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on Forecasting |
| By: | Christopher Kath; Florian Ziel |
| Abstract: | We propose a multivariate elastic net regression forecast model for German quarter-hourly electricity spot markets. While the literature is diverse on day-ahead prediction approaches, both the intraday continuous and intraday call-auction prices have not been studied intensively with a clear focus on predictive power. Besides electricity price forecasting, we check for the impact of early day-ahead (DA) EXAA prices on intraday forecasts. Another novelty of this paper is the complementary discussion of economic benefits. A precise estimation is worthless if it cannot be utilized. We elaborate possible trading decisions based upon our forecasting scheme and analyze their monetary effects. We find that even simple electricity trading strategies can lead to substantial economic impact if combined with a decent forecasting technique. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.08604&r=for |
| By: | Carl Singleton (University of Reading); J. James Reade (University of Reading); Alsdair Brown (University of East Anglia) |
| Abstract: | This paper studies 150 individuals who each chose to forecast the outcome of 380 fixed events, namely all football matches during the 2017/18 season of the English Premier League. The focus is on whether revisions to these forecasts before the matches began improved the likelihood of predicting correct scorelines and results. Against what theory might expect, we show how these revisions tended towards significantly worse forecasting performance, suggesting that individuals should have stuck with their initial judgements, or their ‘gut instincts’. This result is robust to both differences in the average forecasting ability of individuals and the predictability of matches. We find evidence this is because revisions to the forecast number of goals scored in football matches are generally excessive, especially when these forecasts were increased rather than decreased. |
| Keywords: | Sports forecasting, Fixed-event forecasts, Judgement revision |
| JEL: | C53 C23 D84 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2018-006&r=for |
| By: | Havranek, Tomas; Zeynalov, Ayaz |
| Abstract: | In this paper, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using Mixed-data sampling (MIDAS). Our results stress the potential of Google Trends to offer more accurate prediction in the context of tourism: we find that Google Trends information, both two months and one week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model that employs weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data. |
| Keywords: | Google trends, mixed-frequency data, forecasting, tourism |
| JEL: | C53 L83 |
| Date: | 2018–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:90205&r=for |
| By: | Gabriel Mathy (American University); Christian Roatta (The London School of Economics and Political Science) |
| Abstract: | Economic analysts were not able to forecast the Great Contraction of the early 1930s, as previous work has shown (Goldfarb et al., 2005; Mathy and Stekler, 2016). In 1937, with full recovery from the Depression still incomplete, another severe recession struck which lasted about a year. We use a well-established method to convert qualitative forecasts into quantitative scores and use these scores to study forecaster performance in this period. For the peak of the business cycle, we find similar results for this recession as for the 1929-1933 recession in that forecasters largely failed to forecast this downturn. Forecasters also remained overoptimistic throughout the recession, seeing a recovery as imminent, and failed to forecast the recession’s trough. We discuss similarities and differences with the performance of business analysts in the 1929-1933 and 1937-1938 recessions, as well as the methods and heuristics used to construct forecasts in this period. |
| Keywords: | Macroeconomic Forecasting, Textual Analysis, Great Depression, 1937-1938 recession, Qualitative/Quantitative analysis |
| JEL: | E37 N12 C53 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2018-004&r=for |
| By: | Heni Boubaker (International University of Rabat, BEAR LAB, Technopolis Rabat-Shore Rocade-Sale, Morocco); Giorgio Canarella (University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada, USA); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Stephen M. Miller (University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Las Vegas, Nevada, USA) |
| Abstract: | We report the results of applying semi-parametric long-memory estimators to the historical monthly series of U.S. inflation, and analyze their empirical forecasting performance over 1, 6, 12, and 24 months using in-sample and out-of-sample procedures. For comparison purposes, we also apply two parametric estimators, the naive AR(1) and the ARFIMA(1, d, 1) models. We evaluate the forecasting accuracy of the competing methods using the mean square error (MSE) and mean absolute error (MAE) criteria. We evaluate the statistical significance of forecasting accuracy of competing forecasts using the Diebold-Mariano (1995) test. Overall, our results preforms slightly better than the Lahiani and Scaillet (2009) threshold estimator based on the MSE and MAE criteria. This improvement in performance does not prove significant enough to cause a rejection of the null hypothesis of equality of predictive accuracy. The Boubaker (2017) estimator, on the other hand, significantly outperforms the time-invariant estimators over longer horizons. Over shorter horizons, however, the Boubaker (2017) estimator does not exhibit a significantly better predictive performance than the time-invariant long-memory estimators with the exception of the naive AR(1) model. |
| Keywords: | long memory, wavelet analysis, time-varying persistence |
| JEL: | C13 C22 C32 C54 E31 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201869&r=for |
| By: | Jacob T. Jones (The George Washington University); Tara M. Sinclair (The George Washington University); Herman O. Stekler (The George Washington University) |
| Abstract: | The Bank of England publishes a quarterly Inflation Report (IR) that provides numerical forecasts and text discussion of their assessment of the UK economy. Previous research has evaluated the quantitative forecasts included in the IR, but we focus on the qualitative discussion of output growth. We use a textual analysis procedure to convert the qualitative assessments made by the Bank into quantitative scores. We compare these scores to real-time output growth data as well as to the corresponding quantitative projections published by the Bank. We find that overall developments in the UK economy were accurately represented in the text of the IR. Although the Bank failed to forecast the onset of the Great Recession ahead of time, they did perceive underlying weakness in the economy prior to the downturn, which was more clearly communicated in the text than in the quantitative forecasts. |
| Keywords: | Macroeconomic Forecast Evaluation, Qualitative Forecasting, Great Recession |
| JEL: | C53 E37 E58 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:gwc:wpaper:2018-005&r=for |
| By: | Hauzenberger, Niko (WU Wirtschaftsuniversität Wien); Huber, Florian (University of Salzburg) |
| Abstract: | In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered. |
| Keywords: | Empirical exchange rate models; exchange rate fundamentals; Markov switching |
| JEL: | C30 E32 E52 F31 |
| Date: | 2018–11–21 |
| URL: | http://d.repec.org/n?u=RePEc:ris:sbgwpe:2018_008&r=for |
| By: | Niko Hauzenberger; Florian Huber |
| Abstract: | In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered. |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1811.08818&r=for |
| By: | Hossein Hassani (The Statistical Research Centre, Bournemouth University, Bournemouth, UK); Mohammad Reza Yeganegi (Department of Accounting, Islamic Azad University Central Tehran Branch, Iran); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, USA.) |
| Abstract: | This paper explores the potential role of economic inequality for forecasting the stock market volatility of the United Kingdom (UK). Utilizing linear and nonlinear models as well as measures of consumption and income inequalities over the period of 1975 to 2016, we find that linear models incorporating the information of growth in inequality indeed produce lower forecast errors. These models, however, do not necessarily outperform the univariate linear and nonlinear models based on formal statistical forecast comparison tests, especially in short- to medium-runs. On the other hand, at a one-year-ahead horizon, absolute measure of consumption inequality results in significant statistical gains for stock market volatility predictions. We argue that the long-run predictive power of consumption inequality is driven by its informational content over both political and social uncertainty in the long-run. |
| Keywords: | Income and Consumption Inequalities, Stock Markets, Realized Volatility, Forecasting, Linear and Nonlinear Models, United Kingdom |
| JEL: | C22 G1 |
| Date: | 2018–11 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:201880&r=for |