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on Forecasting |
| By: | Sarthak Behera; Hyeongwoo Kim; Soohyon Kim |
| Abstract: | We propose factor-augmented out of sample forecasting models for the real exchange rate between Korea and the US. We estimate latent common factors by applying an array of data dimensionality reduction methods to a large panel of monthly frequency time series data. We augment benchmark forecasting models with common factor estimates to formulate out-of-sample forecasts of the real exchange rate. Major findings are as follows. First, our factor models outperform conventional forecasting models when combined with factors from the US macroeconomic predictors. Korean factor models perform overall poorly. Second, our factor models perform well at longer horizons when American real activity factors are employed, whereas American nominal/financial market factors help improve short-run prediction accuracy. Third, models with global PLS factors from UIP fundamentals overall perform well, while PPP and RIRP factors play a limited role in forecasting. |
| Keywords: | Won/Dollar Real Exchange Rate; Principal Component Analysis; Partial Least Squares; LASSO; Out-of-Sample Forecast |
| JEL: | C38 C53 C55 F31 G17 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:abn:wpaper:auwp2020-02&r=all |
| By: | John H. Rogers; Jiawen Xu |
| Abstract: | Despite the enormous reach and influence of the literature on economic and economic policy uncertainty, one surprisingly under-researched topic has been the forecasting performance of economic uncertainty measures. We evaluate the ability of seven popular measures of uncertainty to forecast in-sample and out-of-sample over real and financial outcome variables. We also evaluate predictive content over different quantiles of the GDP growth distribution. Real-time data and estimation considerations are highly consequential, and we devote considerable attention to them. Four main findings emerge. First, there is some explanatory power in all uncertainty measures, with relatively good performance by macroeconomic uncertainty (Jurado, Ludvigson, and Ng, 2015). Second, macro uncertainty has additional predictive content over the widely-used excess bond premium of (Gilchrist and Zakrajsek, 2012) and the National Financial Conditions Index. Third, quantile regressions for GDP growth indicate strong predictive power, especially at the lower ends of the distribution, for all uncertainty measures except the VIX. Finally, we construct new real-time versions of both macroeconomic and financial uncertainty and compare them to their ex-post counterparts used in the literature. Real-time uncertainty measures have comparatively poor forecasting performance, even to the point of overturning some of the conclusions that emerge from using ex-post uncertainty measures. |
| Keywords: | Forecasting; Uncertainty; Factor model; Real-time data; Quantile regression |
| JEL: | C22 C53 |
| Date: | 2019–12–16 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2019-85&r=all |
| By: | Michael Cai; Marco Del Negro; Edward P. Herbst; Ethan Matlin; Reca Sarfati; Frank Schorfheide |
| Abstract: | This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits o fgeneralized data tempering for “online” estimation (that is, re-estimating a model asnew data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity ofthe predictive performance to changes in the prior distribution. We find that making priors less informative (compared to the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy. |
| Keywords: | Adaptive algorithms; Bayesian inference; Density forecasts; Online estimation; Sequential Monte Carlo methods |
| JEL: | C11 C32 C53 E32 E37 E52 |
| Date: | 2020–02–28 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedgfe:2020-23&r=all |
| By: | Micha{\l} Narajewski; Florian Ziel |
| Abstract: | Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a mixture of the Dirac and the Student's t-distributions. Moreover, the mixing term is estimated using a high-dimensional logistic regression with lasso penalty. We model the expected value and volatility of the series using i.a. autoregressive and no-trade effects or load, wind and solar generation forecasts and accounting for the non-linearities in e.g. time to maturity. Both the in-sample characteristics and forecasting performance are analysed using a rolling window forecasting study. Multiple versions of the model are compared to several benchmark models. The study aims to forecast the price distribution in the German Intraday Continuous Market in the last 3 hours of trading, but the approach allows for application to other continuous markets. The results prove superiority of the mixture model over the benchmarks gaining the most from the modelling of the volatility. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.01365&r=all |
| By: | Marc-Oliver Pohle |
| Abstract: | I provide a unifying perspective on forecast evaluation, characterizing accurate forecasts of all types, from simple point to complete probabilistic forecasts, in terms of two fundamental underlying properties, autocalibration and resolution, which can be interpreted as describing a lack of systematic mistakes and a high information content. This "calibration-resolution principle" gives a new insight into the nature of forecasting and generalizes the famous sharpness principle by Gneiting et al. (2007) from probabilistic to all types of forecasts. It amongst others exposes the shortcomings of several widely used forecast evaluation methods. The principle is based on a fully general version of the Murphy decomposition of loss functions, which I provide. Special cases of this decomposition are well-known and widely used in meteorology. Besides using the decomposition in this new theoretical way, after having introduced it and the underlying properties in a proper theoretical framework, accompanied by an illustrative example, I also employ it in its classical sense as a forecast evaluation method as the meteorologists do: As such, it unveils the driving forces behind forecast errors and complements classical forecast evaluation methods. I discuss estimation of the decomposition via kernel regression and then apply it to popular economic forecasts. Analysis of mean forecasts from the US Survey of Professional Forecasters and quantile forecasts derived from Bank of England fan charts indeed yield interesting new insights and highlight the potential of the method. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.01835&r=all |
| By: | Gaetano Perone |
| Abstract: | Coronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic's inflection point and final size. |
| Keywords: | COVID-2019; infection disease; pandemic; time series; ARIMA model; forecasting models; |
| JEL: | C22 C53 I18 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:yor:hectdg:20/07&r=all |
| By: | Manuel Nunes; Enrico Gerding; Frank McGroarty; Mahesan Niranjan |
| Abstract: | Modern decision-making in fixed income asset management benefits from intelligent systems, which involve the use of state-of-the-art machine learning models and appropriate methodologies. We conduct the first study of bond yield forecasting using long short-term memory (LSTM) networks, validating its potential and identifying its memory advantage. Specifically, we model the 10-year bond yield using univariate LSTMs with three input sequences and five forecasting horizons. We compare those with multilayer perceptrons (MLP), univariate and with the most relevant features. To demystify the notion of black box associated with LSTMs, we conduct the first internal study of the model. To this end, we calculate the LSTM signals through time, at selected locations in the memory cell, using sequence-to-sequence architectures, uni and multivariate. We then proceed to explain the states' signals using exogenous information, for what we develop the LSTM-LagLasso methodology. The results show that the univariate LSTM model with additional memory is capable of achieving similar results as the multivariate MLP using macroeconomic and market information. Furthermore, shorter forecasting horizons require smaller input sequences and vice-versa. The most remarkable property found consistently in the LSTM signals, is the activation/deactivation of units through time, and the specialisation of units by yield range or feature. Those signals are complex but can be explained by exogenous variables. Additionally, some of the relevant features identified via LSTM-LagLasso are not commonly used in forecasting models. In conclusion, our work validates the potential of LSTMs and methodologies for bonds, providing additional tools for financial practitioners. |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2005.02217&r=all |
| By: | Sidra Mehtab; Jaydip Sen |
| Abstract: | Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. Design of such predictive models requires choice of appropriate variables, right transformation methods of the variables, and tuning of the parameters of the models. In this work, we present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. The granular data is aggregated into three slots in a day, and the aggregated data is used for building and training the forecasting models. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data. We build eight classification and eight regression models based on statistical and machine learning approaches. In addition to these models, a deep learning regression model using a long-and-short-term memory (LSTM) network is also built. Extensive results have been presented on the performance of these models, and the results are critically analyzed. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.11697&r=all |
| By: | Theresa Kuchler; Dominic Russel; Johannes Stroebel |
| Abstract: | We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 “hotspots” (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19. |
| Keywords: | social connectedness, COVID-19, coronavirus, communicable disease |
| JEL: | C60 I10 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8241&r=all |
| By: | Bakens, Jessie (RS: GSBE other - not theme-related research, ROA / Dynamics of the labour market); Fouarge, Didier (RS: GSBE Theme Learning and Work, RS: GSBE Theme Data-Driven Decision-Making, ROA / Dynamics of the labour market); Goedhart, Rogier (ROA / Human capital in the region, RS: GSBE other - not theme-related research) |
| Abstract: | As part of the Education and Labour Market Project (POA)1, the Research Centre for Education and the Labour Market (ROA) develops a number of research activities aimed at a better understanding of the medium-term developments in supply and demand on the Dutch labour market. These activities include analyses of the match between skills supply and demand, the development of labour market indicators for the current equilibrium between supply and demand, and labour market forecasts of supply and demand by industry, occupation, education, and region. The indicators for the current state of the labour market as well as the medium-term forecasts are gathered in an online database: the Labour Market Information System (AIS).2 This database is updated on a yearly basis. The POA project was initiated by ROA in 1986 to increase the transparency of the labour market for youngsters in order for them to make better informed decisions on their education. |
| Date: | 2020–05–06 |
| URL: | http://d.repec.org/n?u=RePEc:unm:umarot:2020002&r=all |