The study demonstrates a statistically significant and stable long-run equilibrium relationship between formal credit and ...

Vector autoregression (VAR): Useful for analysing time series data, making them relevant for certain aspects of patient flow forecasting, such as understanding the impact of treatment rate changes ...

Autoregression (AR) is a tool commonly used to understand and predict time series data. Traditionally the excitation noise is modelled as a Gaussian. However, real-world data may not be Gaussian in ...

This paper introduces an autoregression-based single-field deinterlacing algorithm. The proposed algorithm provides a block-wise autoregression mechanism that considers mutual influence between the ...

Advanced econometric techniques, such as Vector Autoregression (VAR) models and Artificial Neural Networks (ANNs), have been developed to improve the accuracy of exchange rate forecasting by ...

What should applied macroeconomists know about local projection (LP) and vector autoregression (VAR) impulse response estimators? The two methods share the same estimand, but in finite samples lie on ...

Abstract: The notion of a limit cycle, which can only exist in a non-linear system, plays the key role in the modelling of cyclical data. We have shown that the class of threshold autoregressive ...

Nvidia Corporation's strong fundamentals in the AI market and underestimated free cash flow growth make its shares highly ...

Apple (AAPL) is reportedly weighing the benefits of using artificial intelligence models from Anthropic or the ...

A musician sits at their computer, not composing note by note, but steering an AI collaborator through a live ...

What Are Autoregressive Models? Autoregressive models are statistical models used for time series analysis, where current values are predicted based on a linear combination of past values. These ...