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on Forecasting |
| By: | Heiner Mikosch (KOF Swiss Economic Institute, ETH Zurich, Switzerland); Laura Solanko (BOFIT, Bank of Finland, Snellmaninaukio) |
| Abstract: | GDP forecasters face tough choices over which leading indicators to follow and which forecasting models to use. To help resolve these issues, we examine a range of monthly indicators to forecast quarterly GDP growth in a major emerging economy, Russia. Numerous useful indicators are identified and forecast pooling of three model classes (bridge models, MIDAS models and unrestricted mixed-frequency models) are shown to outperform simple benchmark models. We further separately examine forecast accuracy of each of the three model classes. Our results show that differences in performance of model classes are generally small, but for the period covering the Great Recession unrestricted mixed-frequency models and MIDAS models clearly outperform bridge models. Notably, the sets of top-performing indicators differ for our two subsample observation periods (2008Q1–2011Q4 and 2012Q1–2016Q4). The best indicators in the first period are traditional real-sector variables, while those in the second period consist largely of monetary, banking sector and financial market variables. This finding supports the notion that highly volatile periods of recession and subsequent recovery are driven by forces other than those that prevail in more normal times. The results further suggest that the driving forces of the Russian economy have changed since the global financial crisis. |
| Keywords: | Keywords: Forecasting, mixed frequency data, Russia, GDP growth |
| JEL: | C53 E27 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:18-438&r=all |
| By: | Elie Bouri (USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Mark E. Wohar (College of Business Administration, University of Nebraska at Omaha, 6708 Pine Street, Omaha, NE 68182, USA, and School of Business and Economics, Loughborough University, Leicestershire, LE11 3TU, UK) |
| Abstract: | We show that the ratio of gold to platinum prices (GP) contains significant predictive information for excess U.S. government bond returns, even after controlling for a large number of financial and macro factors. Including GP in the model improves the predictive accuracy, over and above the standard macroeconomic and financial predictors, at all forecasting horizons for the shortest maturity bonds and at longer forecasting horizons for bonds with longer maturities beyond 2 years. The findings highlight the predictive information captured by commodity prices on bond market excess returns with significant investment and policy making implications. |
| Keywords: | Bond Premia, Predictability, Gold-Platinum Price Ratio, Out-of-Sample Forecasts |
| JEL: | C22 C53 G12 G17 Q02 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201967&r=all |
| By: | Chakraborty, Lekha (National Institute of Public Finance and Policy); Chakraborty, Pinaki (National Institute of Public Finance and Policy); Shrestha, Ruzel (National Institute of Public Finance and Policy) |
| Abstract: | Budget credibility, the ability of governments to accurately forecast the macro-fiscal variables, is crucial for effective Public Financial Management (PFM). Fiscal marksmanship analysis captures the extent of errors in the budgetary forecasting. The fiscal rules can determine fiscal marksmanship, as effective fiscal consolidation procedure affects the fiscal behaviour of the states in conducting the budgetary forecasts. Against this backdrop, applying Theil's technique, we analyse the fiscal forecasting errors for 28 States (except Telangana) in India for the period 2011-12 to 2015-16. There is a heterogeneity in the magnitude of errors across subnational governments in India. The forecast errors in revenue receipts have been greater than revenue expenditure. Within revenue receipts, the errors are pronounced more significantly in grants component. Within expenditure budgets, the errors in capital spending are found greater than revenue spending in all the States. Partitioning the sources of errors, we identified that the errors were more broadly random than systematic bias, except for a few crucial macro-fiscal variables where improving the forecasting techniques can provide better estimates. |
| Keywords: | forecast errors ; fiscal policies ; fiscal forecasting ; political economy ; fiscal marksmanship |
| JEL: | H6 E62 C53 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:npf:wpaper:19/280&r=all |
| By: | Nibbering, D.; Paap, R. |
| Abstract: | This paper proposes an asymmetric grouping estimator for panel data forecasting. The estimator relies on the observation that the bias- variance trade-off in potentially heterogeneous panel data may be dif- ferent across individuals. Hence, the group of individuals used for parameter estimation that is optimal in terms of forecast accuracy, may be different for each individual. For a specific individual, the estimator uses cross-validation to estimate the bias-variance of all individual groupings, and uses the parameter estimates of the optimal grouping to produce the individual-specific forecast. Integer programming and screening methods deal with the combinatorial problem of a large number of individuals. A simulation study and an application to market leverage forecasts of U.S. firms demonstrate the promising performance of our new estimators |
| Keywords: | Panel data, forecasting, parameter heterogeneity |
| Date: | 2019–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:119521&r=all |
| By: | G\'abor Petneh\'azi; J\'ozsef G\'all |
| Abstract: | This article applies a long short-term memory recurrent neural network to mortality rate forecasting. The model can be trained jointly on the mortality rate history of different countries, ages, and sexes. The RNN-based method seems to outperform the popular Lee-Carter model. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.05501&r=all |
| By: | Ansgar Belke; Jens Klose |
| Abstract: | This article compares two types of monetary policy rules – the Taylor-Rule and the Orphanides-Rule – with respect to their forecasting properties for the European Central Bank. In this respect the basic rules, results from estimates models and augmented rules are compared. Using quarterly real-time data from 1999 to the beginning of 2019, we find that an estimated Orphanides-Rule performs best in nowcasts, while it is outperformed by an augmented Taylor-Rule when it comes to forecasts. However, also a no-change rule delivers good results for forecasts, which is hard to beat for most policy rules. |
| Keywords: | Taylor-Rule, Orphanides-Rule, Monetary Policy Rates, Forecasting, European Central Bank |
| JEL: | E43 E52 E58 C53 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:rmn:wpaper:201906&r=all |
| By: | Arezoo Hatefi Ghahfarrokhi; Mehrnoush Shamsfard |
| Abstract: | In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about three months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon-based and learning-based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in TSE is different depending on their attributes. Moreover, we indicate that for predicting the closing price only comments volume and for predicting the daily return both the volume and the sentiment of the comments could be useful. We demonstrate that Users' Trust coefficients have different behaviors toward the three stocks. |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.03792&r=all |
| By: | Baptiste Barreau; Laurent Carlier; Damien Challet |
| Abstract: | We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given timeframe. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.05289&r=all |
| By: | Baptiste Barreau (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec, BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab); Laurent Carlier (BNPP CIB GM Lab - BNP Paribas CIB Global Markets Data & AI Lab); Damien Challet (MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec) |
| Abstract: | We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given timeframe. This architecture performs both investor clustering and modelling at the same time. We first verify its superior performance on a simulated scenario inspired by real data and then apply it to a large proprietary database from BNP Paribas Corporate and Institutional Banking. |
| Keywords: | investor activity prediction,deep learning,neural networks,mixture of experts,clustering |
| Date: | 2019–09–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02276055&r=all |