Dynamic factor model with infinite‐dimensional factor space: Forecasting

Forni, Mario; Giovannelli, Alessandro; Lippi, Marco; Soccorsi, Stefano

doi:10.1002/jae.2634

“…As in [11], to assess the forecasting performance of each couple of methods locally, each time series of the dataset is smoothed by a centered moving average of length m = 61 (with coefficients equal to 1/m) and then the Fluctuation test ( [16]) is run, at 5% significance level. The results for the IP at horizons h ∈ {6, 12, 24} are reported in Figure 2.…”

Section: Prediction Of the Industrial Production And The Inflationmentioning

confidence: 99%

“…The same conclusions are drawn in [10] over the forecasting of Dutch GDP. So far, a systematic comparison of the forecasting performances of SW, FHLR and FHLZ can be found only in [11]. Here, Forni et al conducted a forecasting exercise on an US macroeconomic dataset, where they took an autoregressive process of order 4 as a benchmark.…”

Section: Introductionmentioning

confidence: 99%

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A forecasting performance comparison of dynamic factor models based on static and dynamic methods

Marra

¹

2017

Communications in Applied and Industrial Mathematics

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We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b). The second is based on generalized principal components and it was introduced by Forni, Hallin, Reichlin (2000, 2005). The last model has been recently proposed by Zaffaroni (2015, 2016). The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a insample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016), but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.

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“…the h-step ahead FHLR forecasting equation can be finally derived as (5) χ it+h|t =χ it+h|t +ξ it+h|t .…”

Section: The Fhlr Modelmentioning

confidence: 99%

“…This model was proposed by Forni et al in [5], [6]. Differently from the previous models, here the assumption that the common component spans a finite-dimensional space is relaxed.…”

Section: The Fhlz Modelmentioning

confidence: 99%

A Forecasting Performance Comparison of Dynamic Factor Models Based on Static and Dynamic Methods

Marra

¹

2017

SSRN Journal

0

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We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b). The second is based on generalized principal components and it was introduced by Forni, Hallin, Reichlin (2000, 2005). The last model has been recently proposed by Zaffaroni (2015, 2016). The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a insample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016), but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.

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Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach

Zhang

¹

,

Yang

²

,

Zhang

³

et al. 2023

Journal of Futures Markets

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This paper proposes a new network topology approach to identify the contemporaneous and noncontemporaneous idiosyncratic spillovers of lowermoment and higher-moment risks in commodity futures markets using highfrequency data. Our results show that contemporaneous information has more explanatory power in constructing a network than noncontemporaneous information, especially for higher-moment risk spillover networks. In contemporaneous spillover networks, the role of one commodity future and the structure of the networks vary across different realized estimators. Specifically, gold, silver, and wheat are the main volatility and kurtosis risk transmitters, while corn and silver are the main skewness spillover transmitters. Agricultural futures markets are relatively closed in the volatility and kurtosis risk spillover networks, while in the skewness network, they become closer to precious metal futures. Furthermore, crisis events can enlarge the idiosyncratic volatility spillovers in commodity markets. The total spillover effects of higher-moment risk are stronger than those of lower-moment risk.

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Dynamic factor model with infinite‐dimensional factor space: Forecasting

Cited by 44 publications

References 47 publications

A forecasting performance comparison of dynamic factor models based on static and dynamic methods

A forecasting performance comparison of dynamic factor models based on static and dynamic methods

A Forecasting Performance Comparison of Dynamic Factor Models Based on Static and Dynamic Methods

Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach

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