Likelihood Functions for State Space Models with Diffuse Initial Conditions

Francke, Marc; Koopman, Siem Jan; Vos, A.F.

doi:10.2139/ssrn.1120262

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…(2007), with restrictions across γ 1 and γ 2 , and they can be straightforwardly added to state‐space representation (4)‐(5). When regressor variables are introduced as in model (6), one may want to consider the use of the marginal likelihood as discussed in Tunnicliffe‐Wilson (1989), Shephard (1993), and Francke et al. (2010).…”

Section: State‐space Representation Of Vecmmentioning

confidence: 99%

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Estimation of vector error correction models with mixed‐frequency data

Seong

Ahn

Zadrozny

2012

Journal Time Series Analysis

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We develop a method for directly modeling cointegrated multivariate time series that are observed in mixed frequencies. We regard lower-frequency data as regularly (or irregularly) missing and treat them with higher-frequency data by adopting a statespace model. This utilizes the structure of multivariate data as well as the available sample information more fully than the methods of transformation to a single frequency, and enables us to estimate parameters including cointegrating vectors and the missing observations of low-frequency data and to construct forecasts for future values. For the maximum likelihood estimation of the parameters in the model, we use an expectation maximization algorithm based on the state-space representation of the error correction model. The statistical efficiency of the developed method is investigated through a Monte Carlo study. We apply the method to a mixedfrequency data set that consists of the quarterly real gross domestic product and the monthly consumer price index.JEL classification: C13, C22, C32.

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Section: State‐space Representation Of Vecmmentioning

confidence: 99%

“…Durbin and Koopman (2001, Ch. 5) and Francke et al. (2010) considered other diffuse initializations.…”

Section: Em Algorithm For Estimating Vecmmentioning

confidence: 99%

Estimation of vector error correction models with mixed‐frequency data

Seong

Ahn

Zadrozny

2012

Journal Time Series Analysis

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Using the Helmert-Transformation to Reduce Dimensionality in a Mixed Model: Application to a Wage Equation with Worker and Firm Heterogeneity

2011

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Abstract:A model for matched data with two types of unobserved heterogeneity is considered -one related to the observation unit, the other to units to which the observation units are matched. One or both of the unobserved components are assumed to be random. Applying the Helmert transformation to reduce dimensionality simplifies the computational problem substantially. The framework has many potential applications; we apply it to wage modeling. Traditionally, unobserved individual and firm heterogeneity in wage equations have been represented by fixed effects. However, because of the presence of time-invariant covariates, we argue that specifications with random effects also deserve some attention. Our mixed model allows identification of the effects of time invariant variables on wages, such as for instance education. Using Norwegian manufacturing data it turns out that the assumption with respect to firm-specific unobserved heterogeneity affects the estimate of the return to education considerably.

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Likelihood Functions for State Space Models with Diffuse Initial Conditions

Cited by 2 publications

References 32 publications

Estimation of vector error correction models with mixed‐frequency data

Estimation of vector error correction models with mixed‐frequency data

Using the Helmert-Transformation to Reduce Dimensionality in a Mixed Model: Application to a Wage Equation with Worker and Firm Heterogeneity

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