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on Forecasting |
| By: | Eraslan, Sercan; Nöller, Marvin |
| Abstract: | We follow the idea of exploiting cross-sectional information to improve recession probability forecasts by aggregating indicator-specific turning point predictions to obtain economy-wide recession probabilities. This stands in contrast to most of the relevant literature, which relies on an aggregated economic indicator to identify business cycle turning points. Using smooth transition regressions we compare the forecast performance of both approaches to business cycle dating in a comprehensive real-time forecasting exercise for recessions in the US. Moreover, we propose a novel smooth transition modelling framework which makes use of the interrelation between business and growth cycles to forecast recession probabilities. Our real-time out-of-sample forecast evaluation reveals that (i) using cross-sectional information is benficial to predicting recession probabilities, (ii) aggregating indicator-specific turning point forecasts clearly outperforms turning point predictions based on a single indicator and (iii) the proposed smooth transition framework is able to provide informative recession probability forecasts for up to three months in the US. |
| Keywords: | Business cycles,forecasting,recessions,STAR models,turning points |
| JEL: | C24 C53 E37 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bubdps:082020&r=all |
| By: | Robert Lehmann; Magnus Reif |
| Abstract: | This analysis investigates the predictive power of the most important leading indicators for the German economy, which are provided by the ifo Institute and IHS Markit. We conduct an out-of-sample, real-time forecast experiment for growth of gross domestic product and growth of gross value added in both the manufacturing and the service sector. We find that both survey providers produce valuable leading indicators to predict the current quarter of German GDP growth. Regarding forecasts for the next quarter, the ifo indicators are slightly better than the IHS Markit headline index. For the manufacturing sector, series provided by ifo are clearly superior to those of IHS Markit. For the service sector, the ifo indicators produce better nowcasts, whereas the indicators by IHS are more valuable for one-quarter-ahead predictions. |
| Keywords: | forecasting nowcasting, survey data, ifo Business Climate, PMI |
| JEL: | E17 E27 E37 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8145&r=all |
| By: | Barbara Rossi |
| Abstract: | This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007-2008, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth and inflation. In the context of unstable environments, I discuss how to assess models' forecasting ability; how to robustify models' estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models' parameters are neither necessary nor sufficient to generate time variation in models' forecasting performance: thus, one should not test for breaks in models' parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models' forecasting performance are more appropriate than traditional, average measures. |
| Keywords: | forecasting, instabilities, time variation, inflation, structural breaks, density forecasts, great recession, forecast confidence intervals, output growth, Business cycles |
| JEL: | E4 E52 E21 H31 I3 D1 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:bge:wpaper:1162&r=all |
| By: | Kohei Maehashi (School of Engineering, The University of Tokyo); Mototsugu Shintani (Faculty of Economics, The University of Tokyo) |
| Abstract: | We perform a thorough comparative analysis of factor models and machine learningto forecast Japanese macroeconomic time series. Our main results can be summarizedas follows. First, factor models and machine learning perform better than the con-ventional AR model in many cases. Second, predictions made by machine learningmethods perform particularly well for medium to long forecast horizons. Third, thesuccess of machine learning mainly comes from the nonlinearity and interaction ofvariables, suggesting the importance of nonlinear structure in predicting the Japanesemacroeconomic series. Fourth, while neural networks are helpful in forecasting, simplyadding many hidden layers does not necessarily enhance its forecast accuracy. Fifth,the composite forecast of factor models and machine learning performs better thanfactor models or machine learning alone, and machine learning methods applied toprincipal components are found to be useful in the composite forecast. |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2020cf1146&r=all |
| By: | Felix Haase; Matthias Neuenkirch |
| Abstract: | Stock market recessions are often early warning signals for financial or economic crises. Hence, forecasting bear markets is important for investors, policymakers, and economic agents in general. In our two-step procedure, we first identify stock market regimes in the US using three different techniques (Markov-switching models, dating rules, and a naïve moving average). Second, we predict recessions in the S&P 500 with the help of several modeling approaches, utilizing the information of 92 macro-financial variables. Our results suggest that several variables are suitable for forecasting recessions in stock markets in-sample and out-of-sample. Our early warning models for the US equity market, in particular those using principal components to aggregate the information in the macro-financial variables, provide a statistical improvement over several benchmarks. In addition, these generate economic value by boosting returns, improving the sharp ratio and the omega, and substantially reducing drawdowns. |
| Keywords: | Dating Algorithms, Markov-Switching Models, Predictions, Principal Component Analysis, Specific-to-General Approach, Stock Market Recessions |
| JEL: | C53 G11 G17 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:trr:wpaper:202001&r=all |
| By: | Yuan, Huiling; Zhou, Yong; Zhang, Zhiyuan; Cui, Xiangyu |
| Abstract: | Low-frequency historical data, high-frequency historical data and option data are three major sources, which can be used to forecast the underlying security's volatility. In this paper, we propose two econometric models, which integrate three information sources. In GARCH-It\^{o}-OI model, we assume that the option-implied volatility can influence the security's future volatility, and the option-implied volatility is treated as an observable exogenous variable. In GARCH-It\^{o}-IV model, we assume that the option-implied volatility can not influence the security's volatility directly, and the relationship between the option-implied volatility and the security's volatility is constructed to extract useful information of the underlying security. After providing the quasi-maximum likelihood estimators for the parameters and establishing their asymptotic properties, we also conduct a series of simulation analysis and empirical analysis to compare the proposed models with other popular models in the literature. We find that when the sampling interval of the high-frequency data is 5 minutes, the GARCH-It\^{o}-OI model and GARCH-It\^{o}-IV model has better forecasting performance than other models. |
| Date: | 2020–03–27 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:vdsqf&r=all |
| By: | Gao, Shen (Center for Economics, Finance and Management Studies, Hunan University, China.); Hou, Chenghan (Center for Economics, Finance and Management Studies, Hunan University, China.); Nguyen, Bao H. (Tasmanian School of Business & Economics, University of Tasmania) |
| Abstract: | The growing disintegration between the natural gas and oil prices, together with shale revolution and market financialization, lead to continued fundamental changes in the natural gas markets. To capture these structural changes, this paper considers a wide set of highly flexible time-varying parameter models to evaluate the out-of-sample forecasting performance of the natural gas spot prices across the US, European and Japanese markets. The results show that for both Japan and EU markets, the best forecasting performance is found when the model allows for drastic changes in the conditional mean and gradual changes in the conditional volatility. For the US market, however, no model performs systematically better than the simple autoregressive model. Full sample estimation results further con- firm that allowing t-distributed error is important in modelling the natural gas prices, especially for EU markets. |
| Keywords: | natural gas price; structural breaks; forecasting; time-varying pa- rameter; Markov switching; stochastic volatility. |
| JEL: | C32 E32 Q43 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:tas:wpaper:32412&r=all |
| By: | Yuan, Huiling; Zhou, Yong; Xu, Lu; Sun, Yulei; Cui, Xiangyu |
| Abstract: | Volatility asymmetry is a hot topic in high-frequency financial market. In this paper, we propose a new econometric model, which could describe volatility asymmetry based on high-frequency historical data and low-frequency historical data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed methodologies. And an empirical application is demonstrated that the new model has stronger volatility prediction power than GARCH-It\^{o} model in the literature. |
| Date: | 2020–03–27 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:hkzdr&r=all |
| By: | Alexandros Botsis; Christoph Görtz; Plutarchos Sakellaris |
| Abstract: | Using a novel dataset that contains qualitative firm survey data on sales forecasts as well as balance-sheet data on realized sales, we document that only major forecast errors are predictable and display autocorrelation. This result is a particular violation of the Full Information Rational Expectations hypothesis that requires explanation. In contrast, minor forecast errors are neither predictable nor autocorrelated. To arrive at this result, we develop a novel methodology to quantify qualitative survey data on firm forecasts. It is generally applicable when quantitative information, e.g. from balance sheets, is available on the realization of the forecasted variable. Finally, we provide a model of rational inattention that explains our empirical results. Firms optimally limit their degree of attention to information when operating in market environments where information processing is more costly. This results in major forecast errors that are predictable and autocorrelated. |
| Keywords: | survey data, firm data, expectations, forecast errors, rational inattention |
| JEL: | C53 C83 D22 D84 E32 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8148&r=all |