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on Forecasting |
| By: | Martin, Ertl (Institute for Advanced Studies Vienna, Austria); Fortin, Ines (Institute for Advanced Studies Vienna, Austria); Hlouskova, Jaroslava (Institute for Advanced Studies Vienna, Austria); Koch, Sebastian P. (Institute for Advanced Studies Vienna, Austria); Kunst, Robert M. (Institute for Advanced Studies Vienna, Austria); Sögner, Leopold (Institute for Advanced Studies Vienna, Austria and Vienna Graduate School of Finance (VGSF)) |
| Abstract: | Recently, many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia’s invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events, we propose a mixed-frequency Bayesian vector autoregressive (BVAR) model with t-distributed innovations or with stochastic volatility. While inflation, industrial production, oil and gas prices are available at monthly frequencies, real gross domestic product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency framework using the forward-filtering-backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries which extensively import energy from Russia and therefore have been heavily exposed to the recent oil and gas price shocks. To measure the forecast performance of our mixed-frequency BVAR model, we compare these inflation forecasts with those generated by a battery of competing inflation forecasting models. The proposed BVAR models dominate the competition for all countries in terms of the log predictive density score. |
| Keywords: | Bayesian VAR, mixed-frequency, forward-filtering-backward-sampling, inflation forecasting |
| JEL: | C5 E3 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:ihs:ihswps:number56 |
| By: | Jeremy Rothfield; Mansour Al Rajhi (King Abdullah Petroleum Studies and Research Center) |
| Abstract: | Bayesian vector autoregressions have been used by central banks to prepare short-term projections of quarterly GDP and other macroeconomic variables. The Bayesian approach offers the advantage that a researcher can use a priori knowledge to specify a prior distribution of the parameters. In this paper, we have combined monthly data for Saudi Arabia with quarterly fiscal and GDP variables to produce forecasts over an approximate 12-month period. |
| Keywords: | Economic Growth and Convergence |
| Date: | 2024–05–23 |
| URL: | https://d.repec.org/n?u=RePEc:prc:dpaper:ks--2024-dp17 |
| By: | Huaqing Xie; Xingcheng Xu; Fangjia Yan; Xun Qian; Yanqing Yang |
| Abstract: | GDP is a vital measure of a country's economic health, reflecting the total value of goods and services produced. Forecasting GDP growth is essential for economic planning, as it helps governments, businesses, and investors anticipate trends, make informed decisions, and promote stability and growth. While most previous works focus on the prediction of the GDP growth rate for a single country or by machine learning methods, in this paper we give a comprehensive study on the GDP growth forecasting in the multi-country scenario by deep learning algorithms. For the prediction of the GDP growth where only GDP growth values are used, linear regression is generally better than deep learning algorithms. However, for the regression and the prediction of the GDP growth with selected economic indicators, deep learning algorithms could be superior to linear regression. We also investigate the influence of the novel data -- the light intensity data on the prediction of the GDP growth, and numerical experiments indicate that they do not necessarily improve the prediction performance. Code is provided at https://github.com/Sariel2018/Multi-Coun try-GDP-Prediction.git. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.02551 |