On first and second order stationarity of random coefficient models

Boshnakov, Georgi N.

doi:10.1016/j.laa.2010.09.023

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…Transformations such as logarithms can help to stabilize the variance of a time series [53]. We performed the estimation using the least squares method and tested the validity of the model, its degree of reliability, and the statistical significance of the parameters included in the model.…”

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confidence: 99%

Impact of Employed Labor Force, Investment, and Remittances on Economic Growth in EU Countries

et al. 2020

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This paper analyzes the evolution and influence of gross domestic product per capita, labor force participation rate, gross fixed capital formation, and personal remittances on economic growth for European Union (EU) countries, using data from the World Bank (1996–2019) and from Eurostat (the first two quarters 2019–2020). The study has three components: statistical analysis, analysis of the evolution for each country and EU, and estimation of the impact on economic growth rate by using a linear multifactorial regression model for 1996–2019, 1996–2008, and 2009–2019. The model was validated by econometric techniques. The long-term causal relationship between exogenous and endogenous variables was validated by the Granger test. The results of the study show a differentiated evolution of the indicators, and that all indicators are severely affected by the 2008 financial crisis and the debut of the COVID-19 crisis in early 2020. The model used shows the significant positive influence of labor and investment, and the minor effect of remittances on economic growth. In the context of the COVID-19 epidemic, the results of the study could be arguments to be considered for the redesign of economic policies at European Union level.

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confidence: 99%

Impact of Employed Labor Force, Investment, and Remittances on Economic Growth in EU Countries

et al. 2020

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“…So they only gave the second order stationarity conditions for MAR( M ;1,…,1) and MAR( M ;2,…,2) models in theorems 2 and 3 of Wong & Li (), respectively, and for MVAR( K , M ;1,…,1), MVAR( K , M ;2,…,2) and MAR( M ; p 1 ,…, p M ) models in theorem 3, 4 and 5 of Fong et al (), respectively. Stationarity conditions for the scalar case have been given in Boshnakov (). The MVAR models can be seen as particular cases of Markov switching VARMA (MS VARMA) models in which the Markov chain is an i.i.d.…”

Section: The Mixture Vector Autoregressive Modelmentioning

confidence: 99%

Statistical Analysis Of Mixture Vector Autoregressive Models

Cavicchioli

2016

Scandinavian J Statistics

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In this paper, we reconsider the mixture vector autoregressive model, which was proposed in the literature for modelling non-linear time series. We complete and extend the stationarity conditions, derive a matrix formula in closed form for the autocovariance function of the process and prove a result on stable vector autoregressive moving-average representations of mixture vector autoregressive models. For these results, we apply techniques related to a Markovian representation of vector autoregressive moving-average processes. Furthermore, we analyse maximum likelihood estimation of model parameters by using the expectation-maximization algorithm and propose a new iterative algorithm for getting the maximum likelihood estimates. Finally, we study the model selection problem and testing procedures. Several examples, simulation experiments and an empirical application based on monthly financial returns illustrate the proposed procedures.

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“…Much more recent references can be found in [7]. The approaches of all these papers, however, are quite different compared to the one presented below.…”

Section: B Arrc(2) Signalsmentioning

confidence: 99%

“…This is for example the case when using bilinear models introduced in the standard book of Granger [8]. But this is also one important topic in the analysis of autoregressive models with random coefficients [6], [7], [9], [10]. The question is in general rather complicated, but the structure of the random coefficients in the particular model (5) introduces important simplifications.…”

Section: B Arrc(2) Signalsmentioning

confidence: 99%