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Lasso-type and heuristic strategies in model selection and forecasting

Savin, Ivan; Winker, Peter - 2012

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Persistent link: https://www.econbiz.de/10009630302

Econometrics with Machine Learning

Chan, Felix (ed.); Mátyás, László (ed.) - 2022

Effects From Observational Health Care Data Using Machine Learning Methods -- Econometrics of Networks with Machine Learning … -- Fairness in Machine Learning and Econometrics -- Graphical Models and their Interactions with Machine Learning in the Context …This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine …

Persistent link: https://www.econbiz.de/10013365082

Russia's comparative advantages in foreign trade : forecasting potential dynamics and identifying stimulation measures

Savin, Ivan - 2013

Persistent link: https://www.econbiz.de/10009684693

Empirical Pricing Kernel Estimation Using a Functional Gradient Descent Algorithm Based on Splines

Meier, Pirmin - 2012

We propose a new methodology to estimate the empirical pricing kernel implied from option data. In contrast to most of the studies in the literature that use an indirect approach, i.e. first estimating the physical and risk-neutral densities and obtaining the pricing kernel in a second step, we...

Persistent link: https://www.econbiz.de/10013108080

Supplement To 'Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets'

Fruet Dias, Gustavo - 2017

The online Supplement presents the proof the auxiliary Lemmas 1-6, the entire set of tables with results from the Monte Carlo and the empirical studies, and further discussion on selected topics.Full paper is available at: 'https://ssrn.com/abstract=2707176' https://ssrn.com/abstract=2707176

Persistent link: https://www.econbiz.de/10012968328

Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets

Fruet Dias, Gustavo - 2017

We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares...

Persistent link: https://www.econbiz.de/10012970411

Splines for Financial Volatility

Audrino, Francesco; Buhlmann, Peter - 2007

We propose a flexible GARCH-type model for the prediction of volatility in financial time series. The approach relies on the idea of using multivariate B-splines of lagged observations and volatilities. Estimation of such a B-spline basis expansion is constructed within the likelihood framework...

Persistent link: https://www.econbiz.de/10014051065

Numerical stability analysis of linear DSGE models : backward errors, forward errors and condition numbers

Meyer-Gohde, Alexander - 2023

This paper develops and implements a backward and forward error analysis of and condition numbers for the numerical stability of the solutions of linear dynamic stochastic general equilibrium (DSGE) models. Comparing seven different solution methods from the literature, I demonstrate an...

Persistent link: https://www.econbiz.de/10014429077

A Machine Learning Approach to Estimating Large Positive Definite Covariance Matrix of High Frequency Data

Cui, Liyuan - 2019

This paper proposes a novel covariance estimator via a machine learning approach when both the sampling frequency and covariance dimension are large. Assuming that a large covariance matrix can be decomposed into low rank and sparse components, our method simultaneously provides a consistent...

Persistent link: https://www.econbiz.de/10012867396

Contour Map of Estimation Error for Expected Shortfall

Kondor, Imre - 2015

The contour map of estimation error of Expected Shortfall (ES) is constructed. It allows one to quantitatively determine the sample size (the length of the time series) required by the optimization under ES of large institutional portfolios for a given size of the portfolio, at a given...

Persistent link: https://www.econbiz.de/10013027781

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