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Articoli di riviste sul tema "Econometric models"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

Cho, Jin Seo, and Halbert White. "DIRECTIONALLY DIFFERENTIABLE ECONOMETRIC MODELS." Econometric Theory 34, no. 5 (August 22, 2017): 1101–31. http://dx.doi.org/10.1017/s0266466617000354.

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The current article examines the limit distribution of the quasi-maximum likelihood estimator obtained from a directionally differentiable quasi-likelihood function and represents its limit distribution as a functional of a Gaussian stochastic process indexed by direction. In this way, the standard analysis that assumes a differentiable quasi-likelihood function is treated as a special case of our analysis. We also examine and redefine the standard quasi-likelihood ratio, Wald, and Lagrange multiplier test statistics so that their null limit behaviors are regular under our model framework.

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13

Ray, W. D. "Statistics and econometric models." International Journal of Forecasting 12, no. 4 (December 1996): 561–62. http://dx.doi.org/10.1016/s0169-2070(97)83048-8.

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14

Labys, Walter C. "4.1. Econometric supply models." Energy 15, no. 7-8 (July 1990): 545–47. http://dx.doi.org/10.1016/0360-5442(90)90003-k.

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15

van Els, P. J. A. "Econometric versus quasiempirical models." Economic Modelling 7, no. 2 (April 1990): 133–47. http://dx.doi.org/10.1016/0264-9993(90)90016-w.

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16

Martins, Luís Oscar Silva, Roberto Antônio Fortuna Carneiro, Fábio Matos Fernandes, Marcelo Santana Silva, Francisco Gaudêncio Mendonça Freires, and Ednildo Andrade Torres. "use of econometric models in studies of Eletricity Generation from biomass." Brazilian Journal of Information Science 14, no. 1 Jan.-Mar (March 27, 2020): 130–72. http://dx.doi.org/10.36311/1981-1640.2020.v14n1.07.p130.

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The present research investigated the utilization of econometric models in studies related to the generation of electricity from biomass, through a bibliometric analysis. The general objective of the study was to analyze the publications, from 1987 to 2018, that explored the potential of econometric models involving biomass for electricity generation. Additionally, it was intended to investigate the most cited articles and authors, seeking to verify the most relevant themes and the main econometric techniques used in the analyses; verify the countries most engaged in these researches, drawing a parallel with their current energy strategies; identify future trends of studies in this area. For this, the SCOPUS database was used, selecting articles in English, from the keywords “Econometrics, biomass and Electricity”. The data collected with the literature review were compiled on thematic maps, with the help of the Vosviewer software, which realized analysis of citation, co-citation, co-authorship and keywords. SciMat software was also used, which generated from key terms, longitudinal strategic maps that allowed identifying future trends on the theme discussed in this article. The results indicated that the main research fronts in this field, are related to the use of econometrics to estimate the impacts of energy generation from biomass in variables such as economic growth, energy demand and greenhouse gas emissions.

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17

Sun, Yiguo, Zongwu Cai, and Qi Li. "SEMIPARAMETRIC FUNCTIONAL COEFFICIENT MODELS WITH INTEGRATED COVARIATES." Econometric Theory 29, no. 3 (January 8, 2013): 659–72. http://dx.doi.org/10.1017/s0266466612000710.

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AbstractCai, Li, and Park (Journal of Econometrics, 2009) and Xiao (Journal of Econometrics, 2009) developed asymptotic theories for estimators of semiparametric varying coefficient models when regressors are integrated processes but the smooth coefficients are functionals of stationary processes. Using a recent result from Phillips (Econometric Theory, 2009), we extend this line of research by allowing for both the regressors and the covariates entering the smooth functionals to be integrated variables. We derive the asymptotic distribution for the proposed semiparametric estimator. An empirical application is presented to examine the purchasing power parity hypothesis between U.S. and Canadian dollars.

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18

Spirtes, Peter. "Graphical models, causal inference, and econometric models." Journal of Economic Methodology 12, no. 1 (March 2005): 3–34. http://dx.doi.org/10.1080/1350178042000330887.

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19

Rodríguez-Póo, Juan M., Stefan Sperlich, and Philippe Vieu. "SPECIFICATION TESTING WHEN THE NULL IS NONPARAMETRIC OR SEMIPARAMETRIC." Econometric Theory 31, no. 6 (September 18, 2014): 1281–309. http://dx.doi.org/10.1017/s0266466614000504.

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This paper discusses the problem of testing misspecifications in semiparametric regression models for a large family of econometric models under rather general conditions. We focus on two main issues that typically arise in econometrics. First, many econometric models are estimated through maximum likelihood or pseudo-ML methods like, for example, limited dependent variable or gravity models. Second, often one might not want to fully specify the null hypothesis. Instead, one would rather impose some structure like separability or monotonicity. In order to address these points we introduce an adaptive omnibus test. Special emphasis is given to practical issues like adaptive bandwidth choice, general but simple requirements on the estimates, and finite sample performance, including the resampling approximations.

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20

Chen, Wenhui. "Discussion on Collaborative Teaching of Econometrics in the Field of Environmental Economics." Journal of Contemporary Educational Research 7, no. 11 (November 23, 2023): 154–59. http://dx.doi.org/10.26689/jcer.v7i11.5590.

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To capitalize on the synergies between the Econometrics course and the Environmental Economics major, this paper aims to enhance students’ability to conduct empirical analysis and practical application using econometric models. It also seeks to promote collaborative teaching through case studies and model research. The primary focus is on the hot research issues within the field of environmental economics, utilizing the econometric model as a vehicle for instruction. To achieve this, the paper proposes the development of a comprehensive case library specific to environmental economics. This resource will serve to optimize the case teaching approach, incorporating the use of econometric software, and fostering interactive teaching models between educators and students. By implementing these strategies, the paper outlines a path and mode for collaborative teaching that effectively bridges the gap between conometrics and environmental economics.

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21

Mellado, V., and F. Giménez. "ECONOMETRIC MODELS FOR PALM APPRAISAL." Acta Horticulturae, no. 486 (March 1999): 241–46. http://dx.doi.org/10.17660/actahortic.1999.486.36.

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22

Farebrother, R. W., Edwin Kuh, John W. Neese, and Peter Hollinger. "Structural Sensitivity in Econometric Models." Statistician 35, no. 3 (1986): 404. http://dx.doi.org/10.2307/2987771.

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23

Asafu-Adjaye, John, Edwin Kuh, John W. Neese, and Peter Hollinger. "Structural Sensitivity in Econometric Models." Journal of the Operational Research Society 37, no. 4 (April 1986): 440. http://dx.doi.org/10.2307/2582577.

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24

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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25

Pánková, Václava. "Econometric Models with Panel Data." Acta Oeconomica Pragensia 15, no. 1 (February 1, 2007): 79–85. http://dx.doi.org/10.18267/j.aop.41.

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26

Pagan, Adrian, E. Kuh, J. W. Neese, and P. Hollinger. "Structural Sensitivity in Econometric Models." Journal of Business & Economic Statistics 5, no. 4 (October 1987): 549. http://dx.doi.org/10.2307/1392007.

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27

Asafu-Adjaye, John. "Structural Sensitivity in Econometric Models." Journal of the Operational Research Society 37, no. 4 (April 1986): 440. http://dx.doi.org/10.1057/jors.1986.77.

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28

Milani, Fabio, and Dale J. Poirier. "Econometric Issues in DSGE Models." Econometric Reviews 26, no. 2-4 (April 12, 2007): 201–4. http://dx.doi.org/10.1080/07474930701220204.

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29

Henry, S. G. B. "Structural sensitivity in econometric models." International Journal of Forecasting 3, no. 2 (January 1987): 343–44. http://dx.doi.org/10.1016/0169-2070(87)90022-7.

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30

Hallam, David. "Econometric models and agricultural policy." Agricultural Administration and Extension 25, no. 1 (January 1987): 49–62. http://dx.doi.org/10.1016/0269-7475(87)90057-2.

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31

Green, R. Jeffery, Edwin Kuh, John W. Neese, and Peter Hollinger. "Structural Sensitivity in Econometric Models." Journal of the American Statistical Association 81, no. 396 (December 1986): 1124. http://dx.doi.org/10.2307/2289106.

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32

Wittink, Dick R. "Econometric Models for Marketing Decisions." Journal of Marketing Research 42, no. 1 (February 2005): 1–3. http://dx.doi.org/10.1509/jmkr.42.1.1.56893.

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33

Cepeda-Cuervo, Edilberto, B. Piedad Urdinola, and Diana Rodríguez. "Double Generalized Spatial Econometric Models." Communications in Statistics - Simulation and Computation 41, no. 5 (May 2012): 671–85. http://dx.doi.org/10.1080/03610918.2011.600500.

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34

Lin, Kuan-Pin, and Arthur M. Farley. "Causal reasoning in econometric models." Decision Support Systems 15, no. 2 (October 1995): 167–77. http://dx.doi.org/10.1016/0167-9236(94)00035-q.

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35

Coloma, Germán. "Econometric estimation of PCAIDS models." Empirical Economics 31, no. 3 (February 9, 2006): 587–99. http://dx.doi.org/10.1007/s00181-005-0033-6.

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36

Damiani, Mirella, and Lorenzo Panattoni. "Optimal simulation with econometric models." Journal of Economic Dynamics and Control 16, no. 1 (January 1992): 93–108. http://dx.doi.org/10.1016/0165-1889(92)90007-2.

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37

Lee, Duu-Hwa, Duu-Jong Lee, and Ayfer Veziroglu. "Econometric models for biohydrogen development." Bioresource Technology 102, no. 18 (September 2011): 8475–83. http://dx.doi.org/10.1016/j.biortech.2011.04.016.

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TRIVEDI, PRAVIN K. "ECONOMETRIC MODELS OF EVENT COUNTS." Journal of Applied Econometrics 12, no. 3 (May 1997): 199–201. http://dx.doi.org/10.1002/(sici)1099-1255(199705)12:3<199::aid-jae442>3.0.co;2-r.

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Osipov, Vladimir S., Aleksandr P. Tsypin, and Olga V. Ledneva. "Using econometric models to forecast fixed asset investments." Journal Of Applied Informatics 18, no. 1 (February 10, 2023): 111–28. http://dx.doi.org/10.37791/2687-0649-2023-18-1-111-128.

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Abstract (sommario):

One of the key factors in the country’s GDP growth is reproducible capital, which lays the foundation for the production of products, works and services. Accordingly, the study of the state, structure and dynamics of the dominant component, fixed assets, is one of the priority tasks of statistics and econometrics. This implies the purpose of the study, which is to assess the predictive capabilities of econometric models. To achieve this goal, a pool of mathematical-statistical and econometric methods was used, in particular tabular and graphic, descriptive statistics, correlation-regression, adaptive modeling. The main results include: analysis of the structure of investments did not find new or hidden patterns, so investments are directed to the modernization or renewal of capital-intensive areas – these are buildings, structures and land (about 40% of the total investment), the main industries are industry and transport; visual analysis of the dynamics of the temporary series of investments in fixed assets showed the presence of a long-term, seasonal and situational component; the construction of 6 econometric models reflecting the complex dynamics of the macro indicator in question made it possible to distinguish two adaptive models belonging to the group; thus, the best forecast opportunities for complex dynamics of investments in Russian fixed assets are observed in the three-parameter exponential smoothing model and SARIMA (1,0,0)(1,1,0) [4]. The results obtained in the course of the study will be useful for scientists involved in modeling and predicting complex-structured time series

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Kim, Dong-sup, and Seungwoo Shin. "THE ECONOMIC EXPLAINABILITY OF MACHINE LEARNING AND STANDARD ECONOMETRIC MODELS-AN APPLICATION TO THE U.S. MORTGAGE DEFAULT RISK." International Journal of Strategic Property Management 25, no. 5 (July 13, 2021): 396–412. http://dx.doi.org/10.3846/ijspm.2021.15129.

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This study aims to bridge the gap between two perspectives of explainability−machine learning and engineering, and economics and standard econometrics−by applying three marginal measurements. The existing real estate literature has primarily used econometric models to analyze the factors that affect the default risk of mortgage loans. However, in this study, we estimate a default risk model using a machine learning-based approach with the help of a U.S. securitized mortgage loan database. Moreover, we compare the economic explainability of the models by calculating the marginal effect and marginal importance of individual risk factors using both econometric and machine learning approaches. Machine learning-based models are quite effective in terms of predictive power; however, the general perception is that they do not efficiently explain the causal relationships within them. This study utilizes the concepts of marginal effects and marginal importance to compare the explanatory power of individual input variables in various models. This can simultaneously help improve the explainability of machine learning techniques and enhance the performance of standard econometric methods.

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Peng, Jiangyan, and Qiying Wang. "WEAK CONVERGENCE TO STOCHASTIC INTEGRALS UNDER PRIMITIVE CONDITIONS IN NONLINEAR ECONOMETRIC MODELS." Econometric Theory 34, no. 5 (October 26, 2017): 1132–57. http://dx.doi.org/10.1017/s0266466617000408.

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Limit theory with stochastic integrals plays a major role in time series econometrics. In earlier contributions on weak convergence to stochastic integrals, the literature commonly uses martingale and semi-martingale structures. Liang, Phillips, Wang, and Wang (2016) (see also Wang (2015), Chap. 4.5) currently extended weak convergence to stochastic integrals by allowing for a linear process or a α-mixing sequence in innovations. While these martingale, linear process and α-mixing structures have wide relevance, they are not sufficiently general to cover many econometric applications that have endogeneity and nonlinearity. This paper provides new conditions for weak convergence to stochastic integrals. Our frameworks allow for long memory processes, causal processes, and near-epoch dependence in innovations, which have applications in a wide range of econometric areas such as TAR, bilinear, and other nonlinear models.

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Hoover, Kevin D. "On the Reception of Haavelmo’s Econometric Thought." Journal of the History of Economic Thought 36, no. 1 (March 2014): 45–65. http://dx.doi.org/10.1017/s1053837214000029.

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The significance of Haavelmo’s “The Probability Approach in Econometrics” (1944), the foundational document of modern econometrics, has been interpreted in widely different ways. Some regard it as a blueprint for a provocative (but ultimately unsuccessful) program dominated by the need for a priori theoretical identification of econometric models. Others focus more on statistical adequacy than on theoretical identification. They see its deepest insights as unduly neglected. The present article uses bibliometric techniques and a close reading of econometrics articles and textbooks to trace the way in which the economics profession received, interpreted, and transmitted Haavelmo’s ideas. A key irony is that the first group calls for a reform of econometric thinking that goes several steps beyond Haavelmo’s initial vision; the second group argues that essentially what the first group advocates was already in Haavelmo’s “Probability Approach” from the beginning.

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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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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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Du, Kerui, Yonghui Zhang, and Qiankun Zhou. "Fitting partially linear functional-coefficient panel-data models with Stata." Stata Journal: Promoting communications on statistics and Stata 20, no. 4 (December 2020): 976–98. http://dx.doi.org/10.1177/1536867x20976339.

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In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah ( Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators.

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Sun, Liyang. "Implementing Valid Two-Step Identification-Robust Confidence Sets for Linear Instrumental-Variables Models." Stata Journal: Promoting communications on statistics and Stata 18, no. 4 (December 2018): 803–25. http://dx.doi.org/10.1177/1536867x1801800404.

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In this article, we consider inference in the linear instrumental-variables models with one or more endogenous variables and potentially weak instruments. I developed a command, twostepweakiv, to implement the two-step identification-robust confidence sets proposed by Andrews (2018, Review of Economics and Statistics 100: 337–348) based on Wald tests and linear combination tests (Andrews, 2016, Econometrica 84: 2155–2182). Unlike popular procedures based on first-stage F statistics (Stock and Yogo, 2005, Testing for weak instruments in linear IV regression, in Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg), the two-step identification-robust confidence sets control coverage distortion without assuming the data are homoskedastic. I demonstrate the use of twostepweakiv with an example of analyzing the effect of wages on married female labor supply. For inference on subsets of parameters, twostepweakiv also implements the refined projection method (Chaudhuri and Zivot, 2011, Journal of Econometrics 164: 239–251). I illustrate that this method is more powerful than the conventional projection method using Monte Carlo simulations.

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Yu, Jun. "ECONOMETRIC ANALYSIS OF CONTINUOUS TIME MODELS: A SURVEY OF PETER PHILLIPS’S WORK AND SOME NEW RESULTS." Econometric Theory 30, no. 4 (April 1, 2014): 737–74. http://dx.doi.org/10.1017/s0266466613000467.

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Econometric analysis of continuous time models has drawn the attention of Peter Phillips for 40 years, resulting in many important publications by him. In these publications he has dealt with a wide range of continuous time models and the associated econometric problems. He has investigated problems from univariate equations to systems of equations, from asymptotic theory to finite sample issues, from parametric models to nonparametric models, from identification problems to estimation and inference problems, and from stationary models to nonstationary and nearly nonstationary models. This paper provides an overview of Peter Phillips’ contributions in the continuous time econometrics literature. We review the problems that have been tackled by him, outline the main techniques suggested by him, and discuss the main results obtained by him. Based on his early work, we compare the performance of three asymptotic distributions in a simple setup. Results indicate that thein-fillasymptotics significantly outperforms thelong-spanasymptotics and thedoubleasymptotics.

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Ilyos, Abdullaev. "Econometric Models Of Attracting Investment Into Production Industry." International Journal of Management Sciences and Business Research 5, no. 9 (September 30, 2016): 47–50. https://doi.org/10.5281/zenodo.3464897.

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In this article, is analysed relationship between GDP and investments and proven on the basis of econometric models. Model of GDP determination is built on the basis of Solow model with the help of Cobb-Douglas production function. Econometric models for calculating the effectiveness of attracting more investment to production in Uzbek economy are analyzed using dynamic econometric models constructed using Solow model.

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Meena, A., B. Manjunatha, A. Anil Kumar, Jhade Sunil, S. Vishnu Shankar, M. Manjubala, Abinayarajam D, and Himanshu Sekhar Behera. "Spatial Econometrics in Agriculture: Modelling Spatial Dependencies in Data." Archives of Current Research International 24, no. 6 (August 7, 2024): 488–506. http://dx.doi.org/10.9734/acri/2024/v24i6807.

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Spatial econometrics in agriculture focuses on modelling spatial dependencies in data, recognizing that agricultural outcomes are often influenced by geographic proximity and spatial interactions. This approach acknowledges that agricultural phenomena, such as crop yields, pest outbreaks, and soil quality, can exhibit spatial patterns that traditional econometric models may overlook. By incorporating spatial elements into econometric analysis, researchers can better understand how neighbouring regions or locations influence each other's agricultural outcomes. This is crucial for policymakers and farmers seeking to optimize resource allocation, manage environmental impacts, and enhance productivity in agriculture. Spatial econometrics provides a robust framework to uncover hidden relationships and spatial interactions within agricultural data, thereby supporting informed decision-making and sustainable agricultural practices in a spatially interconnected world.

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Ford, J. L., Paul De Grauwe, and Theo Peeters. "Exchange Rates in Multicountry Econometric Models." Economic Journal 95, no. 378 (June 1985): 518. http://dx.doi.org/10.2307/2233243.

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