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Journal articles on the topic 'Economics – Statistical models'

Author: Grafiati

Published: 10 March 2023

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1

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

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

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2

Rajan, Uday, Amit Seru, and Vikrant Vig. "Statistical Default Models and Incentives." American Economic Review 100, no. 2 (May 1, 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 (October 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 (May 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 (June 12, 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 (December 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‐fulfilling way, some of the serial correlation observed in data to extrinsic noise, i.e., statistical sunspots. This leads to sunspot equilibria in models with a unique rational expectations equilibrium. Unlike many rational sunspots, these equilibria are found to be generically stable under learning. Applications are developed in the context of a New Keynesian and an asset‐pricing model.

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9

Krebs, Tom. "Statistical Equilibrium in One-Step Forward Looking Economic Models." Journal of Economic Theory 73, no. 2 (April 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 (May 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 (January 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 (February 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 (January 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 (December 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 (August 1, 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 method using data from the Worker Profiling and Reemployment Services system. (JEL C53, J15, J65, J71)

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19

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

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20

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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21

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 (December 2005): 263–79. http://dx.doi.org/10.1177/097265270500400303.

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22

Dokuchaev, Nikolai. "On statistical indistinguishability of complete and incomplete market models." Studies in Economics and Finance 38, no. 1 (February 8, 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 class of models, there exists a complete market model with arbitrarily close paths of the stock prices and the market parameters. Originality/value The paper results lead to a counterintuitive conclusion that the incomplete markets are indistinguishable in the terms of the market statistics.

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23

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 (August 2, 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-sectional dependence, particularly for models with a multifactor error structure. Tests and diagnostics for testing for unit roots, slope homogeneity, cointegration, and the number of factors are provided. We discuss issues such as estimating common factors, dealing with parameter plethora in practice, testing for structural stability and nonlinearity, and dealing with model and parameter uncertainty. Finally, we address issues related to the use of these economic panel models.

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24

Iannazzo, Sergio. "Bayesian statistic methods and theri application in probabilistic simulation models." Farmeconomia. Health economics and therapeutic pathways 8, no. 1 (March 15, 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 subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

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25

Chan, Konan, and Narasimhan Jegadeesh. "Market-Based Evaluation for Models to Predict Bond Ratings." Review of Pacific Basin Financial Markets and Policies 07, no. 02 (June 2004): 153–72. http://dx.doi.org/10.1142/s0219091504000081.

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Previous studies have examined different statistical models to predict corporate bond ratings. However, these papers use agency ratings as the benchmark to assess models and ignore the evidence that agency ratings may not be accurate in a timely manner. In this paper, we propose a new approach which incorporates ex-post bond returns to evaluate rating prediction models. Relative rating strength portfolios, formed by buying under-rated bonds with agency ratings lower than model ratings and selling over-rated bonds with agency ratings higher than model ratings, are employed to test the performance of different statistical models in rating predictions. Our results show that one version of multiple discriminant analysis model can generate a statistically significant abnormal return of 5% over a 5-year horizon. The ordered probit model which is believed to possess theoretical advantages in classifying bonds does not perform better. This suggests that using traditional measures to evaluate models can be misleading. The existence of a profitable trading strategy also raises the concern of market efficiency in the corporate bond market.

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26

Autzen, Bengt. "BAYESIAN OCKHAM’S RAZOR AND NESTED MODELS." Economics and Philosophy 35, no. 02 (January 14, 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 the philosophical literature are unsatisfactory and develop a novel reply to the problem.

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27

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 statistical test.

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28

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 (June 24, 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;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 Neural Networks (ANN) provide better results than multivariate statistical methods and are used to estimate ratings within all the range provided by the CRAs, obtaining more desegregated results than several proposed models available for intervals of ratings. Two large samples of companies ‘public data’ obtained from Bloomberg are used to obtain forecasts of S&amp;P and Moody’s ratings directly from these data with high level of accuracy. This also permits to check the published rating’s reliability provided by different CRAs.

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29

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 analysis. This paper shows how it is appropriate to focus on the definition of tourists and tourism enterprises, overcoming the uncertainties and distortions in the analyses and results that the current leaning on a generic definition of tourism entails. Furthermore, it is argued that the best impact analysis can be conducted through the use of a SAM, which in addition to being structurally consistent with the choice of defining and implementing tourists and tourist enterprises, confirming its usefulness, allows to analyse fully and, from a theoretical point of view, more correctly than I-O Tables and CGE models, the impact of inbound tourism expenditure on production structure represented by branches, on factors and therefore on GVA/GDP produced by the branches themselves and on households expenditure. Furthermore, the suggested approach offers actual possibilities for assessing the economic sustainability of tourism development. Regional and national impact analyses carried out with the use of SAMs confirm the positions claimed in the paper.</p> </abstract>

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30

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 (August 31, 1985): 25–46. http://dx.doi.org/10.1080/03796205.1985.12129249.

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31

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 (September 30, 2021): 526–54. http://dx.doi.org/10.1111/saje.12298.

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32

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

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33

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 (July 2008): 772–92. http://dx.doi.org/10.1016/j.econmod.2007.11.004.

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34

ROBINSON, PETER M. "MODELING MEMORY OF ECONOMIC AND FINANCIAL TIME SERIES." Singapore Economic Review 50, no. 01 (April 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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35

Lang, Kevin, and Ariella Kahn-Lang Spitzer. "Race Discrimination: An Economic Perspective." Journal of Economic Perspectives 34, no. 2 (May 1, 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 discrimination. Finally, we argue that economists miss the important self-enforcing relationship between disparities and discrimination and the effect of disparities in one domain on discrimination in other domains.

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36

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 restricting factor for these models to be used in practice is still limited availability of information, including not only economic but also environmental data, which is especially critical for regional researches, as the number of statistical indicators for the regions is much lower than at the macro level. The article provides mathematical description of the input-output model with an environmental block. The model is developed at the Institute of Economics and Industrial Engineering of the Siberian Branch of the RAS in collaboration with the Novosibirsk State University. The model can be used for regional researches, given the input-output tables for the region is available.

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37

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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38

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

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39

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

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40

Wong, Wing-Keung. "Review on behavioral economics and behavioral finance." Studies in Economics and Finance 37, no. 4 (June 19, 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 simulation studies to examine whether the estimators or statistics have good power and size. This all helps us to better understand financial and economic decision-making from a descriptive standpoint. Originality/value The research paper is original.

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41

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

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42

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 (September 2004): 475–86. http://dx.doi.org/10.1016/j.qref.2003.08.003.

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43

Lang, Kevin, and Jee-Yeon K. Lehmann. "Racial Discrimination in the Labor Market: Theory and Empirics." Journal of Economic Literature 50, no. 4 (December 1, 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 rational stereotypes have little empirical content. It is plausible that models combining elements of the search models with statistical discrimination could fit the data. We suggest possible avenues to be pursued and comment briefly on the implication of existing theory for public policy. (JEL J15, J31, J64, J71)

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44

Lu, Bo. "Spatial Economics Model Predicting Transport Volume." Polish Maritime Research 23, s1 (October 1, 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, taking city of Zhuanghe as the research object, the study identifies the leading industry that can produce a large number of cargoes, and further predicts the static logistics generation of the Zhuanghe and hinterlands. By integrating various factors that can affect the regional logistics requirements, this study established a logistics requirements potential model from the aspect of spatial economic principles, and expanded the way of logistics requirements prediction from the single statistical principles to an new area of special and regional economics.

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45

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

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46

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

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47

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 historians in particular, to believe that economists are uninterested in history and unreceptive to arguments based on historical evidence.

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48

KIANI, KHURSHID M. "FORECASTING FORWARD EXCHANGE RATE RISK PREMIUM IN SINGAPORE DOLLAR/US DOLLAR EXCHANGE RATE MARKET." Singapore Economic Review 54, no. 02 (June 2009): 283–98. http://dx.doi.org/10.1142/s0217590809003288.

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In this research, monthly forward exchange rates are evaluated for possible existence of time varying risk premia in Singapore forward foreign exchange rates against US dollar. The time varying risk premia in Singapore dollar is modeled using non-Gaussian signal plus noise models that encompass non-normality and time varying volatility. The results from signal plus noise models show statistically significant evidence of time varying risk premium in Singapore forward exchange rates although we failed to reject the hypotheses of no risk premium in the series. The results from Gaussian versions of these models are not much different and are in line with Wolff (1987) who also used the same methodology in Gaussian settings. Our results show statistically significant evidence of volatility clustering in Singapore forward exchange rates. The results from Gaussian signal plus noise models also show statistically significant evidence of volatility clustering and non-normality in Singapore forward foreign exchange rates. Additional tests on the series show that exclusion of conditional heteroskedasticity from the signal plus noise models leads to false statistical inferences.

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49

Féve, Patrick, and François Langot. "The RBC models through statistical inference: An application with french data." Journal of Applied Econometrics 9, S1 (December 1994): S11—S35. http://dx.doi.org/10.1002/jae.3950090503.

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Poudyal, Niraj, and Aris Spanos. "Model Validation and DSGE Modeling." Econometrics 10, no. 2 (April 7, 2022): 17. http://dx.doi.org/10.3390/econometrics10020017.

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Abstract:

The primary objective of this paper is to revisit DSGE models with a view to bringing out their key weaknesses, including statistical misspecification, non-identification of deep parameters, substantive inadequacy, weak forecasting performance, and potentially misleading policy analysis. It is argued that most of these weaknesses stem from failing to distinguish between statistical and substantive adequacy and secure the former before assessing the latter. The paper untangles the statistical from the substantive premises of inference to delineate the above-mentioned issues and propose solutions. The discussion revolves around a typical DSGE model using US quarterly data. It is shown that this model is statistically misspecified, and when respecified to arrive at a statistically adequate model gives rise to the Student’s t VAR model. This statistical model is shown to (i) provide a sound basis for testing the DSGE overidentifying restrictions as well as probing the identifiability of the deep parameters, (ii) suggest ways to meliorate its substantive inadequacy, and (iii) give rise to reliable forecasts and policy simulations.

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