Econometrics

TAC Method for Fitting Exponential Autoregressive Models and Others: Applications in Economy and Finance ArXiv ID: 2402.04138 “View on arXiv” Authors: Unknown Abstract There are a couple of purposes in this paper: to study a problem of approximation with exponential functions and to show its relevance for the economic science. We present results that completely solve the problem of the best approximation by means of exponential functions and we will be able to determine what kind of data is suitable to be fitted. Data will be approximated using TAC (implemented in the R-package nlstac), a numerical algorithm for fitting data by exponential patterns without initial guess designed by the authors. We check one more time the robustness of this algorithm by successfully applying it to two very distant areas of economy: demand curves and nonlinear time series. This shows TAC’s utility and highlights how far this algorithm could be used. ...

MS_Regress - The MATLAB Package for Markov Regime Switching Models ArXiv ID: ssrn-1714016 “View on arXiv” Authors: Unknown Abstract Markov state switching models are a type of specification which allows for the transition of states as an intrinsic property of the econometric model. Such type Keywords: Markov State Switching, Econometric Modeling, Time Series Analysis, Regime Change, Econometrics Complexity vs Empirical Score Math Complexity: 7.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper presents advanced econometric theory with detailed maximum likelihood estimation and regime-switching matrix formulations, but focuses on a MATLAB package’s code and installation rather than providing a specific backtest with real financial data. flowchart TD A["Research Goal: Develop MATLAB Package<br>for Markov Regime Switching Models"] --> B["Data & Inputs<br>Time Series Data & Regime Specifications"] B --> C["Computational Process<br>Maximum Likelihood Estimation"] C --> D["Key Methodology<br>Markov State Transition Modeling"] D --> E["Key Findings: MS_Regress Package<br>Enables Regime Change Analysis<br>with Econometric Precision"]

Review of Discrete and Continuous Processes inFinance: Theory and Applications ArXiv ID: ssrn-1373102 “View on arXiv” Authors: Unknown Abstract We review the main processes used to model financial variables. We emphasize the parallel between discrete-time processes, mainly used by econometricians for ri Keywords: Financial Modeling, Stochastic Processes, Time Series Econometrics, Discrete-time Processes, Econometrics Complexity vs Empirical Score Math Complexity: 8.5/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper is dense with advanced mathematics like stochastic calculus, PDEs, and detailed derivations of processes (e.g., Ornstein-Uhlenbeck, fractional Brownian motion). However, it lacks backtesting, code examples beyond mention, or empirical datasets, focusing instead on theoretical review and intuition. flowchart TD A["Research Goal:\nReview & Compare Discrete vs. Continuous\nFinancial Processes"] --> B{"Methodology"} B --> C["Literature Review"] B --> D["Theoretical Analysis"] C --> E["Data/Inputs:\nEconometric Theory\nFinancial Models\nStochastic Processes"] D --> E E --> F["Computational Process:\nParallel Comparison of\nDiscrete-time vs. Continuous-time\nModeling Frameworks"] F --> G["Key Findings:\n1. Discrete-time: Preferred for Econometrics\n2. Continuous-time: Preferred for Derivatives\n3. Bridging the gap improves forecasting"]