International Monetary Fund

GDP Nowcasting Performance of Traditional Econometric Models vs Machine-Learning Algorithms: Simulation and Case Studies

Are Machine Learning (ML) algorithms superior to traditional econometric models for GDP nowcasting in a time series setting?
JSTOR Daily

Estimating Time-Varying Parameters in Linear Regression Models Using A Two-Part Decomposition of the Optimal Control Formulation

Sankhyā: The Indian Journal of Statistics, Series B (1960-2002), Vol. 62, No. 3 (Dec., 2000), pp. 433-447 (15 pages) This paper discusses an econometric technique based on optimal control theory which ...
InfoWorld

An introduction to time series forecasting

Time series forecasts are used to predict a future value or a classification at a particular point in time. Here’s a brief overview of their common uses and how they are developed. Industries from ...
JSTOR Daily

A General Approach to the Limiting Distribution for Estimators in Time Series Regression with Nonstable Autoregressive Errors

We consider the time series regression model where the error term follows a nonstable autoregressive process, and present a general approach for deriving the limiting ...
Visual Studio Magazine

Neural Network Time Series Regression Using Python

The goal of a time series regression problem is best explained by a concrete example. Suppose you own an airline company and you want to predict the number of passengers you'll have next month based ...
Medscape

Long-Term Cost Effectiveness of Interferon-Beta-1a in the Treatment of Relapsing-Remitting Multiple Sclerosis

Objective: To evaluate the cost effectiveness of subcutaneous interferon-ß-1a (IFNß-1a) 44µg three times weekly in relapsing-remitting multiple sclerosis (RRMS) using an econometric model. Methods: ...