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Journal articles on the topic 'Economics – Statistical models'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

de Paula, Áureo. "Econometric Models of Network Formation." Annual Review of Economics 12, no. 1 (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 relate

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2

Rajan, Uday, Amit Seru, and Vikrant Vig. "Statistical Default Models and Incentives." American Economic Review 100, no. 2 (2010): 506–10. http://dx.doi.org/10.1257/aer.100.2.506.

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3

Wolak, Frank A., and A. Ronald Gallant. "Nonlinear Statistical Models." Journal of Business & Economic Statistics 6, no. 4 (1988): 518. http://dx.doi.org/10.2307/1391473.

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4

Robinson, P. M., J. Pfanzagl, and W. Wefelmeyer. "Asymptotic Expansions for General Statistical Models." Economica 54, no. 214 (1987): 268. http://dx.doi.org/10.2307/2554408.

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5

Bayón, L., and R. García-Rubio. "New computational and statistical models in science and economics." International Journal of Computer Mathematics 92, no. 9 (2015): 1729–32. http://dx.doi.org/10.1080/00207160.2015.1049010.

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6

Canova, Fabio. "Statistical inference in calibrated models." Journal of Applied Econometrics 9, S1 (1994): S123—S144. http://dx.doi.org/10.1002/jae.3950090508.

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7

Dewey, M., D. Clayton, and M. Hills. "Statistical Models in Epidemiology." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 2 (1995): 343. http://dx.doi.org/10.2307/2983301.

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8

Branch, William A., Bruce McGough, and Mei Zhu. "Statistical sunspots." Theoretical Economics 17, no. 1 (2022): 291–329. http://dx.doi.org/10.3982/te3752.

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This paper shows that belief‐driven economic fluctuations are a general feature of many determinate macroeconomic models. In environments with hidden state variables, forecast‐model misspecification can break the link between indeterminacy and sunspots by establishing the existence of “statistical sunspots” in models that have a unique rational expectations equilibrium. To form expectations, agents regress on a set of observables that can include serially correlated nonfundamental factors (e.g., sunspots, judgment, expectations shocks, etc.). In equilibrium, agents attribute, in a self‐fulfill

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9

Krebs, Tom. "Statistical Equilibrium in One-Step Forward Looking Economic Models." Journal of Economic Theory 73, no. 2 (1997): 365–94. http://dx.doi.org/10.1006/jeth.1996.2231.

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10

Consolo, Agostino, Carlo A. Favero, and Alessia Paccagnini. "On the statistical identification of DSGE models." Journal of Econometrics 150, no. 1 (2009): 99–115. http://dx.doi.org/10.1016/j.jeconom.2009.02.012.

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11

Miftakhova, Alena, Kenneth L. Judd, Thomas S. Lontzek, and Karl Schmedders. "Statistical approximation of high-dimensional climate models." Journal of Econometrics 214, no. 1 (2020): 67–80. http://dx.doi.org/10.1016/j.jeconom.2019.05.005.

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12

Akgiray, Vedat, G. Geoffrey Booth, and Otto Loistl. "Statistical models of German stock returns." Journal of Economics 50, no. 1 (1989): 17–33. http://dx.doi.org/10.1007/bf01227606.

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13

Fearn, Tom, R. D. Retherford, and M. K. Choe. "Statistical Models for Causal Analysis." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 1 (1995): 200. http://dx.doi.org/10.2307/2983432.

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14

Jones, M. C., C. C. Clogg, and E. S. Shihadeh. "Statistical Models for Ordinal Variables." Journal of the Royal Statistical Society. Series A (Statistics in Society) 159, no. 1 (1996): 183. http://dx.doi.org/10.2307/2983484.

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15

Ravi, Sreenivasan. "Multilevel Statistical Models, 3rd edn." Journal of the Royal Statistical Society: Series A (Statistics in Society) 168, no. 1 (2005): 252–53. http://dx.doi.org/10.1111/j.1467-985x.2004.00347_5.x.

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16

Ozman, Müge. "Interactions in economic models: Statistical mechanics and networks." Mind & Society 4, no. 2 (2005): 223–38. http://dx.doi.org/10.1007/s11299-005-0014-7.

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17

Khan, Shakeeb, and Denis Nekipelov. "Information structure and statistical information in discrete response models." Quantitative Economics 9, no. 2 (2018): 995–1017. http://dx.doi.org/10.3982/qe288.

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18

Pope, Devin G., and Justin R. Sydnor. "Implementing Anti-Discrimination Policies in Statistical Profiling Models." American Economic Journal: Economic Policy 3, no. 3 (2011): 206–31. http://dx.doi.org/10.1257/pol.3.3.206.

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How should statistical models used for assigning prices or eligibility be implemented when there is concern about discrimination? In many settings, factors such as race, gender, and age are prohibited. However, the use of variables that correlate with these omitted characteristics (e.g., zip codes, credit scores) is often contentious. We provide a framework to address these issues and propose a method that can eliminate proxy effects while maintaining predictive accuracy relative to an approach that restricts the use of contentious variables outright. We illustrate the value of our proposed me

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19

Stummer, Wolfgang, and Igor Vajda. "Optimal statistical decisions about some alternative financial models." Journal of Econometrics 137, no. 2 (2007): 441–71. http://dx.doi.org/10.1016/j.jeconom.2005.10.001.

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20

Lim, Kian-Ping, Melvin J. Hinich, and Venus Khim-Sen Liew. "Statistical Inadequacy of GARCH Models for Asian Stock Markets." Journal of Emerging Market Finance 4, no. 3 (2005): 263–79. http://dx.doi.org/10.1177/097265270500400303.

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21

Reese, R. Allan, and M. C. J. van Pul. "Statistical Analysis of Software Reliability Models." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 1 (1995): 199. http://dx.doi.org/10.2307/2983430.

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22

Karabiyik, Hande, Franz C. Palm, and Jean-Pierre Urbain. "Econometric Analysis of Panel Data Models with Multifactor Error Structures." Annual Review of Economics 11, no. 1 (2019): 495–522. http://dx.doi.org/10.1146/annurev-economics-063016-104338.

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Economic panel data often exhibit cross-sectional dependence, even after conditioning on appropriate explanatory variables. Two approaches to modeling cross-sectional dependence in economic panel data are often used: the spatial dependence approach, which explains cross-sectional dependence in terms of distance among units, and the residual multifactor approach, which explains cross-sectional dependence by common factors that affect individuals to a different extent. This article reviews the theory on estimation and statistical inference for stationary and nonstationary panel data with cross-s

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23

Dokuchaev, Nikolai. "On statistical indistinguishability of complete and incomplete market models." Studies in Economics and Finance 38, no. 1 (2021): 114–25. http://dx.doi.org/10.1108/sef-01-2020-0023.

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Purpose This paper aims to investigate possibility of statistical detection of market completeness for continuous time diffusion stock market models. Design/methodology/approach The paper uses theory of forecasting to find criteria of predictability of market parameters such as volatilities and the appreciation rates. Findings It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into an incomplete one. The paper shows that market incompleteness is also non-robust: for any incomplete market from a wide cla

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24

Autzen, Bengt. "BAYESIAN OCKHAM’S RAZOR AND NESTED MODELS." Economics and Philosophy 35, no. 02 (2019): 321–38. http://dx.doi.org/10.1017/s0266267118000305.

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Abstract:While Bayesian methods are widely used in economics and finance, the foundations of this approach remain controversial. In the contemporary statistical literature Bayesian Ockham’s razor refers to the observation that the Bayesian approach to scientific inference will automatically assign greater likelihood to a simpler hypothesis if the data are compatible with both a simpler and a more complex hypothesis. In this paper I will discuss a problem that results when Bayesian Ockham’s razor is applied to nested economic models. I will argue that previous responses to the problem found in

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25

Iannazzo, Sergio. "Bayesian statistic methods and theri application in probabilistic simulation models." Farmeconomia. Health economics and therapeutic pathways 8, no. 1 (2007): 5–13. http://dx.doi.org/10.7175/fe.v8i1.251.

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Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subje

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26

Smeulders, Bart, Laurens Cherchye, and Bram De Rock. "Nonparametric Analysis of Random Utility Models: Computational Tools for Statistical Testing." Econometrica 89, no. 1 (2021): 437–55. http://dx.doi.org/10.3982/ecta17605.

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Kitamura and Stoye (2018) recently proposed a nonparametric statistical test for random utility models of consumer behavior. The test is formulated in terms of linear inequality constraints and a quadratic objective function. While the nonparametric test is conceptually appealing, its practical implementation is computationally challenging. In this paper, we develop a column generation approach to operationalize the test. These novel computational tools generate considerable computational gains in practice, which substantially increases the empirical usefulness of Kitamura and Stoye's statisti

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27

Belekuulu, Esenbek, Ulan Chortombaev, Zhanyl Baisalova, Uranbek Shergaziev, Nurbek Ibragimov, and Emilia Nazarbekova. "Economic, mathematical and statistical modeling of perennial plantations in Chui region amid climate change." BIO Web of Conferences 116 (2024): 07038. http://dx.doi.org/10.1051/bioconf/202411607038.

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The article is devoted to the study of economic, statistical and mathematical models for the cultivation of perennial plantings under conditions of global climate change in the Chui region of the Kyrgyz Republic. The authors consider the impact of climate change on production processes and the cultivation of perennial crops, such as fruit and berry crops. The article presents data from statistical analysis and mathematical models that allow us to assess the impact of climate change on the productivity and economic indicators of cultivating perennial crops. The work is relevant and important fo

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28

Ferrai, Guido, José Mondéjar Jiménez, and Yanyun Zhao. "The statistical information for tourism economics. The National Accounts perspective." National Accounting Review 4, no. 2 (2022): 204–17. http://dx.doi.org/10.3934/nar.2022012.

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<abstract> <p>Statistical information strengthening in tourism sector is recommended by the United Nations World Tourism Organization (UNWTO). Economic-statistical information is basic for carrying out effective quantitative economic analysis of tourism. Tourism Satellite Account (TSA) is not a suitable source of information for econometric analysis. National Accounts (NA), in the form of Input-Output (I-O) Tables, Computable General Equilibrium (CGE) models or Social Accounting Matrices (SAMs) represent a good source of economic-statistical information and of economic impact analy

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29

Baranov, Аlexander О., Victor N. Pavlov, Tatiana O. Tagaeva, and Yuliia M. Slepenkova. "Construction and Use of the Regional Input-Output Model with Environmental and Economic Development Blocks." World of Economics and Management 20, no. 3 (2020): 27–46. http://dx.doi.org/10.25205/2542-0429-2020-20-3-27-46.

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The paper analyzes environmental-economics models developed by foreign and Russian scientists. The first attempts to combine both economic and environmental issues in mathematical modeling were made in the 1960s, with most of them been theoretical due to lack of necessary data. With the development of modeling approaches, following on Wassily Leontief’s models, an environmental block has been included into input-output models. However, most of the existing models can hardly be applied to practice due to lack of statistical data and the absence of inter-industry approach. Even today the main re

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30

ROBINSON, PETER M. "MODELING MEMORY OF ECONOMIC AND FINANCIAL TIME SERIES." Singapore Economic Review 50, no. 01 (2005): 1–8. http://dx.doi.org/10.1142/s0217590805001809.

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Much time series data are recorded on economic and financial variables. Statistical modeling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from the viewpoint of "memory", or strength of dependence across time, which is a helpful discriminator between different phenomena of interest. Both linear and nonlinear models are discussed.

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31

Lang, Kevin, and Ariella Kahn-Lang Spitzer. "Race Discrimination: An Economic Perspective." Journal of Economic Perspectives 34, no. 2 (2020): 68–89. http://dx.doi.org/10.1257/jep.34.2.68.

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We review the empirical literature in economics on discrimination in the labor market and criminal justice system, focusing primarily on discrimination by race. We then discuss theoretical models of taste-based discrimination, particularly models of frictional labor markets and models of statistical discrimination, including recent work on invalid statistical discrimination. We explore and evaluate the evidence for and against these theories. Although there is substantial evidence of the existence of discrimination, little is known about the extent to which disparities are driven by discrimina

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32

Gallow, Michael, Geoffrey Griffiths, and John Affleck-Graves. "Earnings Forecasting on the JSE: An Empirical Study of Some Statistical Models." Studies in Economics and Econometrics 9, no. 2 (1985): 25–46. http://dx.doi.org/10.1080/03796205.1985.12129249.

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33

Botha, Byron, Tim Olds, Geordie Reid, Daan Steenkamp, and Rossouw Jaarsveld. "Nowcasting South African gross domestic product using a suite of statistical models." South African Journal of Economics 89, no. 4 (2021): 526–54. http://dx.doi.org/10.1111/saje.12298.

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34

Dufour, Jean-Marie, and Mohamed Taamouti. "Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments." Econometrica 73, no. 4 (2005): 1351–65. http://dx.doi.org/10.1111/j.1468-0262.2005.00618.x.

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35

Kapetanios, George, Vincent Labhard, and Simon Price. "Forecast combination and the Bank of England's suite of statistical forecasting models." Economic Modelling 25, no. 4 (2008): 772–92. http://dx.doi.org/10.1016/j.econmod.2007.11.004.

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36

Caridad, Daniel, Jana Hančlová, Hosn el Woujoud Bousselmi, and Lorena Caridad y López del Río. "Corporate rating forecasting using Artificial Intelligence statistical techniques." Investment Management and Financial Innovations 16, no. 2 (2019): 295–312. http://dx.doi.org/10.21511/imfi.16(2).2019.25.

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Forecasting companies long-term financial health is provided by Credit Rating Agencies (CRA) such as S&amp;amp;P, Moody’s, Fitch and others. Estimates of rates are based on publicly available data, and on the so-called ‘qualitative information’. Nowadays, it is possible to produce quite precise forecasts for these ratings using economic and financial information that is available in financial databases, utilizing statistical models or, alternatively, Artificial Intelligence techniques. Several approaches, both cross section and dynamic are proposed, using different methods. Artificial Neur

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37

Wong, Wing-Keung. "Review on behavioral economics and behavioral finance." Studies in Economics and Finance 37, no. 4 (2020): 625–72. http://dx.doi.org/10.1108/sef-10-2019-0393.

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Purpose This paper aims to give a brief review on behavioral economics and behavioral finance and discusses some of the previous research on agents' utility functions, applicable risk measures, diversification strategies and portfolio optimization. Design/methodology/approach The authors also cover related disciplines such as trading rules, contagion and various econometric aspects. Findings While scholars could first develop theoretical models in behavioral economics and behavioral finance, they subsequently may develop corresponding statistical and econometric models, this finally includes s

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38

Skalozub, V. V., and V. Vl Skalozub. "The development of the fuzzy-statistical method modeling and control problems in Economics." Science and Transport Progress, no. 31 (February 25, 2010): 249–55. http://dx.doi.org/10.15802/stp2010/13523.

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39

Luo, Lan. "Statistical model validation through white noise hypothesis testing in regression analysis and ARIMA models." Theoretical and Natural Science 42, no. 1 (2024): 99–104. http://dx.doi.org/10.54254/2753-8818/42/20240672.

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In the domain of finance and economics, ensuring the validity and dependability of forecasting models is crucial. In this paper, the White Noise Hypothesis Test is elucidated as an essential model validation tool for both the regression and ARIMA models. In the case of regression analysis, there is a need for residual independence and homoscedasticity to confirm that the estimates of the parameters are not biased, that is, the chances of the parameter estimated to be true is high. In the case of an ARIMA model, residuals are checked for the white noise characteristics, and so as to ensure that

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40

Luo, Lan. "Statistical model validation through white noise hypothesis testing in regression analysis and ARIMA models." Theoretical and Natural Science 50, no. 1 (2024): 99–104. http://dx.doi.org/10.54254/2753-8818/50/20240672.

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In the domain of finance and economics, ensuring the validity and dependability of forecasting models is crucial. In this paper, the White Noise Hypothesis Test is elucidated as an essential model validation tool for both the regression and ARIMA models. In the case of regression analysis, there is a need for residual independence and homoscedasticity to confirm that the estimates of the parameters are not biased, that is, the chances of the parameter estimated to be true is high. In the case of an ARIMA model, residuals are checked for the white noise characteristics, and so as to ensure that

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41

Lang, Kevin, and Jee-Yeon K. Lehmann. "Racial Discrimination in the Labor Market: Theory and Empirics." Journal of Economic Literature 50, no. 4 (2012): 959–1006. http://dx.doi.org/10.1257/jel.50.4.959.

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We review theories of race discrimination in the labor market. Taste-based models can generate wage and unemployment duration differentials when combined with either random or directed search even when strong prejudice is not widespread, but no existing model explains the unemployment rate differential. Models of statistical discrimination based on differential observability of productivity across races can explain the pattern and magnitudes of wage differentials but do not address employment and unemployment. At their current state of development, models of statistical discrimination based on

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42

Cummins, J. David. "Statistical and Financial Models of Insurance Pricing and the Insurance Firm." Journal of Risk and Insurance 58, no. 2 (1991): 261. http://dx.doi.org/10.2307/253237.

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43

Stengel, Mitchell, and Dennis Glennon. "Evaluating Statistical Models of Mortgage Lending Discrimination: A Bank-Specific Analysis." Real Estate Economics 27, no. 2 (1999): 299–334. http://dx.doi.org/10.1111/1540-6229.00775.

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44

Rockoff, Hugh. "History and Economics." Social Science History 15, no. 2 (1991): 239–64. http://dx.doi.org/10.1017/s0145553200021106.

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In a well-known paper published some years ago Donald McCloskey (1976) addressed his fellow economists on the importance of history to their discipline. He argued that greater emphasis on economic history would make for better economics and for better economists. It cannot be said that McCloskey’s arguments were taken to heart. The tendency for economists to expend their effort on (and award their honors for) the refinement of mathematical models and statistical techniques has continued unabated and perhaps has increased. This has led many scholars in related disciplines, social science histor

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45

Ismail Bengana. "The Statistical and Computational Revolution in Economic Growth Models: A Review of Theoretical Developments and Empirical Applications." Journal of Information Systems Engineering and Management 10, no. 37s (2025): 345–57. https://doi.org/10.52783/jisem.v10i37s.6426.

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This paper examines the transformative role of statistics and computer science in advancing economic growth theory, with particular emphasis on the evolution from neoclassical models to endogenous growth frameworks. The study analyzes how enhanced computational capabilities, and statistical methodologies have enabled economists to develop more sophisticated models that better explain empirical observations of economic growth patterns. We review key theoretical developments, including Romer's innovation-driven growth model (1986, 1990), Lucas's human capital framework (1988), and subsequent con

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46

Lu, Bo. "Spatial Economics Model Predicting Transport Volume." Polish Maritime Research 23, s1 (2016): 36–43. http://dx.doi.org/10.1515/pomr-2016-0044.

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Abstract It is extremely important to predict the logistics requirements in a scientific and rational way. However, in recent years, the improvement effect on the prediction method is not very significant and the traditional statistical prediction method has the defects of low precision and poor interpretation of the prediction model, which cannot only guarantee the generalization ability of the prediction model theoretically, but also cannot explain the models effectively. Therefore, in combination with the theories of the spatial economics, industrial economics, and neo-classical economics,

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47

Gil-Alana, Luis A. "Modelling the U.S. interest rate in terms of I(d) statistical models." Quarterly Review of Economics and Finance 44, no. 4 (2004): 475–86. http://dx.doi.org/10.1016/j.qref.2003.08.003.

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48

Simar, L�opold. "Aspects of statistical analysis in DEA-type frontier models." Journal of Productivity Analysis 7, no. 2-3 (1996): 177–85. http://dx.doi.org/10.1007/bf00157040.

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Johannes, Michael. "The Statistical and Economic Role of Jumps in Continuous-Time Interest Rate Models." Journal of Finance 59, no. 1 (2004): 227–60. http://dx.doi.org/10.1111/j.1540-6321.2004.00632.x.

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50

Suen, WING. "STATISTICAL MODELS OF CONSUMER BEHAVIOR WITH HETEROGENEOUS VALUES AND CONSTRAINTS." Economic Inquiry 28, no. 1 (1990): 79–98. http://dx.doi.org/10.1111/j.1465-7295.1990.tb00804.x.

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