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Journal articles on the topic 'Econometrics – Statistical methods'

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Published: 10 March 2023

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1

Bera, Anil K., and Ramu Ramanathan. "Statistical Methods in Econometrics." Journal of the American Statistical Association 89, no. 427 (September 1994): 1144. http://dx.doi.org/10.2307/2290954.

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2

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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3

Andersen, Torben G. "SIMULATION-BASED ECONOMETRIC METHODS." Econometric Theory 16, no. 1 (February 2000): 131–38. http://dx.doi.org/10.1017/s0266466600001080.

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The accessibility of high-performance computing power has always influenced theoretical and applied econometrics. Gouriéroux and Monfort begin their recent offering, Simulation-Based Econometric Methods, with a stylized three-stage classification of the history of statistical econometrics. In the first stage, lasting through the 1960's, models and estimation methods were designed to produce closed-form expressions for the estimators. This spurred thorough investigation of the standard linear model, linear simultaneous equations with the associated instrumental variable techniques, and maximum likelihood estimation within the exponential family. During the 1970's and 1980's the development of powerful numerical optimization routines led to the exploration of procedures without closed-form solutions for the estimators. During this period the general theory of nonlinear statistical inference was developed, and nonlinear micro models such as limited dependent variable models and nonlinear time series models, e.g., ARCH, were explored. The associated estimation principles included maximum likelihood (beyond the exponential family), pseudo-maximum likelihood, nonlinear least squares, and generalized method of moments. Finally, the third stage considers problems without a tractable analytic criterion function. Such problems almost invariably arise from the need to evaluate high-dimensional integrals. The idea is to circumvent the associated numerical problems by a simulation-based approach. The main requirement is therefore that the model may be simulated given the parameters and the exogenous variables. The approach delivers simulated counterparts to standard estimation procedures and has inspired the development of entirely new procedures based on the principle of indirect inference.
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4

Cullinane, Kevin. "Statistical and Econometric Methods for Transportation Data Analysis." Maritime Economics & Logistics 6, no. 2 (June 2004): 187–89. http://dx.doi.org/10.1057/palgrave.mel.9100102.

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5

Bivand, Roger, Giovanni Millo, and Gianfranco Piras. "A Review of Software for Spatial Econometrics in R." Mathematics 9, no. 11 (June 2, 2021): 1276. http://dx.doi.org/10.3390/math9111276.

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The software for spatial econometrics available in the R system for statistical computing is reviewed. The methods are illustrated in a historical perspective, highlighting the main lines of development and employing historically relevant datasets in the examples. Estimators and tests for spatial cross-sectional and panel models based either on maximum likelihood or on generalized moments methods are presented. The paper is concluded reviewing some current active lines of research in spatial econometric software methods.
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6

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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7

Sirisrisakulchai, Jirakom, Chon Van Le, and Uyen Pham. "On Statistics of Random Sets for Partial Identification of Econometric Structures." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp01 (August 28, 2020): 87–98. http://dx.doi.org/10.1142/s0218488520400085.

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In this paper, we emphasize and elaborate on two important and relatively new aspects in uncertainty analysis in order to increase the credibility of empirical results in statistics in general, and in econometrics in particular, namely, the problem of partial identification, and the use of random set statistics. We elaborate on the current interests in partially identified models, exemplified by econometric structures involving copulas. We spell out the rationale and the statistical methods based upon random set theory for analyzing partial identification problem towards credible econometrics.
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8

Stengos, Thanasis. "Nonparametric Econometric Methods and Applications." Journal of Risk and Financial Management 12, no. 4 (November 30, 2019): 180. http://dx.doi.org/10.3390/jrfm12040180.

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An area of very active research in econometrics over the last 30 years has been that of non- and semi-parametric methods. These methods have provided ways to complement more-traditional parametric approaches in terms of robust alternatives, as well as preliminary data analysis. The present Special Issue collects a number of new contributions, both theoretical and empirical that cover a wide spectrum of areas such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth as well as statistical theory and methodology.
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9

Neuburger, Hugh, and Houston H. Stokes. "Testing the Appropriateness of Statistical Methods." Financial Analysts Journal 47, no. 4 (July 1991): 83–88. http://dx.doi.org/10.2469/faj.v47.n4.83.

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10

Snell, Joyce, and P. Sprent. "Applied Nonparametric Statistical Methods." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 2 (1995): 355. http://dx.doi.org/10.2307/2983315.

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11

Stoimenova, Eugenia. "Applied Nonparametric Statistical Methods." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 1 (January 2010): 276. http://dx.doi.org/10.1111/j.1467-985x.2009.00624_13.x.

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12

Knott, Martin, and J. S. Maritz. "Distribution-Free Statistical Methods." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 2 (1996): 351. http://dx.doi.org/10.2307/2983190.

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13

Teräsvirta, Timo. "Mathematical and Quantitative Methods: Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series." Journal of Economic Literature 51, no. 4 (December 1, 2013): 1190–92. http://dx.doi.org/10.1257/jel.51.4.1183.r4.

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Timo Terasvirta of Aarhus University reviews, “Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series” by Andrew C. Harvey. The Econlit abstract of this book begins: “Presents a theory for a class of nonlinear time series models that can deal with dynamic distributions, with an emphasis on models in which the conditional distribution of an observation may be heavy-tailed and the location and/or scale changes over time. Discusses statistical distributions and asymptotic theory; location; scale; location/scale models for nonnegative variables; dynamic kernel density estimation and time-varying quantiles; multivariate models, correlation, and association; and further directions in dynamic models. Harvey is Professor of Econometrics at the University of Cambridge and Fellow of Corpus Christi College, the Econometric Society, and the British Academy.”
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14

Grieve, A. P., P. C. Meier, and R. E. Zund. "Statistical Methods in Analytical Chemistry." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 2 (1994): 311. http://dx.doi.org/10.2307/2983374.

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15

Klemm, Rebecca, D. H. Kaye, and Mikel Aickin. "Statistical Methods in Discrimination Litigation." Journal of Business & Economic Statistics 5, no. 4 (October 1987): 549. http://dx.doi.org/10.2307/1392006.

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16

Cannings, Chris. "Statistical Methods in Molecular Evolution." Journal of the Royal Statistical Society: Series A (Statistics in Society) 169, no. 2 (March 2006): 391. http://dx.doi.org/10.1111/j.1467-985x.2006.00414_11.x.

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17

Dolores Ugarte, M. "Statistical Methods for Disease Clustering." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 3 (July 2011): 848–49. http://dx.doi.org/10.1111/j.1467-985x.2011.00709_12.x.

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18

Wickham, Carol A. C., P. Armitage, and G. Berry. "Statistical Methods in Medical Research." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 361. http://dx.doi.org/10.2307/2982765.

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19

Orlov, Alexander. "Organizational and economic modeling in the organization of production in the epoch of digital economy." MATEC Web of Conferences 311 (2020): 02001. http://dx.doi.org/10.1051/matecconf/202031102001.

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Statistical methods of production quality management are an integral part of the theory and practice of production organization. It is told about the history of creation and the results of the Center for Statistical Methods and Informatics (currently - Institute of High Statistical Technologies and Econometrics of BMSTU).
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20

Novák, J., H. Sůvová, and J. Vondráček. "Multivariate statistical methods as a tool of financial analysis of farm businesses." Agricultural Economics (Zemědělská ekonomika) 48, No. 1 (February 29, 2012): 9–12. http://dx.doi.org/10.17221/5281-agricecon.

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The paper is focused on the evaluation of the possibilities of analysing the relations between economic and financial indicators of farm businesses by the application of multivariate statistical methods. It also indicates the possibilities of the construction of a general economic indicator of business effectiveness.
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21

Slutskin, L. N. "Graphical Statistical Methods for Studying Causal Effects. Bayesian Networks." Journal of the New Economic Association 36, no. 4 (2017): 12–30. http://dx.doi.org/10.31737/2221-2264-2017-36-4-1.

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22

Dolores Ugarte, M. "Statistical Methods for Spatio-temporal Systems." Journal of the Royal Statistical Society: Series A (Statistics in Society) 170, no. 4 (October 2007): 1182. http://dx.doi.org/10.1111/j.1467-985x.2007.00506_9.x.

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23

Lark, R. M. "Statistical Methods for Estimating Petroleum Resources." Journal of the Royal Statistical Society: Series A (Statistics in Society) 174, no. 2 (March 14, 2011): 513. http://dx.doi.org/10.1111/j.1467-985x.2010.00681_12.x.

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24

Thompson, J. W., W. H. McNeese, and R. A. Klein. "Statistical Methods for the Process Industries." Journal of the Royal Statistical Society. Series A (Statistics in Society) 156, no. 2 (1993): 328. http://dx.doi.org/10.2307/2982751.

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25

Mackenzie, I. G., and C. P. Cox. "A Handbook of Introductory Statistical Methods." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 2 (1988): 365. http://dx.doi.org/10.2307/2982770.

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26

Davies, Neville, and D. Bissell. "Statistical Methods for SPC and TQM." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 2 (1995): 341. http://dx.doi.org/10.2307/2983299.

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27

Koop, Gary. "A Review of A First Course in Bayesian Statistical Methods." Econometrics Journal 13, no. 1 (February 1, 2010): B1—B5. http://dx.doi.org/10.1111/j.1368-423x.2009.00306.x.

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28

Ma, Yue, and Ying Chu Ng. "Bootstrapping statistical inferences of decomposition methods for gender earnings differentials." Applied Economics 40, no. 12 (June 2008): 1583–93. http://dx.doi.org/10.1080/00036840600843970.

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29

Soshnikava, L. A., and A. V. Kishkovich. "Assessment of the Regional Labor Market, Taking into Account the Territorial Effect (Case Study: The Republic of Belarus)." Voprosy statistiki 29, no. 2 (May 1, 2022): 23–32. http://dx.doi.org/10.34023/2313-6383-2022-29-2-23-32.

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The relevance of the study concerning evaluation problems and a specific comparative analysis of regional labor markets is explained by the growing importance of improving macroeconomic regulation in almost all CIS countries. The purpose of the work was to demonstrate the capabilities of spatial econometrics tools in the analysis of regional labor markets on the example of the Republic of Belarus. The authors consider methodological approaches to analyzing labor market indicators based on modern statistical and econometric tools.The authors substantiated the necessity of using spatial econometrics methods for a more accurate assessment of the specific characteristics of the regions of the Republic of Belarus. A spatial autoregressive model was built using panel data. Here, integral block indicators were used as factors, covering only 40 primary characteristics of the region. This article briefly discusses the provisions used in spatial data analysis. It also presents the results of building a mixed model of spatial autoregression. For calculations, the authors used data for 2016–2019, which are freely available in the interactive business intelligence system for distribution of official statistical information of the National Statistical Committee of the Republic of Belarus (Belstat, 2021).As predictors, the regression model included integral indicators of the regional labor market, weighted using a matrix of distances between the centers of regions. Here, were used forty initial indicators. According to the authors, the results of the study can have practical application when planning programs for the development of regional labor markets.
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30

Chatterjee, Samprit, N. Schofield, M. Chatterjee, S. Satchell, and P. Whitely. "Advanced Statistical Methods in the Social Sciences." Journal of Business & Economic Statistics 5, no. 1 (January 1987): 159. http://dx.doi.org/10.2307/1391227.

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31

Ravi, Sreenivasan. "Statistical and Probabilistic Methods in Actuarial Science." Journal of the Royal Statistical Society: Series A (Statistics in Society) 172, no. 2 (April 2009): 530. http://dx.doi.org/10.1111/j.1467-985x.2009.00588_2.x.

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32

Cheshire, James. "A First Course in Bayesian Statistical Methods." Journal of the Royal Statistical Society: Series A (Statistics in Society) 173, no. 3 (May 14, 2010): 694–95. http://dx.doi.org/10.1111/j.1467-985x.2010.00646_7.x.

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33

Osmond, Clive, S. H. Moolgavkar, and R. L. Prentice. "Modern Statistical Methods in Chronic Disease Epidemiology." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 1 (1988): 234. http://dx.doi.org/10.2307/2982210.

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34

Eling, Martin, and Nicola Loperfido. "New mathematical and statistical methods for actuarial science and finance." European Journal of Finance 26, no. 2-3 (January 9, 2020): 96–99. http://dx.doi.org/10.1080/1351847x.2019.1707251.

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35

Walden, Andrew, J. L. Stanford, and S. B. Vardeman. "Statistical Methods for Physical Science: Vol. 28, Methods of Experimental Physics." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 193. http://dx.doi.org/10.2307/2983496.

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36

Kovacs, Peter, Eva Kuruczleki, Klara Kazar, Lilla Liptak, and Tamas Racz. "Modern teaching methods in action in statistical classes." Statistical Journal of the IAOS 37, no. 3 (September 1, 2021): 899–919. http://dx.doi.org/10.3233/sji-210843.

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To act as a responsible member of a democratic society, everybody needs statistical literacy and practical knowledge on how to use statistical data, visualization, and methods. In the case of professions that use or produce statistical data the correct use of statistics and statistical thinking are also important. Practice and knowledge applicable in real life are also needed. To reach these requirements, using real-life problems, modern technologies (digital solutions, online tools) and up-to-date teaching methods tailored to the target audiences is crucial. Several papers show that the use of real problems, technology and modern teaching methods are more efficient than the traditional frontal teaching method. In this study, we describe some new teaching methods, for instance problem-based learning, project-based learning, thinking-based learning, flipped classroom, gamification, new technological devices. We also discuss the combination of different methods and modern technology in action in the field of Statistics. The paper shares our developments, experiences, and lessons we learnt from classes. One of our main results is the idea that the use of modern teaching approaches leads to more practical and applicable knowledge; however, their success also depends on both the educators’ and the students’ time expenditure and attitude.
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37

White, Halbert. "Learning in Artificial Neural Networks: A Statistical Perspective." Neural Computation 1, no. 4 (December 1989): 425–64. http://dx.doi.org/10.1162/neco.1989.1.4.425.

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The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.
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38

Bergstrom, A. R. "The History of Continuous-Time Econometric Models." Econometric Theory 4, no. 3 (December 1988): 365–83. http://dx.doi.org/10.1017/s0266466600013359.

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Although it is only during the last decade that continuous-time models have been extensively used in applied econometric work, the development of statistical methods applicable to such models commenced over 40 years ago. The first significant contribution to the problem of estimating the parameters of continuous-time stochastic models from discrete data was made by the British statistician Bartlett [1946] only three years after the pioneering contribution of Haavelmo [1943] on simultaneous equations models. Moreover, by this time the fundamental mathematical theory of continuous-time stochastic models was already well developed, major contributions having been made by some of the leading mathematicians of the twentieth century, including Einstein, Weiner, and Kolmogorov.
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39

Karachun, Irina, Lyubov Vinnichek, and Andrey Tuskov. "Machine learning methods in finance." SHS Web of Conferences 110 (2021): 05012. http://dx.doi.org/10.1051/shsconf/202111005012.

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This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.
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40

Filipiak, Dominik, and Agata Filipowska. "Towards Data Oriented Analysis of the Art Market: Survey and Outlook." e-Finanse 12, no. 1 (March 1, 2016): 21–31. http://dx.doi.org/10.1515/fiqf-2016-0133.

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AbstractDue to the constantly growing interest in alternative investments, the art market has become the subject of numerous studies. By publishing sales data, many services and auction houses provide a foundation for further research on the latest trends. Determining the definition of the artistic value or formalisation of appraisal may be considered quite complex. Statistical analysis, econometric methods or data mining techniques could pave the way towards better understanding of the mechanisms occurring on the art market. The goal of this paper is to identify, describe and compare solutions (and related challenges) that help to analyse, make decisions and define state of the art in the context of the intersection of econometrics on art markets and computer science. This work is also a starting point for further research.
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41

Elton, R. A., and R. F. Woolson. "Statistical Methods for the Analysis of Biomedical Data." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 3 (1988): 572. http://dx.doi.org/10.2307/2983034.

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42

Vallois, Nicolas, and Dorian Jullien. "A history of statistical methods in experimental economics." European Journal of the History of Economic Thought 25, no. 6 (November 2, 2018): 1455–92. http://dx.doi.org/10.1080/09672567.2018.1523445.

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43

Fameliti, Stavroula P., and Vasiliki D. Skintzi. "Statistical and economic performance of combination methods for forecasting crude oil price volatility." Applied Economics 54, no. 26 (February 17, 2022): 3031–54. http://dx.doi.org/10.1080/00036846.2021.2001425.

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44

Varian, Hal R. "Big Data: New Tricks for Econometrics." Journal of Economic Perspectives 28, no. 2 (May 1, 2014): 3–28. http://dx.doi.org/10.1257/jep.28.2.3.

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Computers are now involved in many economic transactions and can capture data associated with these transactions, which can then be manipulated and analyzed. Conventional statistical and econometric techniques such as regression often work well, but there are issues unique to big datasets that may require different tools. First, the sheer size of the data involved may require more powerful data manipulation tools. Second, we may have more potential predictors than appropriate for estimation, so we need to do some kind of variable selection. Third, large datasets may allow for more flexible relationships than simple linear models. Machine learning techniques such as decision trees, support vector machines, neural nets, deep learning, and so on may allow for more effective ways to model complex relationships. In this essay, I will describe a few of these tools for manipulating and analyzing big data. I believe that these methods have a lot to offer and should be more widely known and used by economists.
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45

Suvorov, N. V., E. E. Balashova, O. B. Davidkova, and G. V. Zenkova. "Econometric methods for investigating dynamics indicators of the resource intensity in the domestic economy (tools and statistical results)." Studies on Russian Economic Development 24, no. 5 (September 2013): 409–21. http://dx.doi.org/10.1134/s1075700713050122.

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46

Engel, Jan, and Henriette (Jettie) C. M. Hoonhout. "Statistics development: statistical methods meeting the user’s needs." AStA Advances in Statistical Analysis 91, no. 4 (October 31, 2007): 413–27. http://dx.doi.org/10.1007/s10182-007-0046-x.

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47

Ludkovski, Michael, and Aditya Maheshwari. "Simulation methods for stochastic storage problems: a statistical learning perspective." Energy Systems 11, no. 2 (January 3, 2019): 377–415. http://dx.doi.org/10.1007/s12667-018-0318-4.

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48

Bajari, Patrick, Denis Nekipelov, Stephen P. Ryan, and Miaoyu Yang. "Machine Learning Methods for Demand Estimation." American Economic Review 105, no. 5 (May 1, 2015): 481–85. http://dx.doi.org/10.1257/aer.p20151021.

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We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.
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49

Worton, B. J., and J. S. U. Hjorth. "Computer Intensive Statistical Methods: Validation Model Selection and Bootstrap." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 3 (1994): 504. http://dx.doi.org/10.2307/2983538.

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50

Spiegelhalter, David, Christopher Sherlaw-Johnson, Martin Bardsley, Ian Blunt, Christopher Wood, and Olivia Grigg. "Statistical methods for healthcare regulation: rating, screening and surveillance." Journal of the Royal Statistical Society: Series A (Statistics in Society) 175, no. 1 (November 21, 2011): 1–47. http://dx.doi.org/10.1111/j.1467-985x.2011.01010.x.

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