Selection of Temporal Lags When Modeling Economic and Financial Processes

Area of Science:

• Econometrics
• Statistical Modeling
• Time Series Analysis

Background:

• Traditional statistical methods often struggle with the complexities of nonlinear economic and financial data.
• Accurate lag selection is crucial for building reliable time series models.

Purpose of the Study:

• To develop novel nonparametric statistical tools for modeling univariate economic and financial processes.
• To provide methods for selecting relevant lags in both linear and nonlinear time series models.
• To assess the robustness and diagnostic capabilities of the proposed statistical tests.

Main Methods:

• Development of new nonparametric statistical procedures.
• Application to linear and nonlinear univariate time series data.
• Evaluation of robustness to parameter selection.

Main Results:

• The proposed tools effectively identify relevant lags in time series models.
• The methods are applicable to both linear and nonlinear models without restriction.
• The statistical tests demonstrate robustness to the choice of free parameters.

Conclusions:

• The new nonparametric tools offer a flexible approach to economic and financial time series modeling.
• These methods enhance model accuracy through effective lag selection.
• The tests provide valuable diagnostic capabilities for model validation.