Representation, estimation and forecasting of the multivariate index-augmented autoregressive model

Cubadda, Gianluca; Guardabascio, Barbara

doi:10.1016/j.ijforecast.2018.08.002

Cited by 19 publications

(11 citation statements)

References 42 publications

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“…which reveals that there is a clear link between the MAI and the popular dynamic factor model (Carriero et al, 2016;Cubadda and Guardabascio, 2019) with the difference that no information is lost in the MAI and that specification might not exist and must be tested.…”

Section: Multivariate Autoregressive Index Modelmentioning

confidence: 99%

Reduced Rank Regression Models in Economics and Finance

Cubadda

Hecq

2021

SSRN Journal

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Section: Multivariate Autoregressive Index Modelmentioning

confidence: 99%

Reduced Rank Regression Models in Economics and Finance

Cubadda

Hecq

2021

SSRN Journal

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“…В эконометрике задача прогнозирования временных рядов рассматривается как частный случай регрессии. Методический инструментарий прогнозирования учитывает модели экспоненциального сглаживания, регрессионные и авторегрессионные модели [8,9] и др. Стоит отметить набирающий популярность подход, учитывающий нейросетевое моделирование.…”

Section: описание метода исследованияunclassified

Neural network approach to forecasting the cost of ferroalloy products

Sirotin¹

2020

Izv. vysš. učebn. zaved., Čern. metall.

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The article notes the increasing role of ferroalloy sub-sector in the qualitative development of metallurgy. Progress predicts of modern metallurgy are difficult in the context of increasing risks of global economic development. The high volatility of domestic producers’ prices for the main ferroalloys also has a negative impact. It is necessary to develop methodological tools for forecasting changes in market prices for metallurgical products with a high degree of accuracy. One of the important areas of application in metallurgy forecasting tools is construction of a model for forecasting the cost of ferroalloy products. It is the main purpose of the study. On the example of constructing a forecast model for changing the price of ferrosilicon, relevance of the neural network approach to forecasting the cost of ferroalloy products was substantiated. As part of the tasks of industry development, the capabilities of neural networks have been poorly studied to date. Formal description of the time series forecasting model based on neural networks is given. When constructing neural networks, any time series problem is represented as a multidimensional regression problem. The main parameters of predictive networks training are highlighted. The average price of ferrosilicon on the Russian market and the prices in the Russian regions were used as input variables. The networks that meet the qualitative criteria of forecasting models were trained. Selection of the networks was carried out taking into account the results of graphical analysis and cross-checking. A neural network model was constructed to predict the change in ferrosilicon price in the short term with high accuracy. This model can be useful in strategic decisions justifying in the activities of industry research institutes and metallurgical enterprises.

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“…In this paper, we propose a new model that bridges TVP-VAR-SV and TVP-FAVAR-SV, with a new estimation strategy based on the results in KK. Specifically, to reduce the dimensionality, we draw from the recent developments in Multivariate Index Autoregressive (MAI) models, see Carriero et al (2016), Cubadda et al (2017), Cubadda and Guardabascio (2019), and Carriero et al (2020), among others. The MAI model, originally introduced by Reinsel (1983), is a bridge between reduced-rank VARs (see Carriero et al, 2015 and the references therein) and the Dynamic Factor Model (DFM, see Stock andWatson, 2016, Lippi, 2019 and the references therein).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the MAI can be applied even to small or medium VARs. Moreover, the factor structure can be tested for and not simply imposed as in the DFM, and the estimation error of the indexes is explicitly accounted for, see Cubadda and Guardabascio (2019) for further details. The contribution of the paper is twofold.…”

Section: Introductionmentioning

confidence: 99%

The Time-Varying Multivariate Autoregressive Index Model

Cubadda¹,

Grassi²,

Guardabascio³

2022

Preprint

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Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they present increasing estimation and interpretation problems. This paper tries to address this issue proposing a new Multivariate Autoregressive Index model that features time varying means and volatility. Technically, we develop a new estimation methodology that mix switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows to select or weight, in real time, the number of common components and other features of the data using Dynamic Model Selection or Dynamic Model Averaging without further computational cost. Using USA macroeconomic data, we provide a structural analysis and a forecasting exercise that demonstrates the feasibility and usefulness of this new model.

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Representation, estimation and forecasting of the multivariate index-augmented autoregressive model

Cited by 19 publications

References 42 publications

Reduced Rank Regression Models in Economics and Finance

Reduced Rank Regression Models in Economics and Finance

Neural network approach to forecasting the cost of ferroalloy products

The Time-Varying Multivariate Autoregressive Index Model

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