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on Forecasting |
| By: | Andrianady, Josué R. |
| Abstract: | In this study, we evaluate the effectiveness of three popular econometric models ARIMA, MIDAS, and VAR for forecasting quarterly GDP in Madagascar. Our analysis reveals that ARIMA provides the most accurate forecasts among the three models, indicating its superiority in predicting the country’s economic performance. However, we also argue that combining multiple models can offer additional benefits for forecasting accuracy and robustness. By leveraging the strengths of each model, such an approach can provide more reliable forecasts and reduce the risk of errors and biases associated with using a single model. Our findings have important implications for policymakers, economists, and investors who rely on GDP forecasts to make informed decisions about economic policies and investments in Madagascar. |
| Keywords: | GDP, Madagascar, Quarterly data, Forecasting, Arima, Var, Midas. |
| JEL: | C5 C53 E1 E17 E27 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:116916&r=for |
| By: | Stephen G. Hall (Leicester University, Bank of Greece, and Pretoria University); George S. Tavlas (Bank of Greece and the Hoover Institution, Stanford University); Yongli Wang (Birmingham University) |
| Abstract: | This paper considers the problem of forecasting inflation in the United States, the euro area and the United Kingdom in the presence of possible structural breaks and changing parameters. We examine a range of moving window techniques that have been proposed in the literature. We extend previous work by considering factor models using principal components and dynamic factors. We then consider the use of forecast combinations with time-varying weights. Our basic finding is that moving windows do not produce a clear benefit to forecasting. Time-varying combination of forecasts does produce a substantial improvement in forecasting accuracy. |
| Keywords: | forecast combinations; structural breaks; rolling windows; dynamic factor models; Kalman filter |
| JEL: | C52 C53 |
| Date: | 2023–02 |
| URL: | http://d.repec.org/n?u=RePEc:bog:wpaper:314&r=for |
| By: | Andrianady, Josué R. |
| Abstract: | In this study, we compare the performance of three econometric models ARIMA, VAR, and MIDAS for forecasting the GDP of Madagascar using quarterly data from INSTAT. Our analysis is based on three evaluation metrics : mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Our results indicate that the ARIMA model outperforms the other two models in terms of forecasting accuracy. However, the VAR and MIDAS models also demonstrate competitive performance in certain aspects, highlighting their usefulness in capturing the underlying dynamics of the GDP data. |
| Keywords: | Madagascar, GDP, Forecasting, ARIMA, VAR, MIDAS |
| JEL: | C01 C1 C53 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:116911&r=for |
| By: | Rafael Alves; Diego S. de Brito; Marcelo C. Medeiros; Ruy M. Ribeiro |
| Abstract: | We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive (VHAR) models with the least absolute shrinkage and selection operator (LASSO). Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.16151&r=for |
| By: | Nikolas Michael; Mihai Cucuringu; Sam Howison |
| Abstract: | We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generalized Regression Neural Networks, integrated with a dimensionality reduction component. To circumvent the curse of dimensionality, we employ a weighted norm based on a modified version of the maximal correlation coefficient. The pipeline we introduce is specifically designed for online tasks, has an interpretable output, and is able to outperform several state-of-the art baselines. The computational efficacy of the algorithm, its online nature, and its ability to operate in low signal-to-noise regimes, render OFTER an ideal approach for financial multivariate time series problems, such as daily equity forecasting. Our work demonstrates that while deep learning models hold significant promise for time series forecasting, traditional methods carefully integrating mainstream tools remain very competitive alternatives with the added benefits of scalability and interpretability. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.03877&r=for |