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- Gustafsson, Oskar, 1990-, et al.
(författare)
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Bayesian optimization of hyperparameters from noisy marginal likelihood estimates
- 2023
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Ingår i: Journal of applied econometrics (Chichester, England). - : Wiley. - 0883-7252 .- 1099-1255. ; 38:4, s. 577-595
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Tidskriftsartikel (refereegranskat)abstract
- Bayesian models often involve a small set of hyperparameters determined by maximizing the marginal likelihood. Bayesian optimization is an iterative method where a Gaussian process posterior of the underlying function is sequentially updated by new function evaluations. We propose a novel Bayesian optimization framework for situations where the user controls the computational effort and therefore the precision of the function evaluations. This is a common in econometrics where the marginal likelihood is often computed by Markov chain Monte Carlo or importance sampling methods. The new acquisition strategy gives the optimizer the option to explore the function with cheap noisy evaluations and therefore find the optimum faster. The method is applied to estimating the prior hyperparameters in two popular models on US macroeconomic time series data: the steady-state Bayesian vector autoregressive (BVAR) and the time-varying parameter BVAR with stochastic volatility.
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| 3. |
- Gustafsson, Oskar, 1990-
(författare)
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Some Contributions to Heteroscedastic Time Series Analysis and Computational Aspects of Bayesian VARs
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Time-dependent volatility clustering (or heteroscedasticity) in macroeconomic and financial time series has been analyzed for more than half a century. The inefficiencies it causes in various inference procedures are well known and understood. Despite this, heteroscedasticity is surprisingly often neglected in practical work. The correct way is to model the variance jointly with the other properties of the time series by using some of the many methods available in the literature. In the first two papers of this thesis, we explore a third option, that is rarely used in the literature, in which we first remove the heteroscedasticity and only then fit a simpler model to the homogenized data.In the first paper, we introduce a filter that removes heteroscedasticity from simulated data without affecting other time series properties. We show that filtering the data leads to efficiency gains when estimating parameters in ARMA models, and in some cases to higher forecast precision for US GDP growth.The work of the first paper is extended to the case of multivariate time series in Paper II. In this paper, the stochastic volatility model is used for tracking the latent evolution of the time series variances. Also in this scenario variance stabilization offers efficiency gains when estimating model parameters.During the last decade, there has been an increasing interest in using large-scale VARs together with Bayesian shrinkage methods. The rich parameterization together with the need for simulations methods results in a computational bottleneck that either force concessions regarding the flexibility of the model or the size of the data set. In the last two papers, we address these issues with methods from the machine learning literature. In Paper III, we develop a new Bayesian optimization strategy for finding optimal hyperparameters for econometric models via maximization of the marginal likelihood. We illustrate that the algorithm finds optimal values fast compared to conventional methods. Finally, in Paper IV we present a fast variational inference (VI) algorithm for approximating the parameter posterior and predictive distribution of the steady-state BVAR. We show that VI produces results that are very close to those of the conventional Gibbs sampler but are obtained at a much lower computational cost. This is illustrated in both a simulation study and on US macroeconomic data.
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| 6. |
- Hlynsson, Jón Ingi, et al.
(författare)
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Uncertainty breeds anxiety and depression : The impact of the Russian invasion in Ukraine on a Swedish clinical population receiving internet-based psychotherapy
- 2024
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Ingår i: Clinical Psychology in Europe. - 2625-3410. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Recent global crises, such as the COVID-19 pandemic and the 2022 Russian invasion of Ukraine, have contributed to a rise in the global prevalence of anxiety and depressive disorders. This study examines the indirect impact of the Ukraine war on emotional disorders within a Swedish clinical population. Method: The sample comprised participants (n = 1,222) actively engaged in an internet-based psychotherapeutic intervention (cognitive-behavioral, psychodynamic, and waitlist) when the war broke out. The Patient Health Questionnaire-9 scale and the Generalized Anxiety Disorder-7 scale were used to measure depression and anxiety. Results: Anxiety and depressive symptom severity increased following the war's onset, with an average weekly increase of 0.77-points for anxiety (p = .001, Cohen's d = 0.08) and 0.09-points for depression (p = .70, Cohen's d = 0.01); however, the increase was negligible for depression. Furthermore, higher socioeconomic status (SES) predicted declines in depression and anxiety during the study period, with a 0.69-point average weekly decrease in anxiety (p < .001, Cohen's d = 0.32) and a 1.09-point decrease in depression (p < .001, Cohen's d = 0.48) per one unit increase in SES, suggesting that SES may serve as a protective factor that buffers against psychopathological development during crises. Conclusions: These findings have implications for mitigating the development of psychopathology during crises and interpreting treatment efficacy estimates during such events. Our findings also emphasize the potential of internet-based psychotherapy in addressing emotional disorders during crises. This study presents up-to-date information about the reaction of treatment-seeking individuals to abrupt uncertainty.
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