Forecasting Using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation

Kapetanios, George; Labhard, Vincent; Price, Simon

doi:10.2139/ssrn.824726

Cited by 31 publications

(42 citation statements)

References 33 publications

(12 reference statements)

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“…Another option is to set certain model prior probabilities shrinking the parameter search space. The resulting model averaging strategies are well discussed in Kapetanios et al (2008). However, these methods investigate only a certain portion of all potential submodels.…”

Section: Heuristic Algorithm and Resulting Modelsmentioning

confidence: 99%

Heuristic model selection for leading indicators in Russia and Germany

Savin

Winker²

2013

OECD Journal: Journal of Business Cycle Measurement and Analysis

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Section: Heuristic Algorithm and Resulting Modelsmentioning

confidence: 99%

Heuristic model selection for leading indicators in Russia and Germany

Savin

Winker²

2013

OECD Journal: Journal of Business Cycle Measurement and Analysis

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“…Also the full enumeration of all possible solutions is only feasible for a moderate k. Consequently, in the last decade many studies have been devoted to the problem in (1): sequential bottom-up (top-down) inclusion (deletion) of individual regressors (Perez-Amaral et al, 2003;Hendry and Krolzig, 2005); usage of certain prior probabilities shrinking the parameter search space and resulting in model averaging (Kapetanios et al, 2008). However, these methods investigate only a specific fraction of all submodels, whereas there is no guarantee to find the 'true' model in this way.…”

Section: Heuristic Optimization Methodsmentioning

confidence: 99%

Lasso–type and Heuristic Strategies in Model Selection and Forecasting

Savin

Winker

2013

Towards Advanced Data Analysis by Combining Soft Computing and Statistics

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractSeveral 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 Lasso-type methods. An alternative approach is based on information criteria. In contrast to the Lasso, these methods also work well in the case of highly correlated predictors. However, this performance can be impaired by the only asymptotic consistency of the information criteria. The resulting discrete optimization problems exhibit a high computational complexity.Therefore, a heuristic optimization approach (Genetic Algorithm) is applied. The two strategies are compared by means of a Monte-Carlo simulation study together with an empirical application to leading business cycle indicators in Russia and Germany.

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“…while not directly derived from the marginal likelihood abovecan be interpreted as an approximation or frequentistic analogue of the posterior model probabilities (6) (see Kapetanios et al, 2008). According to Burnham and Anderson (2002), the difference between the AIC of two models (∆ i ) can be interpreted as the difference between the Kullback-Leibler (KL) distance for the two models (Kullback and Leibler, 1951) and hence has an attractive information theoretic interpretation.…”

Section: Bayesian Model Averaging (Bma)mentioning

confidence: 99%

The Determinants of CDS Spreads: Evidence from the Model Space

Pelster

Vilsmeier

2016

SSRN Journal

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The CDS market plays an important role in understanding the dynamics of financial markets and financial stability and consequently has received growing attention in the recent literature. Despite the rich literature there is still no consensus about which factors mainly drive CDS spreads. Instead we observe an increasingly large number of potential determinants being suggested. We aim to provide a transparent and robust analysis for the determinants of CDS spreads by focusing on the information provided by a large number of models (the "model space") instead of focusing on one or few "hand-picked" specifications. Our model space covers most of the specifications used elsewhere in the literature and includes several variables measuring the sensitivity of firms to extreme market movements (tail dependence). ContributionWe show that the model space includes models which allow backing many different theories by significant coefficients and good fit to the data. Thus, the information content from single model specifications is rather restricted. In contrast to previous studies, we use information from the entire model space instead of restricting ourselves to some "hand-picked" models. Doing so, we provide a transparent and robust analysis for the determinants of CDS spreads. Besides covering the informational content of most previous studies in our analysis we also give clear evidence on the impact of using different estimation procedures to measure tail dependence in CDS markets. ResultsUsing a large data-set, we find that CDS price dynamics can be mainly explained by factors describing firms' sensitivity to extreme market movements. More precisely, our results suggest that variables measuring tail dependence -based on so-called dynamic copula models -incorporate almost all essential pricing information making other potential determinants such as default risk (Merton-type) factors or variables capturing the systematic market evolutions negligible. The determinants of CDS spreads: evidence from the model space * Matthias Pelster Leuphana University of Lueneburg Johannes Vilsmeier Deutsche Bundesbank AbstractWe apply Bayesian Model Averaging and a frequentistic model space analysis to assess the pricing-determinants of credit default swaps (CDS). Our study focuses on the complete model space of plausible models covering most of the variables and specifications used elsewhere in the literature, including different copula models. The approach followed supports ultimate transparency and robustness for the empirical study at hand. Using a large data-set of CDS contracts we find that CDS price dynamics can be mainly explained by factors describing firms' sensitivity to extreme market movements. More precisely, our results suggest that dynamic copula based measures of tail dependence incorporate almost all essential pricing information making other potential determinants such as Merton-type factors or variables measuring the systematic market evolution -based on simple means or principal component analysis -negligible.

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Forecasting Using Bayesian and Information Theoretic Model Averaging: An Application to UK Inflation

Cited by 31 publications

References 33 publications

Heuristic model selection for leading indicators in Russia and Germany

Heuristic model selection for leading indicators in Russia and Germany

Lasso–type and Heuristic Strategies in Model Selection and Forecasting

The Determinants of CDS Spreads: Evidence from the Model Space

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