644.

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Despite their theoretical advantages, Integrated Conditional Moment (ICM) specification tests are not commonly employed in the econometrics practice. An important reason is that the employed test statistics are nonpivotal, and so critical values are not readily available. This article proposes an omnibus test in the spirit of the ICM tests of Bierens and Ploberger (1997, Econometrica 65, 1129–1151) where the test statistic is based on the minimized value of a quadratic function of the residuals of time series econometric models. The proposed test falls under the category of overidentification restriction tests started by Sargan (1958, Econometrica 26, 393–415). The corresponding projection interpretation leads us to propose a straightforward wild bootstrap procedure that requires only linear regressions to estimate the critical values irrespective of the model functional form. Hence, contrary to other existing ICM tests, the critical values are easily calculated while the test preserves the admissibility property of ICM tests.

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de Paula, Áureo. "Econometric Models of Network Formation." Annual Review of Economics 12, no. 1 (August 2, 2020): 775–99. http://dx.doi.org/10.1146/annurev-economics-093019-113859.

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This article provides a selective review of the recent literature on econometric models of network formation. I start with a brief exposition on basic concepts and tools for the statistical description of networks; then I offer a review of dyadic models, focusing on statistical models on pairs of nodes, and I describe several developments of interest to the econometrics literature. I also present a discussion of nondyadic models in which link formation might be influenced by the presence or absence of additional links, which themselves are subject to similar influences. This argument is related to the statistical literature on conditionally specified models and the econometrics of game theoretical models. I close with a (nonexhaustive) discussion of potential areas for further development.

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Bolton, Roger. "REGIONAL ECONOMETRIC MODELS*." Journal of Regional Science 25, no. 4 (November 1985): 495–520. http://dx.doi.org/10.1111/j.1467-9787.1985.tb00320.x.

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Ditzen, Jan, and Simon Reese. "xtnumfac: A battery of estimators for the number of common factors in time series and panel-data models." Stata Journal: Promoting communications on statistics and Stata 23, no. 2 (June 2023): 438–54. http://dx.doi.org/10.1177/1536867x231175305.

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In this article, we introduce a new community-contributed command, xtnumfac, for estimating the number of common factors in time-series and panel datasets using the methods of Bai and Ng (2002, Econometrica 70: 191–221), Ahn and Horenstein (2013, Econometrica 81: 1203–1227), Onatski (2010, Review of Economics and Statistics 92: 1004–1016), and Gagliardini, Ossola, and Scaillet (2019, Journal of Econometrics 212: 503–521). Common factors are usually unobserved or unobservable. In time series, they influence all predictors, while in paneldata models, they influence all cross-sectional units at different degrees. Examples are shocks from oil prices, inflation, or demand or supply shocks. Knowledge about the number of factors is key for multiple econometric estimation methods, such as Pesaran (2006, Econometrica 74: 967–1012), Bai (2009, Econometrica 77: 1229–1279), Norkute et al. (2021, Journal of Econometrics 220: 416–446), and Kripfganz and Sarafidis (2021, Stata Journal 21: 659–686). This article discusses a total of 10 methods to estimate the number of common factors. Examples based on Kapetanios, Pesaran, and Reese (2021, Journal of Econometrics 221: 510–541) show that U.S. house prices are exposed to up to 10 common factors. Therefore, when one fits models with house prices as a dependent variable, the number of factors must be considered.

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Dokumacı, Melis. "AI-Driven Econometric Models for Legal Issues." Human Computer Interaction 8, no. 1 (December 23, 2024): 137. https://doi.org/10.62802/btfvze98.

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Artificial intelligence (AI) is reshaping the landscape of econometric modeling, offering innovative tools to address complex legal issues involving predictive analysis, resource allocation, and policy evaluation. This research explores the application of AI-driven econometric models to legal challenges, focusing on areas such as contract enforcement, intellectual property disputes, and regulatory compliance. By integrating machine learning with traditional econometric techniques, these models enhance the precision and adaptability of legal forecasts and decision-making processes. Key methodologies include the use of natural language processing (NLP) for legal text analysis, deep learning for pattern recognition in case law, and game theory to evaluate strategic interactions in legal contexts. The study highlights the potential of AI to improve efficiency, reduce bias, and facilitate equitable outcomes in legal systems. Challenges such as data privacy, interpretability, and ethical considerations are also addressed. By bridging AI and econometrics, this research aims to provide a robust framework for advancing legal analytics, contributing to more informed policy-making and judicial processes.

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Maziarz, Mariusz. "‘Emerging contrary result’ phenomenon and scientific realism." Panoeconomicus, no. 00 (2020): 24. http://dx.doi.org/10.2298/pan171218024m.

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The article is aimed at reconsidering the question if the project of econometrics can be read in line with scientific realism. Previously, the methodological literature focused on the philosophy of econometrics, voices criticizing realist interpretations of econometrics were raised. The criticism was aimed at showing that econometric models lack robustness. The use of slightly different methods leads to obtaining different and often contrary models what supposedly undermine the project of econometrics. In this article, I aim at offering a new argument in defence of the current practice of the economists devoted to the empirical branch of macroeconomics. To do so, I apply M?ki?s (2009) model of representation to three case studies of contradictory pairs of econometric models and argue that contrary results are not necessarily a drawback of econometrics. Instead, the seemingly contradictory pairs of models are useful in various contexts constituted by their purpose and audience.

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Garcia d'Acuña, Eduardo. "Econometric models for planning." CEPAL Review 1990, no. 41 (September 13, 1990): 193–98. http://dx.doi.org/10.18356/75fb3d71-en.

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Dissertations / Theses on the topic "Econometric models"

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Fahs, Faysal Habib. "Essays in the estimation of systems of limited dependent variables with application to demand systems." Online access for everyone, 2008. http://www.dissertations.wsu.edu/Dissertations/Summer2008/F_Fahs_072508.pdf.

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Conradie, Tiaan. "The South African economy and internationally fuelled business cycles: an econometric analysis." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/4354.

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The objective of this study is to understand the dynamics of international monetary policy and the relationship that exists between larger more developed economies and smaller less developed economies within a policy context. The 2008 financial crisis has caused intense revival of Austrian economics due to the monetary nature of the recession caused as a subsequent effect of the stock/housing market collapse that occurred in 2007. One factor of the 2008 financial crisis that created intense concern was the extent to which the slowdown in economic activity was able to be transmitted across international borders. The South African economy was not isolated from the financial crisis by any means and experienced a significant slowdown in economic growth. By making use of data collected from the Federal Reserve Bank of St. Louis and the appropriate econometric techniques, a model is developed to study the dynamics between United States monetary policy and the South African economy. The Austrian School provides a sound theoretical framework that allows for the specification of testable propositions to verify the validity of an “Austrian” internationally transmitted business cycle. Using United States money supply, South African private consumption, South African gross fixed capital formation and the South African current account, a vector autoregressive model is specified to analyse the dynamics behind the United States and South African economy. The results of the empirical test all confirm the theoretical prescriptions developed in the literature review that monetary growth in the United States raise consumption, investment and improve the current account balance in the South African economy. This is a novel result for this study as it confirms that a large central economy has the ability to trigger economic expansions in a peripheral economy. This study further points out the inefficiencies associated with Keynesian style policy making and propagates for a movement towards a more prudent Austrian approach. Keynesian policy making through demand oriented policies have historically been more concerned with “curing” economic instability rather than preventing it. In light of this, the need for economic reform specifically within the manner in which monetary policy is conducted is evident. Aggressive monetary policy in the wake of economic slowdown is no longer effective at creating a sustainable and stable economic environment. A movement away from the monopolization of money and central economic decision making is necessary if the global economy wishes to reach economic permanence.

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Vilela, Lucas Pimentel. "Hypothesis testing in econometric models." reponame:Repositório Institucional do FGV, 2015. http://hdl.handle.net/10438/18249.

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Submitted by Lucas Pimentel Vilela (lucaspimentelvilela@gmail.com) on 2017-05-04T01:19:37Z No. of bitstreams: 1 Hypothesis Testing in Econometric Models - Vilela 2017.pdf: 2079231 bytes, checksum: d0387462f36ab4ab7e5d33163bb68416 (MD5)<br>Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2017-05-15T19:31:43Z (GMT) No. of bitstreams: 1 Hypothesis Testing in Econometric Models - Vilela 2017.pdf: 2079231 bytes, checksum: d0387462f36ab4ab7e5d33163bb68416 (MD5)<br>Made available in DSpace on 2017-05-15T19:32:18Z (GMT). No. of bitstreams: 1 Hypothesis Testing in Econometric Models - Vilela 2017.pdf: 2079231 bytes, checksum: d0387462f36ab4ab7e5d33163bb68416 (MD5) Previous issue date: 2015-12-11<br>This thesis contains three chapters. The first chapter considers tests of the parameter of an endogenous variable in an instrumental variables regression model. The focus is on one-sided conditional t-tests. Theoretical and numerical work shows that the conditional 2SLS and Fuller t-tests perform well even when instruments are weakly correlated with the endogenous variable. When the population F-statistic is as small as two, the power is reasonably close to the power envelopes for similar and non-similar tests which are invariant to rotation transformations of the instruments. This finding is surprising considering the poor performance of two-sided conditional t-tests found in Andrews, Moreira, and Stock (2007). These tests have bad power because the conditional null distributions of t-statistics are asymmetric when instruments are weak. Taking this asymmetry into account, we propose two-sided tests based on t-statistics. These novel tests are approximately unbiased and can perform as well as the conditional likelihood ratio (CLR) test. The second and third chapters are interested in maxmin and minimax regret tests for broader hypothesis testing problems. In the second chapter, we present maxmin and minimax regret tests satisfying more general restrictions than the alpha-level and the power control over all alternative hypothesis constraints. More general restrictions enable us to eliminate trivial known tests and obtain tests with desirable properties, such as unbiasedness, local unbiasedness and similarity. In sequence, we prove that both tests always exist and under suficient assumptions, they are Bayes tests with priors that are solutions of an optimization problem, the dual problem. In the last part of the second chapter, we consider testing problems that are invariant to some group of transformations. Under the invariance of the hypothesis testing, the Hunt-Stein Theorem proves that the search for maxmin and minimax regret tests can be restricted to invariant tests. We prove that the Hunt-Stein Theorem still holds under the general constraints proposed. In the last chapter we develop a numerical method to implement maxmin and minimax regret tests proposed in the second chapter. The parametric space is discretized in order to obtain testing problems with a finite number of restrictions. We prove that, as the discretization turns finer, the maxmin and the minimax regret tests satisfying the finite number of restrictions have the same alternative power of the maxmin and minimax regret tests satisfying the general constraints. Hence, we can numerically implement tests for a finite number of restrictions as an approximation for the tests satisfying the general constraints. The results in the second and third chapters extend and complement the maxmin and minimax regret literature interested in characterizing and implementing both tests.<br>Esta tese contém três capítulos. O primeiro capítulo considera testes de hipóteses para o coeficiente de regressão da variável endógena em um modelo de variáveis instrumentais. O foco é em testes-t condicionais para hipóteses unilaterais. Trabalhos teóricos e numéricos mostram que os testes-t condicionais centrados nos estimadores de 2SLS e Fuller performam bem mesmo quando os instrumentos são fracamente correlacionados com a variável endógena. Quando a estatística F populacional é menor que dois, o poder é razoavelmente próximo do poder envoltório para testes que são invariantes a transformações que rotacionam os instrumentos (similares ou não similares). Este resultado é surpreendente considerando a baixa performance dos testes-t condicionais para hipóteses bilaterais apresentado em Andrews, Moreira, and Stock (2007). Estes testes possuem baixo poder porque as distribuições das estatísticas-t na hipótese nula são assimétricas quando os instrumentos são fracos. Explorando tal assimetria, nós propomos testes para hipóteses bilaterais baseados em estatísticas-t. Estes testes são aproximadamente não viesados e podem performar tão bem quanto o teste de razão de máxima verossimilhança condicional. No segundo e no terceiro capítulos, nosso interesse é em testes do tipo maxmin e minimax regret para testes de hipóteses mais gerais. No segundo capítulo, nós apresentamos testes maxmin e minimax regret que satisfazem restrições mais gerais que as restrições de tamanho e de controle sobre todo o poder na hipótese alternativa. Restrições mais gerais nos possibilitam eliminar testes triviais e obter testes com propriedades desejáveis, como por exemplo não viés, não viés local e similaridade. Na sequência, nós provamos que ambos os testes existem e, sob condições suficientes, eles são testes Bayesianos com priors que são solução de um problema de otimização, o problema dual. Na última parte do segundo capítulo, nós consideramos testes de hipóteses que são invariantes à algum grupo de transformações. Sob invariância, o Teorema de Hunt-Stein implica que a busca por testes maxmin e minimax regret pode ser restrita a testes invariantes. Nós provamos que o Teorema de Hunt-Stein continua válido sob as restrições gerais propostas. No último capítulo, nós desenvolvemos um procedimento numérico para implementar os testes maxmin e minimax regret propostos no segundo capítulo. O espaço paramétrico é discretizado com o objetivo de obter testes de hipóteses com um número finito de pontos. Nós provamos que, ao considerarmos partições mais finas, os testes maxmin e minimax regret que satisfazem um número finito de pontos possuem o mesmo poder na hipótese alternativa que os testes maxmin e minimax regret que satisfazem as restrições gerais. Portanto, nós podemos implementar numericamente os testes que satisfazem um número finito de pontos como aproximação aos testes que satisfazem as restrições gerais.

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Castelli, Francesca <1982&gt. "Econometric models of financial risks." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4274/1/Castelli_Francesca_tesi.pdf.

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The goal of this dissertation is to use statistical tools to analyze specific financial risks that have played dominant roles in the US financial crisis of 2008-2009. The first risk relates to the level of aggregate stress in the financial markets. I estimate the impact of financial stress on economic activity and monetary policy using structural VAR analysis. The second set of risks concerns the US housing market. There are in fact two prominent risks associated with a US mortgage, as borrowers can both prepay or default on a mortgage. I test the existence of unobservable heterogeneity in the borrower's decision to default or prepay on his mortgage by estimating a multinomial logit model with borrower-specific random coefficients.

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Castelli, Francesca <1982&gt. "Econometric models of financial risks." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amsdottorato.unibo.it/4274/.

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The goal of this dissertation is to use statistical tools to analyze specific financial risks that have played dominant roles in the US financial crisis of 2008-2009. The first risk relates to the level of aggregate stress in the financial markets. I estimate the impact of financial stress on economic activity and monetary policy using structural VAR analysis. The second set of risks concerns the US housing market. There are in fact two prominent risks associated with a US mortgage, as borrowers can both prepay or default on a mortgage. I test the existence of unobservable heterogeneity in the borrower's decision to default or prepay on his mortgage by estimating a multinomial logit model with borrower-specific random coefficients.

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Billah, Baki 1965. "Model selection for time series forecasting models." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8840.

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Spurway, Kayleigh Fay Nanette. "A study of the Consumption Capital Asset Pricing Model's appilcability across four countries." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1013016.

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Historically, the Consumption Capital Asset Pricing Method (C-CAPM) has performed poorly in that estimated parameters are implausible, model restrictions are often rejected and inferences appear to be very sensitive to the choice of economic agents' preferences. In this study, we estimate and test the C-CAPM with Constant Relative Risk Aversion (CRRA) using time series data from Germany, South Africa, Britain and America during relatively short time periods with the latest available data sets. Hansen's GMM approach is applied to estimate the parameters arising from this model. In general, estimated parameters fall outside the bounds specified by Lund & Engsted (1996) and Cuthbertson & Nitzsche (2004), even though the models are not rejected by the J-test and are associated with relatively small minimum distances.

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Paraskevopoulos, Ioannis. "Econometric models applied to production theory." Thesis, Queen Mary, University of London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392498.

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McGarry, Joanne S. "Seasonality in continuous time econometric models." Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313064.

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Gualdani, C. "Econometric analysis of network formation models." Thesis, University College London (University of London), 2017. http://discovery.ucl.ac.uk/1566643/.

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This dissertation addresses topics in the econometrics of network formation models. Chapter 1 provides a review of the literature. Statistical models focus on the specification of the probability distribution of the network. Examples include models in which nodes are born sequentially and meet existing vertices according to random meetings and network-based meetings. Within this group of models, special attention is reserved to the milestone work by Jackson and Rogers (2007): after having discussed and replicated the main results of the paper, an extension of the original model is examined and fitted to a dataset of Google Plus users. Even if statistical models can reproduce relatively well the main characteristics of real networks, they usually lack of microfundation, essential for counterfactual analysis. The chapter hence moves to considering the econometrics of economic models of network formation, where agents form links in order to maximise a payoff function. Within this framework, Chapter 2 studies identification of the parameters governing agents’ preferences in a static game of network formation, where links represent asymmetric relations between players. After having shown existence of an equilibrium, partial identification arguments are provided without restrictions on equilibrium selection. The usual computational difficulties are attenuated by restricting the attention to some local games of the network formation game and giving up on sharpness. Chapter 3 applies the methodology developed in Chapter 2 to empirically investigate which preferences are behind firms’ decisions to appoint competitors’ directors as executives. Using data on Italian companies, it is found that a firm i prefers its executives sitting on the board of a rival j when executives of other competitors are hosted too, possibly because it enables i to engage with them in “cheap talk” communications, besides having the opportunity to learn about j’s decision making process.

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Books on the topic "Econometric models"

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Intriligator, Michael D. Econometric models, techniques, andapplications. 2nd ed. Upper Saddle River, N.J: Prentice-Hall International, 1996.

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Nevezhin, Yuriy. Research of econometric models: collection of laboratory works. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1882574.

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The textbook is a collection of laboratory works on the sections "Study of linear econometric models", "Study of nonlinear econometric models", "Time series". Each work includes theoretical and practical parts (tasks with a possible solution). There are also tasks for independent work on each topic being studied. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students studying in the field of Economics, for the purpose of practical study of the sections of the discipline "Econometrics" (bachelors) or "Econometric research" (undergraduates), graduate students of economic specialties and university teachers. It will also be useful for those who independently study issues related to econometrics or research in this field.

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Congress, Econometric Society World. Advances in econometrics. Cambridge [Cambridgeshire]: Cambridge University Press, 1987.

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Congress, Econometric Society World. Advances in econometrics: Fifth World Congress. Cambridge: Cambridge University Press, 1994.

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Congress, Econometric Society World. Advances in econometrics: Fifth World Congress. Cambridge: Cambridge University Press, 1987.

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Gruber, Josef, ed. Econometric Decision Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-51675-7.

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1948-, Fischer Joachim, ed. Macro-econometric models. 2nd ed. Aldershot, Hants, England: Avebury, 1992.

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K, Puttaswamaiah, ed. Econometric models: Techniques and applications. New Delhi: Indus, 1994.

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Pötscher, Benedikt M., and Ingmar R. Prucha. Dynamic Nonlinear Econometric Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03486-6.

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1963-, Franses Philip Hans, and Montgomery A, eds. Econometric models in marketing. Amsterdam: JAI, 2002.

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Book chapters on the topic "Econometric models"

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Dubé, Jean, and Diègo Legros. "Spatial Econometric Models." In Spatial Econometrics Using Microdata, 93–143. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119008651.ch4.

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LeSage, James P., and R. Kelley Pace. "Spatial Econometric Models." In Handbook of Applied Spatial Analysis, 355–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03647-7_18.

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Asteriou, Dimitrios, and Stephen G. Hall. "Dynamic Econometric Models." In Applied Econometrics, 231–42. London: Macmillan Education UK, 2016. http://dx.doi.org/10.1057/978-1-137-41547-9_10.

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Jiao, Xiaoying, and Jason Li Chen. "Spatiotemporal econometric models." In Econometric Modelling and Forecasting of Tourism Demand, 126–43. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003269366-6.

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Zong, Ping. "Dynamic Econometric Models." In The Art and Science of Econometrics, 157–89. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003273905-7.

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Mizen, Paul. "Econometric methods." In Buffer Stock Models and the Demand for Money, 60–77. London: Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-23660-2_4.

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Italianer, Alexander. "Econometric Specification." In Theory and Practice of International Trade Linkage Models, 217–39. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4472-5_6.

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LeSage, James P., and R. Kelley Pace. "Interpreting Spatial Econometric Models." In Handbook of Regional Science, 1–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-642-36203-3_91-1.

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Pace, R. Kelley, and James P. LeSage. "Spatial Econometric Models, Prediction." In Encyclopedia of GIS, 1–7. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_1266-2.

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Pace, R. Kelley, and James P. LeSage. "Spatial Econometric Models, Prediction." In Encyclopedia of GIS, 2011. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_1266.

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Conference papers on the topic "Econometric models"

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Cedolin, Michele, and Mujde Erol Genevois. "Cash Demand Prediction Problem using Econometric and Computational Intelligence Forecasting Models." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), 103–8. IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708473.

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Ivanyuk, Vera, Anatoly Tsvirkun, David Petrosov, Alexander Voronov, Pavel Tereliansky, and Natalia Shchukina. "Econometric Model of the Indicator for Identifying Crisis Situations." In 2024 17th International Conference on Management of Large-Scale System Development (MLSD), 1–4. IEEE, 2024. http://dx.doi.org/10.1109/mlsd61779.2024.10739537.

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Demianchuk, Maryna, Natalia Maslii, and Valerijs Skribans. "GRP Econometric Models for Regions of Ukraine." In the 2019 10th International Conference. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3345035.3345056.

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Lippi, Marco. "Aggregation and dynamics in one-equation econometric models." In 1986 25th IEEE Conference on Decision and Control. IEEE, 1986. http://dx.doi.org/10.1109/cdc.1986.267526.

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Islamov, Bakhtiyor, Munisa Turdibaeva, and Asomiddin Yusupov. "METHODOLOGICAL ISSUES OF ECONOMETRIC ESTIMATING EXPORT GRAVITY MODELS." In ICFNDS '22: The 6th International Conference on Future Networks & Distributed Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3584202.3584244.

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Ivanyuk, Vera. "Econometric Forecasting Models Based on Forecast Combination Methods." In 2018 Eleventh International Conference "Management of large-scale system development" (MLSD 2018). IEEE, 2018. http://dx.doi.org/10.1109/mlsd.2018.8551825.

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Пугачева, Ольга. "Use of econometric models for solvency analysis and estimation of probability of bankruptcy of the enterprise." In International Scientific Conference “30 Years of Economic Reforms in the Republic of Moldova: Economic Progress via Innovation and Competitiveness”. Academy of Economic Studies of Moldova, 2022. http://dx.doi.org/10.53486/9789975155663.01.

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Abstract:

The article is devoted to the analysis of modern approaches to assessing the solvency and probability of bankruptcy of the enterprise using econometric models. An example of building an econometric model based on the national methodology for analyzing the solvency of a business entity is given

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Kovalchuk, Olha, Mykola Shynkaryk, and Mariia Masonkova. "Econometric Models for Estimating the Financial Effect of Cybercrimes." In 2021 11th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2021. http://dx.doi.org/10.1109/acit52158.2021.9548490.

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Sedlak, Otilija, Jelena Birovljev, Zoran Ciric, Jelica Eremic, and Ivana Ciric. "ANALYSIS OF COMPETITIVENESS OF HIGHER EDUCATION WITH ECONOMETRIC MODELS." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.1121.

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Dobrina, Maria V., Yana A. Yurova, and Galina V. Shurshikova. "Econometric Models with Discrete Dependent Variable in Portfolio Analysis." In Proceedings of the 2nd International Conference on Economy, Management and Entrepreneurship (ICOEME 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icoeme-19.2019.18.

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Reports on the topic "Econometric models"

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de Paula, Áureo. Econometric Models of Network Formation. The IFS, January 2020. http://dx.doi.org/10.1920/wp.cem.2020.420.

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Hansen, Lars Peter, John Heaton, and Erzo G. J. Luttmer. Econometric Evaluation of Asset Pricing Models. Cambridge, MA: National Bureau of Economic Research, October 1993. http://dx.doi.org/10.3386/t0145.

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Lo, Andrew, A. Craig MacKinlay, and June Zhang. Econometric Models of Limit-Order Executions. Cambridge, MA: National Bureau of Economic Research, November 1997. http://dx.doi.org/10.3386/w6257.

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Chetverikov, Denis. Testing regression monotonicity in econometric models. Institute for Fiscal Studies, November 2012. http://dx.doi.org/10.1920/wp.cem.2012.3512.

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Chernozhukov, Victor, Christian Hansen, and Alexandre Belloni. Inference for high-dimensional sparse econometric models. Institute for Fiscal Studies, December 2011. http://dx.doi.org/10.1920/wp.cem.2011.4111.

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Ludwig, Jens, Sendhil Mullainathan, and Ashesh Rambachan. Large Language Models: An Applied Econometric Framework. Cambridge, MA: National Bureau of Economic Research, January 2025. https://doi.org/10.3386/w33344.

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Kaczmarek, Tomasz. Input Data for the Model Determined Based on Econometric Models. Publishing House of the University of Agriculture in Krakow, 2024. http://dx.doi.org/10.15576/repourk/2024.1.05.

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Mullahy, John. Multivariate Fractional Regression Estimation of Econometric Share Models. Cambridge, MA: National Bureau of Economic Research, September 2010. http://dx.doi.org/10.3386/w16354.

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Heckman, James, and Christopher Taber. Econometric Mixture Models and More General Models for Unobservables in Duration Analysis. Cambridge, MA: National Bureau of Economic Research, June 1994. http://dx.doi.org/10.3386/t0157.

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Diebold, Francis, and Til Schuermann. Exact Maximum Likelihood Estimation of Observation-Driven Econometric Models. Cambridge, MA: National Bureau of Economic Research, April 1996. http://dx.doi.org/10.3386/t0194.

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