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The Impact of Uncertainty on Forecasting the US Economy

Journal of Forecasting, EarlyView.
ABSTRACT This paper examines the predictive value of uncertainty measures for key macroeconomic indicators across multiple forecast horizons. We evaluate how different uncertainty proxies—economic policy uncertainty (EPU), VIX, geopolitical risk, and measures of macroeconomic and financial uncertainty—enhance forecast accuracy for industrial production,
Angelica Ghiselli
wiley   +1 more source

What can the Ridge Regression Estimated Linear Probability Model tell us about ChAdOx1 nCoV-19 Vaccine Efficacy in Rhesus Macaques?

, 2020
Gana R.
europepmc   +1 more source

Using DSGE and Machine Learning to Forecast Public Debt for France

Journal of Forecasting, EarlyView.
ABSTRACT Forecasting public debt is essential for effective policymaking and economic stability, yet traditional approaches face challenges due to data scarcity. While machine learning (ML) has demonstrated success in financial forecasting, its application to macroeconomic forecasting remains underexplored, hindered by short historical time series and ...
Emmanouil Sofianos, Thierry Betti, Theophilos Papadimitriou, Amélie Barbier‐Gauchard, Periklis Gogas   +4 more
wiley   +1 more source

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Ridge regression

Wiley Interdisciplinary Reviews: Computational Statistics, 2009
AbstractRidge regression is a popular parameter estimation method used to address the collinearity problem frequently arising in multiple linear regression. The formulation of the ridge methodology is reviewed and properties of the ridge estimates capsulated.
Gary McDonald
exaly   +4 more sources

Ridge Fuzzy Regression Model

International Journal of Fuzzy Systems, 2019
Ridge regression model is a widely used model with many successful applications, especially in managing correlated covariates in a multiple regression model. Multicollinearity represents a serious threat in fuzzy regression models as well. We address this issue by combining ridge regression with the fuzzy regression model.
Seung Hoe Choi, Hye-Young Jung
exaly   +2 more sources

A Poisson ridge regression estimator [PDF]

Economic Modelling, 2011
The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method.
Månsson, Kristofer, Shukur, Ghazi
openaire   +3 more sources

Ridge regression:some simulations

Communications in Statistics, 1975
An algorithm is given for selacting the biasing paramatar, k, in RIDGE regrassion. By means of simulaction it is shown that the algorithm has the following properties: (i) it produces an aberaged squared error for the regrassion coafficiants that is les than least squares, (ii) the distribuction of squared arrots for the regression coafficiants has a ...
Hoerl, Arthur E., Kennard, Robert W., Baldwin, Kent F.   +2 more
exaly   +3 more sources

Ridge Regression: A Historical Context

Technometrics, 2020
Two classical articles on Ridge Regression by Arthur Hoerl and Robert Kennard were published in Technometrics in 1970, making 2020 their 50th anniversary.
R. Hoerl
semanticscholar   +2 more sources