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Beyond Traditional Probabilistic Methods in Econometrics

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Book cover Beyond Traditional Probabilistic Methods in Economics (ECONVN 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 809))

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Abstract

We elaborate on various uncertainty calculi in current research efforts to improve empirical econometrics. These consist essentially of considering appropriate non additive (and non commutative) probabilities, as well as taking into account economic data which involved economic agents’ behavior. After presenting a panorama of well-known non traditional probabilistic methods, we focus on the emerging effort of taking the analogy of financial econometrics with quantum mechanics to exhibit the promising use of quantum probability for modeling human behavior, and of Bohmian mechanics for modeling economic data.

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Author information

Authors and Affiliations

  1. Department of Mathematical Sciences, New Mexico State University, Las Cruces, NM, 88003, USA

    Hung T. Nguyen

  2. Faculty of Economics, Chiang Mai University, Chiang Mai, 50200, Thailand

    Hung T. Nguyen

  3. Banking University of Ho-Chi-Minh City, 36 Ton That Dam Street, District 1, Ho-Chi-Minh City, Vietnam

    Nguyen Duc Trung & Nguyen Ngoc Thach

Authors
  1. Hung T. Nguyen
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  2. Nguyen Duc Trung
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  3. Nguyen Ngoc Thach
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Corresponding author

Correspondence to Hung T. Nguyen .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

  2. Banking University HCMC, Ho Chi Minh City, Vietnam

    Nguyen Ngoc Thach

  3. Banking University HCMC, Ho Chi Minh City, Vietnam

    Nguyen Duc Trung

  4. TTC Group, Ho Chi Minh City, Vietnam

    Dang Van Thanh

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Nguyen, H.T., Trung, N.D., Thach, N.N. (2019). Beyond Traditional Probabilistic Methods in Econometrics. In: Kreinovich, V., Thach, N., Trung, N., Van Thanh, D. (eds) Beyond Traditional Probabilistic Methods in Economics. ECONVN 2019. Studies in Computational Intelligence, vol 809. Springer, Cham. https://doi.org/10.1007/978-3-030-04200-4_1

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