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on Forecasting |
| By: | Hauber, Philipp |
| Abstract: | This paper evaluates forecasts from a factor model estimated with a large real-time dataset of the German economy. The evaluation focuses on a broad cross-section of variables such as activity series including components of the gross domestic product and gross value added, deflators and other price measures as well as several labor market indicators. In addition to unconditional forecasts for these variables, we also investigate to what extent the forecast accuracy improves when we condition on professional forecasters' view on GDP growth and CPI inflation. We find that over the period from 2006 to 2017 the model's unconditional forecasts are broadly in line with autoregressive benchmarks for the majority of the 37 series that we focus on in the evaluation, in some cases performing somewhat better and in others somewhat worse. For a few variables capturing real activity and some price indicators, however, we find large gains in predictive accuracy that persist for forecast horizons of up to two quarters ahead. Conditioning on external information tends to improve the forecast accuracy in some instances but typically only for those series where the unconditional forecasts are already quite accurate. For around a third of the variables under consideration, the differences in forecast accuracy between conditional and unconditional forecasts are statistically significant for density forecasts; for point forecasts on the other hand we find no significant differences. From a methodological point of view, this paper proposes precision-based sampling algorithms to draw from the predictive density - unconditional or conditional on a subset of the system variables - in factor models and other models with unobserved components. Simulations show that these algorithms perform favorably compared to Kalman filter-based alternatives typically used in the literature. |
| Keywords: | factor models,conditional forecasting,precision-based sampling |
| JEL: | C11 C53 C55 E37 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:251469&r= |
| By: | Mehmet Balcilar (Department of Economics, Eastern Mediterranean University, Turkish Republic of North Cyprus, Via Mersin 10, Famagusta 99628, Turkey; Department of Economics, OSTIM Technical University, Ankara 06374, Turkey); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Christian Pierdzioch (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany) |
| Abstract: | We investigate whether oil-price uncertainty helps in forecasting international stock returns of ten advanced and emerging countries. We consider an out-of-sample period of 1925:08 to 2021:09, with an in-sample period 1920:08-1925:07, and employ a quantile-predictiveregression approach, which is more informative relative to a linear model, as it investigates the ability of oil-price uncertainty to forecast the entire conditional distribution of stock returns, rather than only its conditional-mean. A quantile-based approach accounts for nonlinearity (including regime changes), non-normality, and outliers. Based on a recursive estimation scheme, we draw the following main conclusions: the quantile-predictiveregression approach using oil-price uncertainty as a predictor statistically outperforms the corresponding quantile-based constant-mean model for all ten countries at certain quantiles (capturing normal, bear, and bull markets), and over specific forecast horizons, compared to forecastability being detected for eight countries under the linear predictive model. Moreover, we detect forecasting gains in many more horizons (at particular quantiles) compared to the linear case. In addition, an oil-price uncertainty-based state-contingent spillover analysis reveals that the ten equity markets are tighter connected during the upper regime, suggesting that heightened oil-market volatility erodes the benefits from diversification across equity markets. |
| Keywords: | international stock markets, oil price uncertainty, forecasting, quantile regression |
| JEL: | C22 C53 G15 Q41 |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:202217&r= |
| By: | Easaw, Joshy (Cardiff Business School); Golinelli, Roberto (Department of Economics, University of Bologna, ITALY); Heravi, Saeed (Cardiff Business School) |
| Abstract: | The purpose of this paper is to investigate the nature of professionals’ inflation forecasts inattentiveness. We introduce and empirically investigate a new generalized model of inattentiveness due to informational rigidity. In doing so, we outline a novel model that considers the non-linear relationship between inattentiveness and aggregate uncertainty, which crucially distinguishes between macro-economic and data (measurement error) uncertainty. The empirical analysis uses the Survey of Professional Forecasters data and indicates that inattentiveness due to imperfect information explains professional forecasts’ dynamics. |
| Keywords: | Forecasting Popular Votes Shares; Electoral Poll; Forecast combination, Hybrid model; Support Vector Machine |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:cdf:wpaper:2022/7&r= |
| By: | Youssef Ulgazi; Paul Vertier |
| Abstract: | In this paper, we present an updated version of the reference model used at Banque de France to forecast inflation: MAPI (Model for Analysis and Projection of Inflation). While the conceptual framework of the model remains very close to its initial version, our update takes stock of three different factors. First, since the previous version of the model, the underlying nomenclature used at the European level (ECOICOP) to define some of the main aggregates was changed, therefore requiring a careful review of the relevance of initial equations. Second, in the context of the modification in 2019 of the main semi-structural macroeconomic model used for the macroeconomic projections at Banque de France (FR-BDF), it aims at harmonizing the iterations between MAPI and FR-BDF. Finally, large variations in the wage variables in the midst of the sanitary measures related to the Covid-19 pandemics pushed us to use different concepts of wage and compensation variables. At the crossroads of these considerations, we update the model extending the estimation window, correcting specifications and input variables whenever relevant. The resulting model is an up-to-date, simplified and more parsimonious version of the initial model, entailing a stronger harmonization with the central macroeconomic model FR-BDF. It still involves significant pass-through of wages, oil and exchange rate to HICP. |
| Keywords: | Forecasting, Inflation, Time Series |
| JEL: | E37 C32 E31 C53 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:bfr:banfra:869&r= |
| By: | Michael V. Klibanov; Aleksander A. Shananin; Kirill V. Golubnichiy; Sergey M. Kravchenko |
| Abstract: | In the previous paper (Inverse Problems, 32, 015010, 2016), a new heuristic mathematical model was proposed for accurate forecasting of prices of stock options for 1-2 trading days ahead of the present one. This new technique uses the Black-Scholes equation supplied by new intervals for the underlying stock and new initial and boundary conditions for option prices. The Black-Scholes equation was solved in the positive direction of the time variable, This ill-posed initial boundary value problem was solved by the so-called Quasi-Reversibility Method (QRM). This approach with an added trading strategy was tested on the market data for 368 stock options and good forecasting results were demonstrated. In the current paper, we use the geometric Brownian motion to provide an explanation of that effectivity using computationally simulated data for European call options. We also provide a convergence analysis for QRM. The key tool of that analysis is a Carleman estimate. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.07174&r= |
| By: | Wheatcroft, Edward |
| Abstract: | A scoring rule is a function of a probabilistic forecast and a corresponding outcome used to evaluate forecast performance. There is some debate as to which scoring rules are most appropriate for evaluating forecasts of sporting events. This paper focuses on forecasts of the outcomes of football matches. The ranked probability score (RPS) is often recommended since it is 'sensitive to distance', that is it takes into account the ordering in the outcomes (a home win is 'closer' to a draw than it is to an away win). In this paper, this reasoning is disputed on the basis that it adds nothing in terms of the usual aims of using scoring rules. A local scoring rule is one that only takes the probability placed on the outcome into consideration. Two simulation experiments are carried out to compare the performance of the RPS, which is non-local and sensitive to distance, the Brier score, which is non-local and insensitive to distance, and the Ignorance score, which is local and insensitive to distance. The Ignorance score outperforms both the RPS and the Brier score, casting doubt on the value of non-locality and sensitivity to distance as properties of scoring rules in this context. |
| Keywords: | football forecasting; forecast evaluation; ignorance score; ranked probability score; scoring rules |
| JEL: | C1 |
| Date: | 2021–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:111494&r= |
| By: | Hauber, Philipp |
| Abstract: | Factor models feature prominently in the macroeconomic nowcasting literature, yet no clear consensus has emerged regarding the question of how many and which variables to select in such applications. Examples of both large-scale models, estimated with data sets consisting of over 100 time series as well as small-scale models based on only a few, pre-selected variables can be found in the literature. To adress the issue of variable selection in factor models, in this paper we employ sparse priors on the loadings matrix. These priors concentrate more mass at zero than those conventionally used in the literature while retaining fat tails to capture signals. As a result, variable selection and estimation can be performed simultaneously in a Bayesian framework. Using large data sets consisting of over 100 variables, we evaluate the performance of sparse factor models in real-time for US and German GDP point and density nowcasts. We find that sparse priors lead to relatively small gains in nowcast accuracy compared to a benchmark Normal prior. Moreover, different types of sparse priors discussed in the literature yield very similar results. Our findings are compatible with the hypothesis that large macroeconomic data sets typically used in now- or forecasting applications are not sparse but dense. |
| Keywords: | factor models,sparsity,nowcasting,variable selection |
| JEL: | C11 C53 C55 E37 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:251551&r= |
| By: | Jeyhun Mikayilov; Ryan Alyamani; Abdulelah Darandary; Muhammad Javid; Fakhri Hasanov; Saleh T. AlTurki; Rey B. Arnaiz (King Abdullah Petroleum Studies and Research Center) |
| Abstract: | The objective of this study is to investigate Saudi Arabia’s industrial electricity consumption at the regional level. We apply structural time series modeling to annual data over the period of 1990 to 2019. In addition to estimating the size and significance of the price and income elasticities for regional industrial electricity demand, this study projects regional industrial electricity demand up to 2030. This is done using estimated equations and assuming different future values for price and income. The results show that the long-run income and price elasticities of industrial electricity demand vary across regions. The underlying energy demand trend analysis indicates some efficiency improvements in industrial electricity consumption patterns in all regions. |
| Keywords: | Electricity consumption, Electricity demand, Economic Modeling |
| Date: | 2022–01–13 |
| URL: | http://d.repec.org/n?u=RePEc:prc:dpaper:ks--2021-dp19&r= |