Forecasting UK consumer price inflation using inflation forecasts

Hassani, Hossein; Silva, Emmanuel Sirimal

doi:10.1016/j.rie.2018.07.001

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…Evidence from statistical datasets [51,52]. Hassani and Silva, Rasool and Kiani, Naderi et al [53][54][55][56] explain that neural networks by their nature form assumptions that restrict their application, for example, linearity which are needed for the making for the mathematical models tractable . However their treatments vary by the area in which it is applied.…”

Section: Neural Network Modelmentioning

confidence: 99%

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Dumor

Yao

2019

Sustainability

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The Belt and Road Initiative (BRI) under the auspices of the Chinese government was created as a regional integration and development model between China and her trade partners. Arguments have been raised as to whether this initiative will be beneficial to participating countries in the long run. We set to examine how to estimate this trade initiative by comparing the relative estimation powers of the traditional gravity model with the neural network analysis using detailed bilateral trade exports data from 1990 to 2017. The results show that neural networks are better than the gravity model approach in learning and clarifying international trade estimation. The neural networks with fixed country effects showed a more accurate estimation compared to a baseline model with country-year fixed effects, as in the OLS estimator and Poisson pseudo-maximum likelihood. On the other hand, the analysis indicated that more than 50% of the 6 participating East African countries in the BRI were able to attain their predicted targets. Kenya achieved an 80% (4 of 5) target. Drawing from the lessons of the BRI and the use of neural network model, it will serve as an important reference point by which other international trade interventions could be measured and compared.

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Section: Neural Network Modelmentioning

confidence: 99%

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Dumor

Yao

2019

Sustainability

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“…To augment these findings, we further compare predictive accuracy in terms of the root mean square forecast error (RMSE) using the non‐parametric test proposed by Hassani and Silva (, ). In so doing, we first calculate the RRMSE (defined as the RMSE of the A‐MA forecasts divided by the RMSE of the MA forecasts).…”

Section: Forecast Evaluation Resultsmentioning

confidence: 99%

Predicting United Arab Emirates' real effective exchange rates using oil prices

Baghestani

AbuAl-Foul

2019

OPEC Energy Review

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We examine whether oil prices help produce accurate forecasts of the real broad effective exchange rate of the United Arab Emirates (UAE). Using monthly data for 1994–1999, we formulate a univariate moving average (MA) and an augmented moving average (A‐MA) model to generate multi‐period forecasts of UAE real exchange rates for 2000–2019. The MA model utilises past information in real exchange rates, while the A‐MA model utilises past information in both real exchange rates and oil prices. Our results indicate that oil prices help produce accurate forecasts of UAE real exchange rates only for 2000–2009; that is, for this period, the A‐MA forecasts are unbiased, outperform the MA forecasts and are directionally accurate. As for 2009–2019, we take a non‐parametric approach and show that oil price changes accurately predict directional change in UAE real effective exchange rates.

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“…The performance of models is fully evaluated by grid search. It is also widely used in some extended ARMA models [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A study of the impact of COVID‐19 on the Chinese stock market based on a new textual multiple ARMA model

et al. 2022

Statistical Analysis

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Coronavirus 2019 (COVID‐19) has caused violent fluctuation in stock markets, and led to heated discussion in stock forums. The rise and fall of any specific stock is influenced by many other stocks and emotions expressed in forum discussions. Considering the transmission effect of emotions, we propose a new Textual Multiple Auto Regressive Moving Average (TM‐ARMA) model to study the impact of COVID‐19 on the Chinese stock market. The TM‐ARMA model contains a new cross‐textual term and a new cross‐auto regressive (AR) term that measure the cross impacts of textual emotions and price fluctuations, respectively, and the adjacent matrix which measures the relationships among stocks is updated dynamically. We compute the textual sentiment scores by an emotion dictionary‐based method, and estimate the parameter matrices by a maximum likelihood method. Our dataset includes the textual posts from the Eastmoney Stock Forum and the price data for the constituent stocks of the FTSE China A50 Index. We conduct a sliding‐window online forecast approach to simulate the real‐trading situations. The results show that TM‐ARMA performs very well even after the attack of COVID‐19.

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Forecasting UK consumer price inflation using inflation forecasts

Cited by 15 publications

References 35 publications

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Predicting United Arab Emirates' real effective exchange rates using oil prices

A study of the impact of COVID‐19 on the Chinese stock market based on a new textual multiple ARMA model

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