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on Forecasting |
| By: | Graziano Moramarco |
| Abstract: | This paper proposes an approach for enhancing density forecasts of non-normal macroeconomic variables using Bayesian Markov-switching models. Alternative views about economic regimes are combined to produce flexible forecasts, which are optimized with respect to standard objective functions of density forecasting. The optimization procedure explores both forecast combinations and Bayesian model averaging. In an application to U.S. GDP growth, the approach is shown to achieve good accuracy in terms of average predictive densities and to produce well-calibrated forecast distributions. The proposed framework can be used to evaluate the contribution of economists' views to density forecast performance. In the empirical application, we consider views derived from the Fed macroeconomic scenarios used for bank stress tests. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.13761&r= |
| By: | Mamadou-Diéne Diop (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier); Jules Sadefo Kamdem (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier) |
| Abstract: | Forecasts of spot or future prices for agricultural commodities make it possible to anticipate the favorable or above all unfavorable development of future profits from the exploitation of agricultural farms or agri-food enterprises. Previous research has shown that cyclical behavior is a dominant feature of the time series of prices of certain agricultural commodities, which may be affected by a seasonal component. Wavelet analysis makes it possible to capture this cyclicity by decomposing a time series into its frequency and time domains. This paper proposes a time-frequency decomposition based approach to choose a seasonal auto-regressive aggregate (SARIMA) model for forecasting the monthly prices of certain agricultural futures prices. The originality of the proposed approach is due to the identification of the optimal combination of the wavelet transformation type, the wavelet function and the number of decomposition levels used in the multi-resolution approach (MRA), that significantly increase the accuracy of the forecast. Our SARIMA hybrid approach contributes to take into account the cyclicity and of the seasonality when predicting commodity prices. As a relevant result, our study allows an economic agent, according to his forecasting horizon, to choose according to the available data, a specific SARIMA process for forecasting. |
| Keywords: | Commodities,Forecast,Multi-resolution analysis,Wavelets,SARIMA |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03416349&r= |
| By: | Marta Bañbura (European Central Bank); Danilo Leiva-León (Banco de España); Jan-Oliver Menz (Deutsche Bundesbank) |
| Abstract: | Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB’s SPF and Consensus Economics compared to their counterparts that do not. The gains in forecast accuracy from incorporating inflation expectations are typically not large but significant in some periods. Both short- and long-term expectations provide useful information. By contrast, incorporating expectations derived from financial market prices or those of firms and households does not lead to systematic improvements in forecast performance. Individual models we consider are typically better than univariate benchmarks but for the euro area the professional forecasters are more accurate, especially in recent years (not always for the countries). The analysis is undertaken for headline inflation and inflation excluding energy and food and both point and density forecast are evaluated using real-time data vintages over 2001-2019. |
| Keywords: | forecasting, inflation, inflation expectations, Phillips curve, bayesian VAR |
| JEL: | C53 E31 E37 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:bde:wpaper:2138&r= |
| By: | Wentao Xu; Weiqing Liu; Lewen Wang; Yingce Xia; Jiang Bian; Jian Yin; Tie-Yan Liu |
| Abstract: | Stock trend forecasting, which forecasts stock prices' future trends, plays an essential role in investment. The stocks in a market can share information so that their stock prices are highly correlated. Several methods were recently proposed to mine the shared information through stock concepts (e.g., technology, Internet Retail) extracted from the Web to improve the forecasting results. However, previous work assumes the connections between stocks and concepts are stationary, and neglects the dynamic relevance between stocks and concepts, limiting the forecasting results. Moreover, existing methods overlook the invaluable shared information carried by hidden concepts, which measure stocks' commonness beyond the manually defined stock concepts. To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts. The proposed framework simultaneously utilize the stock's shared information and individual information to improve the stock trend forecasting performance. Experimental results on the real-world tasks demonstrate the efficiency of our framework on stock trend forecasting. The investment simulation shows that our framework can achieve a higher investment return than the baselines. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.13716&r= |
| By: | Fantazzini, Dean; Pushchelenko, Julia; Mironenkov, Alexey; Kurbatskii, Alexey |
| Abstract: | This paper examines the suitability of Google Trends data for the modeling and forecasting of interregional migration in Russia. Monthly migration data, search volume data, and macro variables are used with a set of univariate and multivariate models to study the migration data of the two Russian cities with the largest migration inflows: Moscow and Saint Petersburg. The empirical analysis does not provide evidence that the more people search online, the more likely they are to relocate to other regions. However, the inclusion of Google Trends data in a model improves the forecasting of the migration flows, because the forecasting errors are lower for models with internet search data than for models without them. These results also hold after a set of robustness checks that consider multivariate models able to deal with potential parameter instability and with a large number of regressors. |
| Keywords: | Migration; Forecasting; Google Trends; VAR; Cointegration; ARIMA; Russia; Time-varying VAR; Multivariate Ridge regression. |
| JEL: | C22 C32 C52 C53 C55 F22 J11 O15 R23 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:110452&r= |
| By: | Ruipeng Liu (Department of Finance, Deakin Business School, Deakin University, Melbourne, VIC 3125, Australia); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Elie Bouri (School of Business, Lebanese American University, Lebanon) |
| Abstract: | Theory suggests the existence of a bi-directional relationship between stock market volatility and monetary policy rate uncertainty. In light of this, we forecast volatilities of equity markets and shadow short rates (SSR) - a common metric of both conventional and unconventional monetary policy decisions, by applying a bivariate Markov-switching multifractal (MSM) model. Using daily data of eight advanced economies (Australia, Canada, Euro area, Japan, New Zealand, Switzerland, the UK, and the US) over the period of January, 1995 to March, 2021, we find that the bivariate MSM model outperforms, in a statistically significant manner, not only the benchmark historical volatility and the univariate MSM models, but also the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework, particularly at longer forecast horizons. This finding confirms the bi-directional relationship between stock market volatility and uncertainty surrounding conventional and unconventional monetary policies, which in turn has important implications for academics, investors and policymakers. |
| Keywords: | Shadow short rate uncertainty, Stock market volatility, Markov-switching multifractal model (MSM), Forecasting |
| JEL: | C22 C32 C53 D80 E52 G15 |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202178&r= |
| By: | Khondaker Golam Moazzem; Helen Mashiyat Preoty |
| Abstract: | The new Power and Energy System Master Plan (PESMP) is on the process of drafting by the Ministry of Power Energy and Mineral Resources (MoPEMR). The new PSEMP aims to promote a low or zero-carbon transformation of the total energy supply and demand system. The successive PSMPs (2005, 2010 and 2016) have been criticised to have an inappropriate demand projection which led to different types of challenges. The paper reviews the successive PSMPs (PSMP 2005, 2010 and 2016) to find out the methodological weaknesses and suggests the alternative methodology for demand-side analysis of the power sector for the new plan. Based on the literature of developing countries and the findings of the key informant interviews (KIIs) the paper finds that Bangladesh needs to consider a sound methodology for proper forecasting of electricity demand. A number of methods which are methodologically well-recognised and applied to different countries such as bottom-up approach which could be more appropriate in the context of Bangladesh to forecast the power demand in the PESMP 2021. This paper concludes with a number of recommendations for the next PESMP. |
| Keywords: | PESMP, Power Sector, Power and Energy, Renewable Energy, Clean Energy, Rental Power, COVID-19, |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:pdb:opaper:139&r= |
| By: | M. Eren Akbiyik; Mert Erkul; Killian Kaempf; Vaiva Vasiliauskaite; Nino Antulov-Fantulin |
| Abstract: | Understanding the variations in trading price (volatility), and its response to external information is a well-studied topic in finance. In this study, we focus on volatility predictions for a relatively new asset class of cryptocurrencies (in particular, Bitcoin) using deep learning representations of public social media data from Twitter. For the field work, we extracted semantic information and user interaction statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute intraday price data over a 144-day horizon. Using this data, we built several deep learning architectures that utilized a combination of the gathered information. For all architectures, we conducted ablation studies to assess the influence of each component and feature set in our model. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both autoregressive and other deep learning-based models in the literature, and (ii) the tweet author meta-information, even detached from the tweet itself, is a better predictor than the semantic content and tweet volume statistics. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.14317&r= |
| By: | Allan Dizioli; Aneta Radzikowski; Daniel Rivera Greenwood |
| Abstract: | This paper introduces a simple, frequently and easily updated, close to the data epidemiological model that has been used for near-term forecast and policy analysis. We provide several practical examples of how the model has been used. We explain the epidemic development in the UK, the USA and Brazil through the model lens. Moreover, we show how our model would have predicted that a super infectious variant, such as the delta, would spread and argue that current vaccination levels in many countries are not enough to curb other waves of infections in the future. Finally, we briefly discuss the importance of how to model re-infections in epidemiological models. |
| Keywords: | COVID-19, epidemiology modelling, vaccines impact, virus variants and testing; vaccine hesitancy; vaccination data; Google mobility; virus variant; vaccination assumption; COVID-19; Emerging and frontier financial markets; Aging; Global |
| Date: | 2021–08–27 |
| URL: | http://d.repec.org/n?u=RePEc:imf:imfwpa:2021/226&r= |
| By: | Leonardo Nogueira Ferreira |
| Abstract: | This paper explores the complementarity between traditional econometrics and machine learning and applies the resulting model – the VAR-teXt – to central bank communication. The VAR-teXt is a vector autoregressive (VAR) model augmented with information retrieved from text, turned into quantitative data via a Latent Dirichlet Allocation (LDA) model, whereby the number of topics (or textual factors) is chosen based on their predictive performance. A Markov chain Monte Carlo (MCMC) sampling algorithm for the estimation of the VAR-teXt that takes into account the fact that the textual factors are estimates is also provided. The approach is then extended to dynamic factor models (DFM) generating the DFM-teXt. Results show that textual factors based on Federal Open Market Committee (FOMC) statements are indeed useful for forecasting. |
| Date: | 2021–11 |
| URL: | http://d.repec.org/n?u=RePEc:bcb:wpaper:559&r= |