| 1. |
- Ankargren, Sebastian, et al.
(författare)
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A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior
- 2020
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Ingår i: Journal of Time Series Econometrics. - : Walter de Gruyter GmbH. - 1941-1928 .- 2194-6507. ; 12:2
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Tidskriftsartikel (refereegranskat)abstract
- We propose a Bayesian vector autoregressive (VAR) model for mixed-frequency data. Our model is based on the mean-adjusted parametrization of the VAR and allows for an explicit prior on the 'steady states' (unconditional means) of the included variables. Based on recent developments in the literature, we discuss extensions of the model that improve the flexibility of the modeling approach. These extensions include a hierarchical shrinkage prior for the steady-state parameters, and the use of stochastic volatility to model heteroskedasticity. We put the proposed model to use in a forecast evaluation using US data consisting of 10 monthly and 3 quarterly variables. The results show that the predictive ability typically benefits from using mixed-frequency data, and that improvements can be obtained for both monthly and quarterly variables. We also find that the steady-state prior generally enhances the accuracy of the forecasts, and that accounting for heteroskedasticity by means of stochastic volatility usually provides additional improvements, although not for all variables.
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| 2. |
- Ankargren, Sebastian, et al.
(författare)
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A mixed-frequency Bayesian vector autoregression with a steady-state prior
- 2018
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We consider a Bayesian vector autoregressive (VAR) model allowing for an explicit priorspecication for the included variables' `steady states' (unconditional means) for data measuredat dierent frequencies. We propose a Gibbs sampler to sample from the posteriordistribution derived from a normal prior for the steady state and a normal-inverse-Wishart prior for the dynamics and error covariance. Moreover, we suggest a numerical algorithmfor computing the marginal data density that is useful for nding appropriate values for thenecessary hyperparameters. We evaluate the proposed model by applying it to a real-timedata set where we forecast Swedish GDP growth. The results indicate that the inclusionof high-frequency data improves the accuracy of low-frequency forecasts, in particular forshorter time horizons. The proposed model thus facilitates a simple and helpful way ofincorporating information about the long run through the steady-state prior as well asabout the near future through its ability to cope with mixed frequencies of the data.
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| 3. |
- Ankargren, Sebastian, et al.
(författare)
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Estimating a VECM for a small open economy
- 2018
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- One of the most popular ways to model macro economic variables is bythe vector error correction model (VECM). Besides forecasting and testing ofhypotheses, the VECM is often used for calculating impulse responses, whichdescribe how shocks today aect the variables in the future. In economic theory,a small open economy denotes the economy of a country which is toosmall to inuence the surrounding world. The surrounding world can, for thisreason, be seen as exogenous relative to the economy of this small open economy.The main contribution of this paper is the proposal of how to estimatea VECM with exogeneity restrictions on both the short-run dynamics andthe short-run adjustment parameters between small open economies and thesurrounding world. A Monte Carlo simulation of impulse responses showsthat the proposed model is considerably more ecient compared to modelsfully or partially ignoring exogeneity. It is also shown that the empirical sizewhen testing for the number of long-run relations is closer to the nominalsize. Using two Swedish macroeconomic data sets the proposed method isapplied to estimate the models under weak exogeneity and Granger noncausality,respectively. We nd for some variables large deviances in impulseresponses between our proposed model incorporating both types of restrictionsand models using none or only one type of restriction, thus illustratingthe need for imposing the full set of restrictions instead of settling for just one.
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| 4. |
- Ankargren, Sebastian, et al.
(författare)
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Estimating a VECM for a Small Open Economy
- 2019
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In economic theory, the term small open economy refers to an economy that is too small to influence the surrounding world. The surrounding world can, for this reason, be seen as exogenous relative to the economy of this small open economy. The main contribution of this paper is the proposal of how to estimate a vector error correction model with exogeneity restrictions on the long-run parameters, the adjustment parameters as well as on the short-run dynamic parameters between small open economies and the surrounding world. A Monte Carlo simulation study of impulse responses shows that the proposed method is considerably more efficient compared to models that fully or partially ignore the restrictions implied by the small open economy property. Using two Swedish macroeconomic datasets, we find that there are, for some variables, large differences in impulse responses between our proposed method incorporating the restrictions and models using no or partial restrictions. As the small open economy property is in many situations uncontroversial, our method enables the incorporation of indisputable economic theory into the econometric estimation of the model.
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| 5. |
- Ankargren, Sebastian, et al.
(författare)
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Estimating Large Mixed-Frequency Bayesian VAR Models
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We discuss the issue of estimating large-scale vector autoregressive (VAR) models with stochastic volatility in real-time situations where data are sampled at different frequencies. In the case of a large VAR with stochastic volatility, the mixed-frequency data warrant an additional step in the already computationally challenging Markov Chain Monte Carlo algorithm used to sample from the posterior distribution of the parameters. We suggest the use of a factor stochastic volatility model to capture a time-varying error covariance structure. Because the factor stochastic volatility model renders the equations of the VAR conditionally independent, settling for this particular stochastic volatility model comes with major computational benefits. First, we are able to improve upon the mixed-frequency simulation smoothing step by leveraging a univariate and adaptive filtering algorithm. Second, the regression parameters can be sampled equation-by-equation in parallel. These computational features of the model alleviate the computational burden and make it possible to move the mixed-frequency VAR to the high-dimensional regime. We illustrate the model by an application to US data using our mixed-frequency VAR with 20, 34 and 119 variables.
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| 6. |
- Ankargren, Sebastian, et al.
(författare)
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Mixed-Frequency Bayesian VAR Models in R: The mfbvar Package
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Time series are often sampled at different frequencies, which leads to mixed-frequency data. Mixed frequencies are often neglected in applications as high-frequency series are aggregated to lower frequencies. In the mfbvar package, we introduce the possibility to estimate Bayesian vector autoregressive (VAR) models when the set of included time series consists of monthly and quarterly variables. The package implements several common prior distributions as well as stochastic volatility methods. The mixed-frequency nature of the data is handled by assuming that quarterly variables are weighted averages of unobserved monthly observations. We provide a user-friendly interface for model estimation and forecasting. The capabilities of the package are illustrated in an application.
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| 7. |
- Ankargren, Sebastian, et al.
(författare)
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On the equivalence of confidence interval estimation based on frequentist model averagingand least-squares for the full model in linear regression
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In many applications of linear regression models, model selection is vital. However, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty in these linear models, least squares frequentist model averaging has been proposed recently. In this paper, we show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. Furthermore, we demonstrate that this equivalence also holds in finite samples if the parameter of interest is a linear function of the regression coefficients.
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| 8. |
- Ankargren, Sebastian, et al.
(författare)
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On the least-squares model averaging interval estimator
- 2018
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Ingår i: Communications in Statistics - Theory and Methods. - : Informa UK Limited. - 0361-0926 .- 1532-415X. ; 47:1, s. 118-132
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Tidskriftsartikel (refereegranskat)abstract
- In many applications of linear regression models, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty, least-squares frequentist model averaging has been proposed recently. We show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. The finite-sample confidence intervals based on approximations to the asymptotic distributions are also equivalent if the parameter of interest is a linear function of the regression coefficients. Furthermore, we demonstrate that this equivalence also holds for prediction intervals constructed in the same fashion.
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| 9. |
- Ankargren, Sebastian, et al.
(författare)
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Simulation smoothing for nowcasting with large mixed-frequency VARs
- 2021
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Ingår i: Econometrics and Statistics. - : Elsevier. - 2452-3062. ; 19, s. 97-113
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Tidskriftsartikel (refereegranskat)abstract
- There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macro-economic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows. We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.
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| 10. |
- Ankargren, Sebastian, et al.
(författare)
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The importance of the financial system for the real economy
- 2017
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Ingår i: Empirical Economics. - : Springer Science and Business Media LLC. - 0377-7332 .- 1435-8921. ; 53:4, s. 1553-1586
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Tidskriftsartikel (refereegranskat)abstract
- This paper first describes financial variables that have been constructed to correspond to various channels in the transmission mechanism. Next, a Bayesian VAR model for the macroeconomy, with priors on the steady states, is augmented with these financial variables and estimated using Swedish data for 1989–2015. The results support three conclusions. First, the financial system is important and the strength of the results is dependent on identification, with the financial variables accounting for 10–25 % of the forecast error variance of Swedish GDP growth. Second, the suggested model produces an earlier signal regarding the probability of recession, compared to a model without financial variables. Third, the model’s forecasts for the deep downturn in 2008 and 2009, conditional on the development of the financial variables, outperform a macro-model that lacks financial variables. Furthermore, this improvement in modelling Swedish GDP growth during the financial crisis does not come at the expense of unconditional predictive power. Taken together, the results suggest that the proposed model presents an accessible possibility to analyse the macro-financial linkages and the GDP developments, especially during a financial crisis.
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