Forecasting Recessions: Can We Do Better on MARS

Sephton, Peter S.

doi:10.20955/r.83.39-50

Cited by 53 publications

(36 citation statements)

References 22 publications

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“…The product of two appropriately structured Heaviside functions, as described in equation (51), has this property. In particular, 36 One also sees definitions of the Heaviside function where,…”

Section: First Splitmentioning

confidence: 99%

Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

Bolder

Rubin

2007

SSRN Journal

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of the Bank of Canada for useful comments. We would also like to thank Jeremy Graveline from the University of Minnesota and Mark Reesor from the University of Western Ontario for helpful discussions. All thanks are without implication and we retain any and all responsibility for any remaining omissions or errors.iii AbstractThe stochastic simulation model suggested by Bolder (2003) for the analysis of the federal government's debt-management strategy provides a wide variety of useful information. It does not, however, assist in determining an optimal debt-management strategy for the government in its current form. Including optimization in the debt-strategy model would be useful, since it could substantially broaden the range of policy questions that can be addressed. Finding such an optimal strategy is nonetheless complicated by two challenges. First, performing optimization with traditional techniques in a simulation setting is computationally intractable. Second, it is necessary to define precisely what one means by an "optimal" debt strategy. The authors detail a possible approach for addressing these two challenges. They address the first challenge by approximating the numerically computed objective function using a function-approximation technique. They consider the use of ordinary least squares, kernel regression, multivariate adaptive regression splines, and projection-pursuit regressions as approximation algorithms. The second challenge is addressed by proposing a wide range of possible government objective functions and examining them in the context of an illustrative example. The authors' view is that the approach permits debt and fiscal managers to address a number of policy questions that could not be fully addressed with the current stochastic simulation engine.

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Section: First Splitmentioning

confidence: 99%

Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

Bolder

Rubin

2007

SSRN Journal

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“…Most importantly, the model ensures that there is adequate data in a subgroup in order to avoid over-fitting using the shortest distance between neighboring knots [36,37]. The forward pass and the backward pass are two important phases in building a MARS model.…”

Section: Multivariate Adaptive Regression Splinementioning

confidence: 99%

MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms

et al. 2017

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Terrestrial latent heat flux (LE) is a key component of the global terrestrial water, energy, and carbon exchanges. Accurate estimation of LE from moderate resolution imaging spectroradiometer (MODIS) data remains a major challenge. In this study, we estimated the daily LE for different plant functional types (PFTs) across North America using three machine learning algorithms: artificial neural network (ANN); support vector machines (SVM); and, multivariate adaptive regression spline (MARS) driven by MODIS and Modern Era Retrospective Analysis for Research and Applications (MERRA) meteorology data. These three predictive algorithms, which were trained and validated using observed LE over the period [2000][2001][2002][2003][2004][2005][2006][2007], all proved to be accurate. However, ANN outperformed the other two algorithms for the majority of the tested configurations for most PFTs and was the only method that arrived at 80% precision for LE estimation. We also applied three machine learning algorithms for MODIS data and MERRA meteorology to map the average annual terrestrial LE of North America during 2002-2004 using a spatial resolution of 0.05 • , which proved to be useful for estimating the long-term LE over North America.

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“…MARS approach allows a nonlinear relationship over different intervals of (the vector of regressors) for modeling (Sephton, 2001). The relationship between the regressors and the response is fitted by basis functions that are basically splines.…”

Section: Multivariate Adaptive Regression Splinesmentioning

confidence: 99%

Variable selection with genetic algorithm and multivariate adaptive regression splines in the presence of multicollinearity

Kılınç

Aşikgil²,

Erar

et al. 2016

Int. j. adv. appl. sci

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In this paper, it is aimed to determine the true regressors explaining the dependent variable in multiple linear regression models and also to find the best model by using two different approaches in the presence of low, medium and high multicollinearity. These approaches compared in this study are genetic algorithm and multivariate adaptive regression splines. A comprehensive Monte Carlo experiment is performed in order to examine the performance of these approaches. This study exposes that nonparametric methods can be preferred for variable selection in order to obtain the best model when there is a multicollinearity problem in the small, medium or large data sets.

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Forecasting Recessions: Can We Do Better on MARS

Cited by 53 publications

References 22 publications

Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

Optimization in a Simulation Setting: Use of Function Approximation in Debt Strategy Analysis

MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms

Variable selection with genetic algorithm and multivariate adaptive regression splines in the presence of multicollinearity

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