State-Space Methods for Time Series Analysis

Casals, José Francisco Forniés; García‐Hiernaux, Alfredo; Jerez, Miguel; Sotoca, Sonia; Trindade, A. Alexandre

doi:10.1201/9781315372471

Cited by 13 publications

(16 citation statements)

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“…We use a Matlab toolbox byCasals et al (2016) for state-space estimation of econometric models.13 We also estimate country VARX models to account for country heterogeneity (see section 5.4. ).…”

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confidence: 99%

Does Monetary Policy Affect Income Inequality in the Euro Area?

Samarina

Nguyen

2019

SSRN Journal

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confidence: 99%

Does Monetary Policy Affect Income Inequality in the Euro Area?

Samarina

Nguyen

2019

SSRN Journal

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“…Here we use the procedure by Garcia-Hiernaux et al (2009). Then, the exact ML is computed using the standard Kalman filter equations for a state space model with stochastic inputs (see, Casals et al, 2016), by iterating on the set of parameters {a 0,1 , a 1,1 , a 0,2 , a 1,2 , a 0,3 , a 1,3 , b 1 , b 2 , b 3 , ψ} and keeping the 0s and 1s in the state space matrices representation constrained to its value. 6 Obviously, the estimation through iterative methods may entail some drawbacks with respect to LS techniques, as computational cost and stability issues.…”

Section: Tf-midas Estimationmentioning

confidence: 99%

“…In order to perform the calculations we use the MatLab toolboxes E4 (Casals et al, 2016) and Midas (Ghysels, E. and collaborators, 2017). Main descriptive statistics of this ratio are presented in the following tables for each one of the DGPs considered and the cases of stock and flow variables.…”

Section: Nowcast Performance Evaluation 431 Evaluation Proceduresmentioning

confidence: 99%

TF-MIDAS: A Transfer Function Based Mixed-Frequency Model

García‐Hiernaux

2020

SSRN Journal

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This paper tackles the mixed-frequency modeling problem from a new perspective. Instead of drawing upon the common distributed lag polynomial model, we use a transfer function representation to develop a new type of models, named TF-MIDAS. We derive the theoretical TF-MIDAS implied by the high-frequency VARMA family models for two common aggregation schemes, flow and stock. This exact correspondence leads to potential gains in terms of nowcasting and forecasting performance against the current alternatives. The estimation of the model proposed is also addressed via its state space equivalent form. A Monte Carlo simulation exercise confirms that TF-MIDAS beats U-MIDAS models (its natural competitor) in terms of out-of-sample nowcasting performance for several data generating high-frequency processes.

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“…Structural time series (STS) models can be formulated in the state-space form, such as a way the typical components of the series can be defined using the unobserved variables of state and the series itself (Casals et al, 2016). The fact of being able to formulate a time series model in the state-space approach allows the use of the Kalman filter, a set of algorithms that offer the possibility of estimating the components of the series and their parameters, as well as forecasting, among others (Song et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Univariate and multivariate forecasting of tourism demand using state-space models

et al. 2019

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Tourism forecasting plays a major role in tourism planning and management and it is one of the main economic activities in many countries. For this reason, it is fundamental to provide several models that allow describing and forecasting the tourist demand. International visitants who arrive at a certain tourist destination may come from countries or regions with similar or different customs and behaviours and therefore be able to present correlated arrival patterns. Based on the state-space methods with time-varying parameters, this study develops the application and comparison of univariate and multivariate models in the applied case of German and British tourist at Canary Islands (Spain). The choice of model can be conditioned by the volume of tourists from one country with respect to the other. Structural models will be used incorporating intervention and exogenous variables, among which airline seat reservations for regular flights.

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State-Space Methods for Time Series Analysis

Cited by 13 publications

References 0 publications

Does Monetary Policy Affect Income Inequality in the Euro Area?

Does Monetary Policy Affect Income Inequality in the Euro Area?

TF-MIDAS: A Transfer Function Based Mixed-Frequency Model

Univariate and multivariate forecasting of tourism demand using state-space models

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