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on Forecasting |
| By: | Timmermann, Allan; Zhu, Yinchu |
| Abstract: | We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the strength of the monitoring instruments. Finite-sample bounds on forecasting performance that account for estimation error are used to compute the expected loss of the competing forecasts as well as for the dynamic rotation strategy. Finally, using Monte Carlo simulations and empirical applications to forecasting inflation and stock returns, we demonstrate the potential gains from using conditioning information to rotate between forecasts |
| Keywords: | finite sample bounds; Forecasting Performance; real time monitoring |
| JEL: | C18 C32 C53 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15917&r= |
| By: | Engel, Charles M; Wu, Steve Pak Yeung |
| Abstract: | The level of the (log of) the exchange rate seems to have strong forecasting power for dollar exchange rates against major currencies post-2000 at medium- to long-run horizons of 12-, 36- and 60-months. We find that this is true using conventional asymptotic statistics correcting for serial correlation biases. But correcting for small-sample bias using simulation methods, we find little evidence to reject a random walk. This small sample bias arises because of near-spurious correlation when the predictor variable is persistent and the horizon for exchange rate forecasts is long. Similar problems of spurious correlation may arise when other persistent variables are used to forecast changes in the exchange rate. We find, in fact, using asymptotic statistics, the level of the exchange rate provides better forecasts than economic measures of "global risk", and the measures of global risk do not improve the (possibly spurious) forecasting power of the level of the exchange rate. |
| Keywords: | forecasting exchange rates |
| JEL: | C53 F30 F31 G15 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15915&r= |
| By: | Carriero, Andrea; Clark, Todd; Marcellino, Massimiliano; Mertens, Elmar |
| Abstract: | Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in volatility. In addition, we consider the treatment of outliers as missing data. Evaluating forecast performance over the last few decades in quasi-real time, we find that the outlier-augmented SV scheme does at least as well as a conventional SV model, while both out-perform standard homoskedastic VARs. Point forecasts made in 2020 from heteroskedastic VARs are much less sensitive to outliers in the data, and the outlier-adjusted SV model generates more reasonable gauges of forecast uncertainty than a standard SV model. At least pre-COVID, a close alternative to the outlier-adjusted model is an SV model with t-distributed shocks. Treating outliers as missing data also generates better-behaved forecasts than the conventional SV model. However, since uncertainty about the incidence of outliers is ignored in that approach, it leads to strikingly tight predictive densities. |
| Keywords: | Bayesian VARs; Forecasts; Outliers; Pandemics; stochastic volatility |
| JEL: | C53 E17 E37 F47 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15964&r= |
| By: | Qiutong Guo; Shun Lei; Qing Ye; Zhiyang Fang |
| Abstract: | Bitcoin, one of the major cryptocurrencies, presents great opportunities and challenges with its tremendous potential returns accompanying high risks. The high volatility of Bitcoin and the complex factors affecting them make the study of effective price forecasting methods of great practical importance to financial investors and researchers worldwide. In this paper, we propose a novel approach called MRC-LSTM, which combines a Multi-scale Residual Convolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to implement Bitcoin closing price prediction. Specifically, the Multi-scale residual module is based on one-dimensional convolution, which is not only capable of adaptive detecting features of different time scales in multivariate time series, but also enables the fusion of these features. LSTM has the ability to learn long-term dependencies in series, which is widely used in financial time series forecasting. By mixing these two methods, the model is able to obtain highly expressive features and efficiently learn trends and interactions of multivariate time series. In the study, the impact of external factors such as macroeconomic variables and investor attention on the Bitcoin price is considered in addition to the trading information of the Bitcoin market. We performed experiments to predict the daily closing price of Bitcoin (USD), and the experimental results show that MRC-LSTM significantly outperforms a variety of other network structures. Furthermore, we conduct additional experiments on two other cryptocurrencies, Ethereum and Litecoin, to further confirm the effectiveness of the MRC-LSTM in short-term forecasting for multivariate time series of cryptocurrencies. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.00707&r= |
| By: | Navid Mottaghi; Sara Farhangdoost |
| Abstract: | With the heightened volatility in stock prices during the Covid-19 pandemic, the need for price forecasting has become more critical. We investigated the forecast performance of four models including Long-Short Term Memory, XGBoost, Autoregression, and Last Value on stock prices of Facebook, Amazon, Tesla, Google, and Apple in COVID-19 pandemic time to understand the accuracy and predictability of the models in this highly volatile time region. To train the models, the data of all stocks are split into train and test datasets. The test dataset starts from January 2020 to April 2021 which covers the COVID-19 pandemic period. The results show that the Autoregression and Last value models have higher accuracy in predicting the stock prices because of the strong correlation between the previous day and the next day's price value. Additionally, the results suggest that the machine learning models (Long-Short Term Memory and XGBoost) are not performing as well as Autoregression models when the market experiences high volatility. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2105.02785&r= |
| By: | Dessaint, Olivier; Foucault, Thierry; Frésard, Laurent |
| Abstract: | We analyze the effect of alternative data on the informativeness of financial forecasts. Our starting hypothesis is that the emergence of alternative data reduces the cost of obtaining information about firms' short-term cash-flows more than their long-term cash-flows. If correct, and forecasting short-term and long-term cash-flows are distinct tasks, analysts will reduce effort to process long-term information when alternative data become available. Alternative data thus makes long-term forecasts less informative, while increasing the informativeness of short-term forecasts. We confirm this prediction using variations in analysts' exposure to social media data and a new measure of forecast informativeness at various horizons. |
| Keywords: | Alternative data; Forecasting horizon; Forecasts' informativeness; Security analysts; social media |
| JEL: | D84 G14 G17 M41 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15786&r= |
| By: | Wilson, Thomas (The University of Melbourne); Grossman, Irina; Alexander, Monica; Rees, Philip; Temple, Jeromey |
| Abstract: | Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001-2020. The key themes covered by the review are: extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socio-economic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used. |
| Date: | 2021–04–28 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:sp6me&r= |
| By: | Pacifico, Antonio |
| Abstract: | This paper aims to address semiparametric forecasting problem when studying high dimensional data in multivariate dynamic panel model with correlated random effects. A hierarchical empirical Bayesian perspective is developed to jointly deal with incidental parameters, structural framework, unobserved heterogeneity, and model misspecification problems. Methodologically, an ad-hoc model selection on a mixture of normal distributions is addressed to obtain the best combination of outcomes to construct empirical Bayes estimator and then investigate ratio-optimality and posterior consistency for better individual–specific forecasts. Simulations for Monte Carlo designs are performed to account for relative regrets dealing with correlated random effects distribution. A real case-study on the current COVID-19 pandemic crisis among a pool of developed and emerging economies is also conducted to highlight the performance of the estimating procedure. |
| Keywords: | Dynamic Panel Data; Ratio-Optimality; Bayesian Methods; Forecasting; MCMC Simulations; Tweedie Correction. |
| JEL: | C1 C5 O1 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:107523&r= |
| By: | Cimadomo, Jacopo; Giannone, Domenico; Lenza, Michele; Monti, Francesca; Sokol, Andrej |
| Abstract: | Monitoring economic conditions in real time, or nowcasting, and Big Data analytics share some challenges, sometimes called the three "Vs". Indeed, nowcasting is characterized by the use of a large number of time series (Volume), the complexity of the data covering various sectors of the economy, with different frequencies and precision and asynchronous release dates (Variety), and the need to incorporate new information continuously and in a timely manner (Velocity). In this paper, we explore three alternative routes to nowcasting with Bayesian Vector Autoregressive (BVAR) models and find that they can effectively handle the three Vs by producing, in real time, accurate probabilistic predictions of US economic activity and a meaningful narrative by means of scenario analysis. |
| Keywords: | Big Data; business cycles; Mixed frequency; Nowcasting; Real time; Scenario analysis |
| JEL: | C01 C33 C53 E32 E37 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15854&r= |
| By: | Ayodele Idowu, Mr |
| Abstract: | This study examined econometric modelling and forecasting foreign direct investment inflows in Nigeria over the next decade using Box-Jenkins ARIMA model approach. The scope of the study is from 1970 to 2020. The correlogram show that the net foreign direct investment inflow in Nigeria is integrated of the first order. Based on the number of significant coefficients, highest adjusted R-squared, lowest volatility and the lowest SBIC and the AIC, the study estimated and presents the ARIMA (1, 1, 3) model. The diagnostic test also shows that the estimated model is not only consistent but good for forecasting the net foreign direct investment inflows in Nigeria and it also explains the dynamics around it. The result of the study shows that net foreign direct investment inflows in Nigeria are likely for exhibit very slow upward trend between 2.80 billion USD and 3.26 billion USD in the next decade which is not significantly different from values of FDI inflows in Nigeria in the recent years. The study also provide policy recommendations so as to assist policy makers and the Nigerian government on better ways to accelerate and maintain higher level of net foreign direct investment inflows in Nigeria. |
| Keywords: | ARIMA, Foreign Direct Investment Inflows, Forecasting, Box-Jenkins, Nigeria. |
| JEL: | E2 F1 F17 F4 F47 |
| Date: | 2021–04–29 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:107466&r= |
| By: | Møller, Stig; Pedersen, Thomas; Schütte, Erik Christian Montes; Timmermann, Allan |
| Abstract: | We develop a new housing seach index (HSI) extracted from online search activity on a limited set of keywords related to the house buying process. We show that HSI has strong predictive power for subsequent changes in house prices, both in-sample and out-of-sample, and after controlling for the effect of commonly used predictors. Compared to the stock market, online search has much stronger predictive power over house prices and its effect also lasts longer. Variation in housing search is a particularly strong predictor of subsequent price changes in markets with inelastic housing supply and high speculation. |
| Keywords: | Forecasting; Housing Demand; housing markets; inelastic housing supply; Internet search |
| JEL: | C10 E17 G10 R3 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:15875&r= |
| By: | Nicolas Scelles (MMU - Manchester Metropolitan University); Wladimir Andreff (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne); Liliane Bonnal (Université de Poitiers - Faculté de Sciences économiques - Université de Poitiers, CRIEF - Centre de Recherche sur l'Intégration Economique et Financière - Université de Poitiers); Madeleine Andreff (Université Gustave Eiffel); Pascal Favard (IRJI - Institut de recherche juridique interdisciplinaire François Rabelais - Université de Tours) |
| Abstract: | This article aims at explaining national medal totals at the 1992–2016 Summer Olympic Games (n = 1,289 observations) and forecasting them in 2016 (based on 1992–2012 data) and 2020 with a set of variables similar to previous studies, as well as a regional (subcontinents) variable not tested previously in the literature in English. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:cesptp:hal-03206951&r= |
| By: | Nicolas Scelles (MMU - Manchester Metropolitan University); Wladimir Andreff (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, UP1 - Université Paris 1 Panthéon-Sorbonne); Liliane Bonnal (Université de Poitiers - Faculté de Sciences économiques - Université de Poitiers, CRIEF - Centre de Recherche sur l'Intégration Economique et Financière - Université de Poitiers); Madeleine Andreff (Université Gustave Eiffel); Pascal Favard (IRJI - Institut de recherche juridique interdisciplinaire François Rabelais - Université de Tours) |
| Abstract: | This article aims at explaining national medal totals at the 1992–2016 Summer Olympic Games (n = 1,289 observations) and forecasting them in 2016 (based on 1992–2012 data) and 2020 with a set of variables similar to previous studies, as well as a regional (subcontinents) variable not tested previously in the literature in English. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03206951&r= |