Journal articles: 'Applied Econometric'

1

Favero, Carlo A., Keith Cuthbertson, Stephen G. Hall, and Mark P. Taylor. "Applied Econometric Techniques." Economic Journal 102, no. 415 (November 1992): 1572. http://dx.doi.org/10.2307/2234832.

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MW and Walter Enders. "Applied Econometric Time Series." Journal of the American Statistical Association 90, no. 431 (September 1995): 1135. http://dx.doi.org/10.2307/2291367.

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Ziegel, Eric R. "Applied Econometric Time Series." Technometrics 37, no. 4 (November 1995): 469–70. http://dx.doi.org/10.1080/00401706.1995.10484400.

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Mullainathan, Sendhil, and Jann Spiess. "Machine Learning: An Applied Econometric Approach." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 87–106. http://dx.doi.org/10.1257/jep.31.2.87.

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Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Machine learning not only provides new tools, it solves a different problem. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. So applying machine learning to economics requires finding relevant tasks. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. We hope to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble—and thus where they can be most usefully applied.

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MİXON, Franklin, and Kamal UPADHYAYA. "Scholarly Impact of Core Econometrics Journals: A Catalog and Citations-Based Ranking." International Econometric Review 13, no. 4 (July 5, 2022): 118–31. http://dx.doi.org/10.33818/ier.984141.

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With 23 core econometrics journals now in operation, this study fills a gap in the literature by cataloguing the editorial specifications of each journal, and, more importantly, assessing their impact based on citations to research published in each over the 15-year period beginning in 2001 and ending in 2015. Our investigation reveals that about one-half of all core econometrics journals publish in an online format only, while the others publish both online and in-print. About one-fourth of all core econometrics journals are affiliated with an academic organization or society, while the same number are published by either Elsevier, Springer or Wiley. In terms of our assessment, Econometrica, Journal of Econometrics and Journal of Applied Econometrics sit atop our citations index ranking, while the relatively-new Journal of Financial Econometrics breaks into the top five, and other younger journals, such as the Journal of Econometric Methods and Econometrics, also make a strong showing in what is the first academic study assessing their productivity.

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

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

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8

SENGUPTA, JATI K. "Maximum entropy in applied econometric research." International Journal of Systems Science 22, no. 10 (October 1991): 1941–51. http://dx.doi.org/10.1080/00207729108910760.

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9

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

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

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10

Hisnanick, John J., and Kern O. Kymn. "Econometric techniques applied to productivity growth estimation." Atlantic Economic Journal 16, no. 3 (September 1988): 88. http://dx.doi.org/10.1007/bf02304852.

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11

Rancan, Antonella. "Econometric modelling in Italy: From economic planning to academic research." HISTORY OF ECONOMIC THOUGHT AND POLICY, no. 1 (November 2021): 63–82. http://dx.doi.org/10.3280/spe2021-001003.

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The paper deals with the introduction and acceptance of econometric model-ling as a tool to conduct economic policy analysis in Italy in the Post War. A re-search practice first applied in public and private institutions other than universi-ties. It is argued that economic planning and policymakers' needs of empirical es-timations, simulations and forecasts played an important role in supporting quan-titative research, at the time when economics was still conceived as a theoretical discipline. Sylos Labini's (1967) econometric model, the Modellaccio (1970-75), the University of Bologna model (1976) were the first examples of econometric modelling activities within academia. Only since the late 1980s, also due to a gen-erational change, econometrics is fully accepted and introduced in economics cur-ricula with the discipline that aligned to international standards.

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Yarkina, Natalya, and Natalya Logunova. "Applied potential of econometric instrumentation of substantiation of economic decisions within fisheries sector." E3S Web of Conferences 91 (2019): 08054. http://dx.doi.org/10.1051/e3sconf/20199108054.

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The article discusses the econometrics potential to substantiate economic decisions aimed at improving the competitiveness of fishery enterprises. The authors specified the definition of the economic category “competitiveness of fishery enterprises” and emphasized the importance of this concept as an indicator of the effectiveness of economic decisions. The paper contains the analysis of indicator methods for assessing the competitiveness of enterprises. The modified graphical indicator method named “competitiveness polygon” is recommended for assessing the competitiveness of fishery enterprises. The method for calculating the area of a competitiveness polygon was improved by replacing the expert method of determining the values of significance coefficients for relevant competitiveness factors with the econometric method by composing a multiple regression equation.

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13

Batchelor, Roy, and J. P. Ancot. "Analysing the Structure of Econometric Models. (Advanced Studies in Theoretical and Applied Econometrics, Volume 2.)." Economica 54, no. 214 (May 1987): 273. http://dx.doi.org/10.2307/2554412.

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14

Storm, Hugo, Kathy Baylis, and Thomas Heckelei. "Machine learning in agricultural and applied economics." European Review of Agricultural Economics 47, no. 3 (August 21, 2019): 849–92. http://dx.doi.org/10.1093/erae/jbz033.

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AbstractThis review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.

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15

Hausman, Jerry. "An Econometric Life." Journal of Econometrics 211, no. 1 (July 2019): 4–10. http://dx.doi.org/10.1016/j.jeconom.2018.12.002.

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16

Maksimov, A., and V. Kramkov. "Joint session of the conference “Modern econometric tools and applications” (META2020) and the workshop “Applied Econometrics”." Applied Econometrics 61 (2021): 140–43. http://dx.doi.org/10.22394/1993-7601-2021-61-140-143.

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17

Fíglová, Zuzana. "Econometric Analysis of Panel Data Applied to Household Characteristics." Acta Oeconomica Pragensia 15, no. 1 (February 1, 2007): 13–19. http://dx.doi.org/10.18267/j.aop.32.

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18

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

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

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Angrist, Joshua D., and Jörn-Steffen Pischke. "Undergraduate Econometrics Instruction: Through Our Classes, Darkly." Journal of Economic Perspectives 31, no. 2 (May 1, 2017): 125–44. http://dx.doi.org/10.1257/jep.31.2.125.

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The past half-century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist's typical mission was to “explain” economic variables like wages or GDP growth. Applied econometrics has since evolved to prioritize the estimation of specific causal effects and empirical policy analysis over general models of outcome determination. Yet econometric instruction remains mostly abstract, focusing on the search for “true models” and technical concerns associated with classical regression assumptions. Questions of research design and causality still take a back seat in the classroom, in spite of having risen to the top of the modern empirical agenda. This essay traces the divergent development of econometric teaching and empirical practice, arguing for a pedagogical paradigm shift.

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20

Slottje, Daniel J., Daniel L. Millimet, and Michael J. Buchanan. "Econometric analysis of copyrights." Journal of Econometrics 139, no. 2 (August 2007): 303–17. http://dx.doi.org/10.1016/j.jeconom.2006.10.013.

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21

Szabó, Zsombor, and Árpád Török. "Spatial Econometrics – Usage in Transportation Sciences: A Review Article." Periodica Polytechnica Transportation Engineering 48, no. 2 (May 23, 2019): 143–49. http://dx.doi.org/10.3311/pptr.12047.

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Nowadays the spatial econometrics is became widely used in transportation sciences. In order to know which method can be used or how they should be used, the review articles give answers. In this recent paper the goal is to collect all of the methods in one article which can be used in further researches. The improvement in this article is that beside the spatial econometric methods, other necessary techniques are also introduced. With this fact a whole analysis can be applied.

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22

Tinbergen, J. "On a Method of Statistical Business-Cycle Research. A Reply (With comment by J. M. Keynes)." Voprosy Ekonomiki, no. 4 (April 20, 2007): 46–59. http://dx.doi.org/10.32609/0042-8736-2007-4-46-59.

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In his polemic reply Tinbergen defends the econometric method he applied. He reproaches Keynes for the inaccuracy of his argument and justifies the applicability of linear relationships in economic analysis and their existence in economic interactions. Tinbergen also provides a detailed account of some methodological (primarily addressing the problem of causality) and economic issues of his analysis. In a comment to the Tinbergen’s paper Keynes clarifies his point of view on some particular points as well as on econometrics as a whole.

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23

Chung, Joseph H. "Introduction." L'Actualité économique 51, no. 4 (July 20, 2009): 505–9. http://dx.doi.org/10.7202/800641ar.

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Abstract One of the most significant post-war development in economics has been the contribution of econometrics to the refinement of techniques of analysis. Econometrics is now an integral part of economics teaching in most of the known universities throughout the world and large econometric models are being used for economic policies in industrialized countries. The CANDIDE model (Canadian Disaggregated Inter-Departmental Econometric Model) is a medium-term policy oriented model and an indication of some degree of maturity in the Art of model building in CANADA. The purpose of this special issue of L'Actualité Économique is to initiate undergraduate students in economics as well as the interested public to the model, to show how the model can be applied and finally to discuss some of its deficiencies. It is hoped that this special issue will be a useful tool for the teaching of econometrics in Canada. It has three parts. The first part comprising three papers, explains the nature and the characteristics of the model. The second part through five papers shows various applications of the model. Finally, in the third part, seven papers discuss some of the deficiencies of the major blocks of the model1. 1 Only the first two parts are included in this issue. The third part will be published in the next issue.

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24

Abadie, Alberto, and Matias D. Cattaneo. "Econometric Methods for Program Evaluation." Annual Review of Economics 10, no. 1 (August 2, 2018): 465–503. http://dx.doi.org/10.1146/annurev-economics-080217-053402.

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Program evaluation methods are widely applied in economics to assess the effects of policy interventions and other treatments of interest. In this article, we describe the main methodological frameworks of the econometrics of program evaluation. In the process, we delineate some of the directions along which this literature is expanding, discuss recent developments, and highlight specific areas where new research may be particularly fruitful.

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25

Athey, Susan, and Guido W. Imbens. "Machine Learning Methods That Economists Should Know About." Annual Review of Economics 11, no. 1 (August 2, 2019): 685–725. http://dx.doi.org/10.1146/annurev-economics-080217-053433.

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We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.

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26

Ehrenberg, Ronald G. "Econometric studies of higher education." Journal of Econometrics 121, no. 1-2 (July 2004): 19–37. http://dx.doi.org/10.1016/j.jeconom.2003.10.008.

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27

Anatolyev, Stanislav, and Grigory Kosenok. "ANOTHER NUMERICAL METHOD OF FINDING CRITICAL VALUES FOR THE ANDREWS STABILITY TEST." Econometric Theory 28, no. 1 (August 2, 2011): 239–46. http://dx.doi.org/10.1017/s0266466611000314.

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We propose a method, alternative to that of Estrella (2003, Econometric Theory 19, 1128–1143), of obtaining exact asymptotic p-values and critical values for the popular Andrews (1993, Econometrica 61, 821–856) test for structural stability. The method is based on inverting an integral equation that determines the intensity of crossing a boundary by the asymptotic process underlying the test statistic. Further integration of the crossing intensity yields a p-value. The proposed method can potentially be applied to other stability tests that employ the supremum functional.

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28

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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Cassing, S., and F. Giarratani. "A Simulation-Oriented Regional Econometric Model." Environment and Planning A: Economy and Space 18, no. 12 (December 1986): 1611–28. http://dx.doi.org/10.1068/a181611.

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This paper concerns the structure and application of an econometric model of the Pittsburgh region. A distinction is drawn between forecasting models and those to be used for policy simulation. With this in mind, a simulation-oriented model based on annual data is specified and applied.

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30

Geweke, John, Patrick J. F. Groenen, Richard Paap, and Herman K. van Dijk. "Computational techniques for applied econometric analysis of macroeconomic and financial processes." Computational Statistics & Data Analysis 51, no. 7 (April 2007): 3506–8. http://dx.doi.org/10.1016/j.csda.2006.11.015.

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31

Bjerkholt, Olav. "TRYGVE HAAVELMO AT THE COWLES COMMISSION." Econometric Theory 31, no. 1 (June 27, 2014): 1–84. http://dx.doi.org/10.1017/s026646661400019x.

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The article reviews the early history of the Cowles Commission (CC), its close and intertwined relations with the Econometric Society (ES), and the influence and guidance of Ragnar Frisch. It provides detail on three rounds of choosing a research director for CC in 1937–38, in 1939 and, particularly, at the end of 1942. Haavelmo’s work in the early 1940s came to play a major role for the econometric research at the Cowles Commission under Jacob Marschak as research director 1943–48. The article points to the importance of Abraham Wald and Jacob Marschak for the success of Haavelmo’s venture and its influence and tells the story of how it came about that Haavelmo’s ideas were adopted, applied, and disseminated by the Cowles Commission. Thus the mention of Trygve Haavelmo in the title is referring also to his econometric ideas. The ideas themselves and their further evolvement at the CC have been a dominating theme in the history of econometrics literature, e.g., Hildreth (1986), Epstein (1987), Morgan (1990), Qin (1993), and Christ (1994). The article discusses the recruitment, the inner workings, and various other concerns of the Cowles econometricians, from Marxism to Black Magic. It recounts at some length the efforts made by Marschak to recruit Abraham Wald to the University of Chicago and the Cowles Commission. The article can be read as a sequel to Bjerkholt (2005, 2007).

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32

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

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

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33

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

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34

Makeienko, Maryna. "Symbolic Analysis Applied to the Specification of Spatial Trends and Spatial Dependence." Entropy 22, no. 4 (April 20, 2020): 466. http://dx.doi.org/10.3390/e22040466.

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This article provides symbolic analysis tools for specifying spatial econometric models. It firstly considers testing spatial dependence in the presence of potential leading deterministic spatial components (similar to time-series tests for unit roots in the presence of temporal drift and/or time-trend) and secondly considers how to econometrically model spatial economic relations that might contain unobserved spatial structure of unknown form. Hypothesis testing is conducted with a symbolic-entropy based non-parametric statistical procedure, recently proposed by Garcia-Cordoba, Matilla-Garcia, and Ruiz (2019), which does not rely on prior weight matrices assumptions. It is shown that the use of geographically restricted semiparametric spatial models is a promising modeling strategy for cross-sectional datasets that are compatible with some types of spatial dependence. The results state that models that merely incorporate space coordinates might be sufficient to capture space dependence. Hedonic models for Baltimore, Boston, and Toledo housing prices datasets are revisited, studied (with the new proposed procedures), and compared with standard spatial econometric methodologies.

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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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Hendry, David F. "Achievements and challenges in econometric methodology." Journal of Econometrics 100, no. 1 (January 2001): 7–10. http://dx.doi.org/10.1016/s0304-4076(00)00045-2.

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Hong, Han, and Matthew Shum. "Econometric models of asymmetric ascending auctions." Journal of Econometrics 112, no. 2 (February 2003): 327–58. http://dx.doi.org/10.1016/s0304-4076(02)00199-9.

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Li, Tong. "Indirect inference in structural econometric models." Journal of Econometrics 157, no. 1 (July 2010): 120–28. http://dx.doi.org/10.1016/j.jeconom.2009.10.027.

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Judge, George. "Fellow’s opinion corner: Econometric information recovery." Journal of Econometrics 176, no. 1 (September 2013): 1–2. http://dx.doi.org/10.1016/j.jeconom.2013.03.005.

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Tarasova, Tatyana Alexandrovna, Evgeniya Vitalievna Ivashchenko, and Irina Evgenievna Ovsyannikova. "Echnology of orientation of the applied use of the logarithmic function in econometric models of production activity." KANT 39, no. 2 (June 15, 2021): 418–23. http://dx.doi.org/10.24923/2222-243x.2021-39.73.

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The purpose of the study is to show the use of the logarithmic function in mathematical statistics, using the example of the Cobb-Douglas production function, and to introduce students to the content of individual concepts and formulas of econometric modeling. The contents of individual concepts and formulas based on econometric modeling are disclosed in order to teach students the ability to model and calculate some aspects of production activity, evaluate the correctness of the research procedure, master subject terminology and additional knowledge of economic orientation. The scientific novelty lies in the construction of a regression mathematical model describing the dependence of demand Y on price or income X, for seven regions of the Krasnodar Territory. As a result, the technology of the orientation of the applied.

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Magnus, Jan R., and Mary S. Morgan. "The ET Interview: Professor J. Tinbergen." Econometric Theory 3, no. 1 (February 1987): 117–42. http://dx.doi.org/10.1017/s0266466600004151.

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Jan Tinbergen is one of the founding fathers of econometrics, publishing in the field from 1927 until the early 1950s. This was the frontier age of econometrics when the distinction between mathematical economics and econometrics, let alone between theoretical and applied econometrics, did not yet exist. Tinbergen's approach to economics has always been a practical one. This was highly appropriate for the new field of econometrics, and enabled him to make important contributions to conceptual and theoretical issues, but always in the context of a relevant economic problem. The development of the first macroeconometric models, the solution of the identification problem, and the understanding of dynamic models are perhaps his three most important legacies to econometrics. Tinbergen was awarded the first Nobel Memorial Prize in economics in 1969 (jointly with Ragnar Frisch) for his contributions to econometrics.Tinbergen's desire to communicate his ideas to others is matched by a talent for clear and direct writing. This gives his econometric work great appeal and an apparent simplicity which should not be underestimated. This talent was also fruitfully applied to the development of pedagogical tools for teaching econometrics to his students.Since the early 1950s Tinbergen's interests have moved on and he has made notable contributions to such diverse fields as the theory of economic policy, development planning, and income distribution. Tinbergen's political and pacifist views have always been an important element in his economics, and even, as this interview shows, his econometrics. His overriding aim has been to improve the welfare of the less fortunate in this world.It is now 60 years since Tinbergen's first article in economics appeared, yet he shows no signs of retiring. We met him on May 27, 1986, in the study of his house in The Hague, where he has lived for most of his working life and which bears the hallmarks of continued study and writing. Most of the discussion during the afternoon concerned his econometric work published in the 1930s and 1940s. He gave us his views of those early developments—both what he thought then and how he sees them now. What follows is an edited transcript of the conversation. We hope that this interview will bring alive to the readers of the 1980s the issues and difficulties faced by econometricians in the 1930s, as well as Tinbergen's characteristic response to those problems. One of Tinbergen's attributes is a considerable modesty about his own achievements; the reader should bear this in mind when reading his remarks.

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Demidova, Olga. "Methods of spatial econometrics and evaluation of government programs effectiveness." Applied Econometrics 64, no. 4 (2021): 107–34. http://dx.doi.org/10.22394/1993-7601-2021-64-107-134.

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The article provides an overview of the main spatial-econometric models and notes the shortcomings that limit their application to the description of the processes taking place in large heterogeneous countries, such as Russia. The main approaches and modifications of the models are given, which make it possible to take into account Russian conditions, and a brief description of the basic articles is given in which spatial-econometric toolkit is applied to Russian data. A very promising direction in the development of spatial-econometric methods is the improvement of methods for assessing of government programs, therefore, the article describes the main approaches how to do this.

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43

Pollock, D. S. G. "Econometric methods of signal extraction." Computational Statistics & Data Analysis 50, no. 9 (May 2006): 2268–92. http://dx.doi.org/10.1016/j.csda.2005.07.010.

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Aradillas-López, Andrés. "The Econometrics of Static Games." Annual Review of Economics 12, no. 1 (August 2, 2020): 135–65. http://dx.doi.org/10.1146/annurev-economics-081919-113720.

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This article reviews the econometrics of static games, with a focus on discrete-choice cases. These models have been used to study a rich variety of empirical problems, ranging from labor force participation to entry decisions. We outline the components of a general game and describe the problem of doing robust inference in the presence of multiple solutions, as well as the different econometric approaches that have been applied to tackle this problem. We then describe the specific challenges that arise in different variations of these models depending on whether players are assumed to have complete or incomplete information, as well as whether or not nonequilibrium play is allowed. We describe the results in 2 × 2 games (the most widely studied games in econometrics), and we present extensions and recent results in games with richer action spaces. Areas for future research are also discussed.

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Geweke, John. "Econometric issues in using the AHEAD panel." Journal of Econometrics 112, no. 1 (January 2003): 115–20. http://dx.doi.org/10.1016/s0304-4076(02)00153-7.

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Kunitomo, Naoto, Michael McAleer, and Yoshihiko Nishiyama. "Moment Restriction-Based Econometric Methods: An overview." Journal of Econometrics 165, no. 1 (November 2011): 1–4. http://dx.doi.org/10.1016/j.jeconom.2011.05.001.

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Phillips, Peter C. B., Thomas Leirvik, and Trude Storelvmo. "Econometric estimates of Earth’s transient climate sensitivity." Journal of Econometrics 214, no. 1 (January 2020): 6–32. http://dx.doi.org/10.1016/j.jeconom.2019.05.002.

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Banik, Shipra, Mohammed Anwer, and A. F. M. Khodadad Khan. "Modeling Chaotic Behavior of Chittagong Stock Indices." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/410832.

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Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock investors, policy makers, applied researchers, and many others who are involved in the capital market. In this paper, a comparative study has been conducted to predict stock index values using soft computing models and time series model. Paying attention to the applied econometric noises because our considered series are time series, we predict Chittagong stock indices for the period from January 1, 2005 to May 5, 2011. We have used well-known models such as, the genetic algorithm (GA) model and the adaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models. Very widely used forecasting models in applied time series econometrics, namely, the generalized autoregressive conditional heteroscedastic (GARCH) model is considered as time series model. Our findings have revealed that the use of soft computing models is more successful than the considered time series model.

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Gao, Jiti, Heather Anderson, and Tong Li. "Special Issue of the Journal of Econometrics on “Econometric Estimation and Testing: Essays in Honour of Maxwell King”." Journal of Econometrics 219, no. 2 (December 2020): 201–3. http://dx.doi.org/10.1016/j.jeconom.2020.03.001.

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Rudzkis, R., and E. Mačiulaitytė. "Econometrical Modelling of Profit Tax Revenue." Nonlinear Analysis: Modelling and Control 12, no. 1 (January 25, 2007): 95–112. http://dx.doi.org/10.15388/na.2007.12.1.14724.

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The aim of this article is to present a forecast of budget revenue from the profit tax using econometric models. The set of applied models has to be reduced to very simple models due to short time series used. Therefore, the profit tax regression analysis is made in two stages. In the first stage, econometric modelling of profit tax revenue with the main profit indicators (called the profit tax base) is performed on the basis of information on profit tax regulation and its changes. In the second stage, algorithms of forecasting the profit tax base are formed when the main macroeconomic indicators of Lithuanian economy are used as regressors. Crossvalidation was applied to estimate the accuracy of these algorithms.

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Journal articles on the topic 'Applied Econometric'

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