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on Forecasting |
| By: | Dean Croushore; Stephanie M. Wilshusen |
| Abstract: | This paper considers whether the inclusion of information contained in consumer credit reports might improve the predictive accuracy of forecasting models for consumption spending. To investigate the usefulness of aggregate consumer credit information in forecasting consumption spending, this paper sets up a baseline forecasting model. Based on this model, a simulated real-time, out-of-sample exercise is conducted to forecast one-quarter ahead consumption spending. The exercise is run again after the addition of credit bureau variables to the model. Finally, a comparison is made to test whether the model using credit bureau data produces lower or higher root-mean-squared-forecast errors than the baseline model. Key features of the analysis include the use of real-time data, out-of-sample forecast tests, a strong parsimonious benchmark model, and data that span more than two business cycles. Our analysis reveals evidence that some credit bureau variables may be useful in improving forecasts of consumption spending in certain subperiods and for some categories of consumption spending, especially for services. Also, the use of credit bureau variables sometimes makes the forecasts significantly worse by adding noise into the forecasting models. |
| Keywords: | consumption spending; real-time data; consumer credit information; forecasting |
| JEL: | C53 C55 D12 D14 E27 |
| Date: | 2020–06–04 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedpwp:88121&r=all |
| By: | Degiannakis, Stavros; Filis, George |
| Abstract: | Despite the arguments that are put forward by the literature that oil price forecasts are economically useful, such claim has not been tested to date. In this study we evaluate the economic usefulness of oil price forecasts by means of conditional forecasting of three core macroeconomic indicators that policy makers are predicting, using assumptions about the future path of the oil prices. The chosen indicators are the core inflation rate, industrial production and purchasing price index. We further consider two more indicators, namely inflation expectation and monetary policy uncertainty. To do so, we initially forecast oil prices using a MIDAS framework and subsequently we use regression-based models for our conditional forecasts. Overall, there is diminishing importance of oil price forecasts for macroeconomic projections and policy formulation. An array of arguments is presented as to why this might be the case, which relate to the improved energy efficiency, the contemporary monetary policy tools and the financialisation of the oil market. Our findings remain robust to alternative oil price forecasting frameworks. |
| Keywords: | Conditional forecasting; oil price forecasts; MIDAS; core inflation; inflation expectations |
| JEL: | C53 E27 E37 Q47 |
| Date: | 2020–05–27 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:100705&r=all |
| By: | Jennifer L. Castle (Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Magdalen College, University of Oxford); Jurgen A. Doornik (Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Climate Econometrics, Nuffield College, University of Oxford); David F. Hendry (Dept of Economics, Institute for New Economic Thinking at the Oxford Martin School and Climate Econometrics, Nuffield College, University of Oxford) |
| Abstract: | We have been publishing real-time forecasts of confirmed cases and deaths for COVID-19 online at www.doornik.com/COVID-19 from mid-March 2020. These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative of short term developments, without requiring other assumptions of how the SARS-CoV-2 virus is spreading, or whether preventative policies are effective. As such they are complementary to forecasts from epidemiological models. The forecasts are based on extracting trends from windows of the data, applying machine learning, and then computing forecasts by applying some constraints to this flexible extracted trend. The methods have previously been applied to various other time series data and have performed well. They are also effective in this setting, providing better forecasts than some epidemiological models. |
| Keywords: | Autometrics; Cardt; COVID-19; Epidemiology; Forecasting; Forecast averaging; Machine learning; Smoothing; Trend Indicator Saturation. |
| Date: | 2020–04–27 |
| URL: | http://d.repec.org/n?u=RePEc:nuf:econwp:2006&r=all |
| By: | Malo Huard (LMO - Laboratoire de Mathématiques d'Orsay - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, Milvue); Rémy Garnier (Cdiscount); Gilles Stoltz (LMO - Laboratoire de Mathématiques d'Orsay - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique, HEC Paris - Ecole des Hautes Etudes Commerciales, CELESTE - Statistique mathématique et apprentissage - Inria Saclay - Ile de France - Inria - Institut National de Recherche en Informatique et en Automatique - LMO - Laboratoire de Mathématiques d'Orsay - Université Paris-Saclay - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We revisit the interest of classical statistical techniques for sales forecasting like exponential smoothing and extensions thereof (as Holt's linear trend method). We do so by considering ensemble forecasts, given by several instances of these classical techniques tuned with different (sets of) parameters, and by forming convex combinations of the elements of ensemble forecasts over time, in a robust and sequential manner. The machine-learning theory behind this is called "robust online aggregation", or "prediction with expert advice", or "prediction of individual sequences" (see Cesa-Bianchi and Lugosi, 2006). We apply this methodology to a hierarchical data set of sales provided by the e-commerce company Cdiscount and output forecasts at the levels of subsubfamilies, subfamilies and families of items sold, for various forecasting horizons (up to 6-week-ahead). The performance achieved is better than what would be obtained by optimally tuning the classical techniques on a train set and using their forecasts on the test set. The performance is also good from an intrinsic point of view (in terms of mean absolute percentage of error). While getting these better forecasts of sales at the levels of subsubfamilies, subfamilies and families is interesting per se, we also suggest to use them as additional features when forecasting demand at the item level. |
| Keywords: | prediction with expert advice,ensemble forecasts,exponential smoothing,Holt's linear trend method,e-commerce data |
| Date: | 2020–06–05 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-02794320&r=all |
| By: | Claudia Foroni; Massimiliano Marcellino; Dalibor Stevanovic |
| Abstract: | We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track by a specific form of intercept correction. Among all these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and following recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment and very persistent negative effects on trend growth. Similar findings emerge also for the other G7 countries. |
| Keywords: | COVID-19,Mixed-Frequency,Forecasting,GDP, |
| Date: | 2020–06–10 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2020s-32&r=all |
| By: | Baumeister, Christiane; Korobilis, Dimitris; Lee, Thomas K. |
| Abstract: | This paper evaluates alternative indicators of global economic activity and other market fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of the most useful indicators that has been proposed in the literature. However, by combining measures from a number of different sources we can do even better. Our analysis results in a new index of global economic conditions and new measures for assessing future tightness of energy demand and expected oil price pressures. |
| Keywords: | Energy demand; Forecasting; oil price pressures; petroleum consumption; state of the world economy; stochastic volatility |
| JEL: | C11 C32 C52 Q41 Q47 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14580&r=all |