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on Forecasting |
| By: | M. Hashem Pesaran; Andreas Pick; Allan Timmermann |
| Abstract: | We develop novel forecasting methods for panel data with heterogeneous parameters and examine them together with existing approaches. We conduct a systematic comparison of their predictive accuracy in settings with different cross-sectional (N) and time (T) dimensions and varying degrees of parameter heterogeneity. We investigate conditions under which panel forecasting methods can perform better than forecasts based on individual estimates and demonstrate how gains in predictive accuracy depend on the degree of parameter heterogeneity, whether heterogeneity is correlated with the regressors, the goodness of fit of the model, and, particularly, the time dimension of the data set. We propose optimal combination weights for forecasts based on pooled and individual estimates and develop a novel forecast poolability test that can be used as a pretesting tool. Through a set of Monte Carlo simulations and three empirical applications to house prices, CPI inflation, and stock returns, we show that no single forecasting approach dominates uniformly. However, forecast combination and shrinkage methods provide better overall forecasting performance and offer more attractive risk profiles compared to individual, pooled, and random effects methods. |
| Keywords: | forecasting, panel data, heterogeneity, forecast evaluation, forecast combination, shrinkage, pooling |
| JEL: | C33 C53 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9690&r= |
| By: | Li Li; Yanfei Kang; Fotios Petropoulos; Feng Li |
| Abstract: | Intermittent demand forecasting is a ubiquitous and challenging problem in operations and supply chain management. There has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives in recent years. However, limited attention has been given to forecast combination methods, which have been proved to achieve competitive performance in forecasting fast-moving time series. The current study aims to examine the empirical outcomes of some existing forecast combination methods, and propose a generalized feature-based framework for intermittent demand forecasting. We conduct a simulation study to perform a large-scale comparison of a series of combination methods based on an intermittent demand classification scheme. Further, a real data set is used to investigate the forecasting performance and offer insights with regards the inventory performance of the proposed framework by considering some complementary error measures. The proposed framework leads to a significant improvement in forecast accuracy and offers the potential of flexibility and interpretability in inventory control. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.08283&r= |
| By: | Souhir Ben Amor; Heni Boubaker; Lotfi Belkacem |
| Abstract: | In this paper, dual generalized long memory modelling has been proposed to predict the electricity spot price. First, we focus on modelling the conditional mean of the series so we adopt a generalized fractional k-factor Gegenbauer process ( k-factor GARMA). Secondly, the residual from the k-factor GARMA model has been used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using two different learning algorithms, so we estimate the hybrid k- factor GARMA-LLWNN based backpropagation (BP) algorithm and based particle swarm optimization (PSO) algorithm. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has been adopted, and the parameters of the k-factor GARMAG- GARCH model have been estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. To illustrate the usefulness of our methodology, we carry out an empirical application using the hourly returns of electricity prices from the Nord Pool market. The empirical results have shown that the k-factor GARMA-G-GARCH model has the best prediction accuracy in terms of forecasting criteria, and find that this is more appropriate for forecasts. |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2204.08289&r= |
| By: | Christina Anderl; Guglielmo Maria Caporale |
| Abstract: | This paper investigates the predictive power of the shadow rate for the inflation rate in countries with a zero lower bound (the US, the UK and Canada) and in those with negative rates (Japan, the Euro Area and Switzerland). Using shadow rates obtained from two different models (the Wu-Xia (2016) and the Krippner (2015a) ones) and for different lower bound parameters we compare the out-of-sample forecasting performance of an inflation model including a shadow rate interaction term with a benchmark one excluding it. Both specifications are estimated by OLS (Ordinary Least Squares) and includes a range of macroeconomic factors computed by means of principal component analysis. Both point and density forecasts of the inflation rate are evaluated. The models including the shadow rate interaction term are found to outperform the benchmark ones according to both sets of criteria except in countries operating an official inflation targeting regime. The presence or absence of a zero lower bound affects which type of shadow rate produces more accurate inflation forecasts. |
| Keywords: | shadow interest rates, zero lower bound, inflation forecasting, density forecasts |
| JEL: | C38 C53 E37 E43 E58 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9687&r= |
| By: | Antonio Musa (Central Bank of Bosnia and Herzegovina) |
| Abstract: | The aim of this paper is to evaluate current quarterly nowcasts of the gross domestic product in Bosnia and Herzegovina based on the flow of available monthly economic indicators that are available during the same quarter. The nowcasting performance indicates that it is worthwhile to include a broad group of forecasting models based on the different methodologies. In addition to the models, the choice of the variables and measurement of the loss function in evaluating nowcasting performance are the core of nowcasting. In a time marked by pandemic of corona virus and war in Ukraine, nowcasting models have more profound role than more structural models. The high variance of the specific nowcasting model influences the use of the results of combinations of many models. Using a comprehensive method for preselection of variables and by using the other combination methods, the forecasting errors are lower, even in times of high uncertainty. |
| Keywords: | Nowcasting; short-term forecasting; uncertainty; pandemic |
| JEL: | E17 E66 C52 C55 O11 |
| Date: | 2022–04–26 |
| URL: | http://d.repec.org/n?u=RePEc:gii:giihei:heidwp08-2022&r= |
| By: | Duan, Fang |
| Abstract: | Correlation models, such as Constant Conditional Correlation (CCC) GARCH model or Dynamic Conditional Correlation (DCC) GARCH model, play a crucial role in forecasting Value-at-Risk (VaR) or Expected Shortfall (ES). The additional inclusion of constant correlation tests into correlation models has been proven to be helpful in terms of the improvement of the accuracy of VaR or ES forecasts. Galeano & Wied (2017) suggested an algorithms for detecting structural breaks in the correlation matrix whereas Duan & Wied (2018) proposed a residual based testing procedure for constant correlation matrix which allows for time-varying marginal variances. In this chapter, we demonstrate the application of aforementioned correlation testing procedures and compare its performance in backtesting VaR and ES predictions. Portfolios in consideration are constructed from four stock indices DAX30, STOXX50, FTSE100 and S&P500. |
| Keywords: | structural break tests,correlation model,value-at-risk,expected shortfall |
| JEL: | C12 C32 C53 C58 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:945&r= |
| By: | Sabyasachi Kar; Amaani Bashir; Mayank Jain (Institute of Economic Growth, Delhi) |
| Abstract: | The use of big data and machine learning techniques is now very common in many spheres and there is growing popularity of these approaches in macroeconomic forecasting as well. Is big data and machine learning really useful in the prediction of macroeconomic outcomes? Are they superior in performance compared to their traditional counterparts? What are the tradeoffs that forecasters need to keep in mind, and what are the steps they need to take to use these resources effectively? We carry out a critical analysis of the existing literature in order to answer these questions. Our analysis suggests that the answer to most of these questions are nuanced, conditional on a number of factors identified in the study. |
| Keywords: | Forecasting, Big Data, Machine Learning, Supervised Learning, Meta-analysis, Growth, Inflation |
| JEL: | C14 C45 C52 C53 C55 E17 E37 |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:awe:wpaper:446&r= |
| By: | François Facchini (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | This article is an update and extension of the electoral forecasting model of Lafay, Facchini and Auberger (2007) for the French presidential elections of 2022. Lafay and al. argued that the Socialist Party's rating was a good way to predict the vote split in the second round of elections between the left and the right. Socialist Pary's rating, nonetheless, cannot explain Emmanuel Macron's victory in the 2017 elections. This does not mean that party ratings are not a good predictor of the 2022 elections, if a number of adjustments are made. Based on party ratings the indicators proposed in this article argue that the scores in the first round of the April 2022 elections should be as follows: 30.5% for Emmanuel Macron, 22.7% for Valérie Pécresse (all the candidates of right wing), 18,7% for Marine Le Pen and 24.7% for the left and far left. The second round Macron - Pécresse is favorable to Emmanuel Macron, but depends fundamentally on the vote transfers between the left and the outgoing President. If the left abstains and Marine Le Pen's election rallies to the candidate of the right (LR), then Valérie Pécresse can win with a score of 51% against 49%. |
| Date: | 2022–03–23 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03624729&r= |