Dynamics of the Shapovalov mid-size firm model
William Barnett (),
Nikolay Kuznetsov and
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Tatyana Alexeeva: St. Petersburg School of Mathematics, Physics and Computer Science, National Research University Higher School of Economics, 194100 St. Petersburg, Kantemirovskaya ul., 3, Russia
Nikolay Kuznetsov: Faculty of Mathematics and Mechanics, St. Petersburg State University, 198504 Peterhof, St. Petersburg, Russia; Department of Mathematical Information Technology, University of Jyvaskyla, 40014 Jyvaskyla, Finland; Institute for Problems in Mechanical Engineering RAS, 199178 St. Petersburg, V.O., Bolshoj pr., 61, Russia
Timur Mokaev: Faculty of Mathematics and Mechanics, St. Petersburg State University, 198504 Peterhof, St. Petersburg, Russia
No 202007, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics
One of the main tasks in the study of financial and economic processes is forecasting and analysis of the dynamics of these processes. Within this task lie important research questions including how to determine the qualitative properties of the dynamics (stable, unstable, deterministic chaotic, and stochastic process) and how best to estimate quantitative indicators: dimension, entropy, and correlation characteristics. These questions can be studied both empirically and theoretically. In the empirical approach, one considers the real data represented by time series, identifies patterns of their dynamics, and then forecasts short- and long-term behavior of the process. The second approach is based on postulating the laws of dynamics for the process, deriving mathematical dynamic models based on these laws, and conducting subsequent analytical investigation of the dynamics generated by the models. To implement these approaches, both numerical and analytical methods can be used. It should be noted that while numerical methods make it possible to study complex models, the possibility of obtaining reliable results using them is significantly limited due to calculations being performed only over finite-time intervals, numerical integration errors, and the unbounded space of possible initial data sets. In turn, analytical methods allow researchers to overcome these problems and to obtain exact qualitative and quantitative characteristics of the process dynamics. However, their effective applications are often limited to low-dimensional models (in the modern scientific literature on this subject, two-dimensional dynamic systems are the most often studied). In this paper, we develop analytical methods for the study of deterministic dynamic systems based on the Lyapunov stability theory and on chaos theory. These methods make it possible not only to obtain analytical stability criteria and to estimate limiting behavior (localization of self-excited and hidden attractors, study of multistability), but also to overcome the difficulties related to implementing reliable numerical analysis of quantitative indicators (such as Lyapunov exponents and Lyapunov dimension). We demonstrate the effectiveness of the proposed methods using the Ã¬mid-size firmÃ® model suggested recently by V.I. Shapovalov as an example.
Keywords: mid-size firm model; forecasting; global stability; chaos; absorbing set; Lyapunov exponents; multistability (search for similar items in EconPapers)
Date: 2020-04, Revised 2020-04
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Journal Article: Dynamics of the Shapovalov mid-size firm model (2020)
Working Paper: Dynamics of the Shapovalov Mid-Size Firm Model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:kan:wpaper:202007
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