Simon Fraser University

Preliminary Autoregressive Models

After computing the sample autocovariance matrices, PROC STATESPACE fits a sequence of vector autoregressive models. These preliminary autoregressive models are used to estimate the autoregressive ...
Simon Fraser University

Autoregressive Error Model

The regression model with autocorrelated disturbances is as follows: In these equations, y t are the dependent values, x t is a column vector of regressor variables, is a column vector of structural ...
JSTOR Daily

Estimation for Autoregressive Time Series with a Root near 1

Estimators for the parameters of autoregressive time series are compared, emphasizing processes with a unit root or a root close to 1. The approximate bias of the sum of the autoregressive ...
Nature

Spatial Econometrics and Autoregressive Models

Spatial econometrics addresses the challenges posed by spatially correlated data, enabling researchers to understand and quantify how economic phenomena in one location can influence those in ...
TMCnet

Inception Launches Mercury 2, the Fastest Reasoning LLM - 5x Faster Than Leading Speed-Optimized LLMs, with Dramatically Lower Inference Cost

Inception, the company behind the first commercial diffusion large language models (dLLMs), today announced the launch of ...
Risk

Range-based volatility forecasting: an extended conditional autoregressive range model

This paper proposes an extended conditional autoregressive range (EXCARR) model to describe the range-based volatility dynamics of financial assets. Our EXCARR model not only takes the conditional ...
Risk

Range-based volatility forecasting: a multiplicative component conditional autoregressive range model

To capture the "long-memory" effect in volatility, a multiplicative component conditional autoregressive range (MCCARR) model is proposed. We show theoretically that the MCCARR model can capture the ...