Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components

Ramsauer, Franz; Min, Aleksey; Lingauer, Michael

doi:10.3390/econometrics7030031

Cited by 2 publications

(4 citation statements)

References 41 publications

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“…If the reconstruction Formula (4) and error correlation assumptions enter the update steps of Bańbura and Modugno [20], both approaches coincide. Additionally, Bańbura and Modugno [20] allow for the linear restrictions given in [23,33], which can also be transferred to our approach [34].…”

Section: Estimation Of Adfms For Known Model Dimensions and Autoregrementioning

confidence: 99%

“…In this section, we treat incomplete data as stock, flow and change in flow variables. We apply the notation from, e.g., Schumacher and Breitung [4], Ramsauer et al [34]. As before, let N and T be the number of time series and sample size.…”

Section: Estimation With Incomplete Panel Datamentioning

confidence: 99%

“…In this case, the fractions in Q i in (12) are replaced by ones. Only, if all low-frequency periods comprise the same number of high-frequency periods, the sum version of the change in flow variable (13) exists [34] (p. 8, footnote 6). The change in an averaged flow variable (13) does require this equality.…”

Section: Estimation With Incomplete Panel Datamentioning

confidence: 99%

See 2 more Smart Citations

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Defend¹,

Min

Portelli³

et al. 2021

Forecasting

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This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

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Section: Estimation Of Adfms For Known Model Dimensions and Autoregrementioning

confidence: 99%

Section: Estimation With Incomplete Panel Datamentioning

confidence: 99%

See 1 more Smart Citation

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Defend¹,

Min

Portelli³

et al. 2021

Forecasting

Self Cite

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“…In Africa, Ahmed et al [89] applied the ANNs model to forecast GRACE data of African watersheds and found that the model provided the most accurate forecast. Ramsauer et al [90] adapted a Factor-Augmented Vector Autoregression Model (FAVAR) with an extension of a Kalman Filter for Factors to measure the impact of monetary policy in a case study.…”

Section: Literature Reviewmentioning

confidence: 99%

The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model

Sutthichaimethee¹,

Naluang²

2019

Energies

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This research aims to predict the efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand for the next 17 years (2020–2036) and analyze the relationships among causal factors by applying a structural equation modeling/vector autoregressive model with exogenous variables (SEM-VARIMAX Model). This model is effective for analyzing relationships among causal factors and optimizing future forecasting. It can be applied to contexts in different sectors, which distinguishes it from other previous models. Furthermore, this model ensures the absence of heteroskedasticity, multicollinearity, and autocorrelation. In fact, it meets all the standards of goodness of fit. Therefore, it is suitable for use as a tool for decision-making and planning long-term national strategies. With the implementation of the Sustainable Development Policy for Energy Consumption under Environmental Law ( S . D . E L ) , the forecast results derived from the SEM-VARIMAX Model indicate a continuously high change in energy consumption from 2020 to 2036the change exceeds the rate determined by the government. In addition, energy consumption is predicted to have an increased growth rate of up to 185.66% (2036/2020), which is about 397.08 ktoe (2036). The change is primarily influenced by a causal relationship that contains latent variables, namely, the economic factor ( E C O N ) , social factor ( S O C I ) , and environmental factor ( E N V I ) . The performance of the SEM-VARIMAX Model was tested, and the model produced a mean absolute percentage error (MAPE) of 1.06% and a root-mean-square error (RMSE) of 1.19%. A comparison of these results with those of other models, including the multiple linear regression model (MLR), back-propagation neural network (BP model), grey model, artificial neural natural model (ANN model), and the autoregressive integrated moving average model (ARIMA model), indicates that the SEM-VARIMAX model fits and is appropriate for long-term national policy formulation in various contexts in Thailand. This study’s results further indicate the low efficiency of Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand. The predicted result for energy consumption in 2036 is greater than the government-established goal for consumption of no greater than 251.05 ktoe.

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Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components

Cited by 2 publications

References 41 publications

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model

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