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on Forecasting |
| By: | Müller, Karsten |
| Abstract: | Based on German business cycle forecast reports covering 10 German institutions for the period 1993-2017, the paper analyses the information content of German forecasters' narratives for German business cycle forecasts. The paper applies textual analysis to convert qualitative text data into quantitative sentiment indices. First, a sentiment analysis utilizes dictionary methods and text regression methods, using recursive estimation. Next, the paper analyses the different characteristics of sentiments. In a third step, sentiment indices are used to test the efficiency of numerical forecasts. Using 12-month-ahead fixed horizon forecasts, fixed-effects panel regression results suggest some informational content of sentiment indices for growth and inflation forecasts. Finally, a forecasting exercise analyses the predictive power of sentiment indices for GDP growth and inflation. The results suggest weak evidence, at best, for in-sample and out-of-sample predictive power of the sentiment indices. |
| Keywords: | Textual analysis,Sentiment,Macroeconomic forecasting,Forecast evaluation,Germany |
| JEL: | C53 E32 E37 E66 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:pp1859:23&r=all |
| By: | Koop, Gary (University of Strathclyde); McIntyre, Stuart (University of Strathclyde); Mitchell, James (University of Warwick); Poon, Aubrey (University of Strathclyde) |
| Abstract: | High-frequency financial and economic activity indicators are usually time aggregated before forecasts of low-frequency macroeconomic events, such as recessions, are computed. We propose a mixed-frequency modelling alternative that delivers high-frequency probability forecasts (including their confidence bands) for these low-frequency events. The new approach is com- pared with single-frequency alternatives using loss functions adequate to rare event forecasting. We provide evidence that: (i) weekly-sampled spread improves over monthly-sampled to predict NBER recessions, (ii) the predictive content of the spread and the Chicago Fed Financial Condition Index (NFCI) is supplementary to economic activity for one-year-ahead forecasts of contractions, and (iii) a weekly activity index can date the 2020 business cycle peak two months in advance using a mixed-frequency filtering. |
| Keywords: | mixed frequency models ; recession ; financial indicators ; weekly activity index ; event probability forecasting ; |
| JEL: | C25 C53 E32 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkemf:38&r=all |
| By: | Foltas, Alexander; Pierdzioch, Christian |
| Abstract: | We study the efficiency of growth and inflation forecasts published by three leading German economic research institutes during a period of time ranging from 1970 to 2017. To this end, we examine whether the information used by the research institutes when they formed their forecasts helps to explain the ex-post realized forecast errors. We identify the information that the research institutes used to set up their quantitative forecasts by applying computational-linguistics techniques to decompose the business-cycle reports published by the research institutes into various topics. Our results show that several topics have predictive value for the forecast errors. |
| Keywords: | Growth forecasts,Inflation forecasts,Forecast efficiency,Business-cycle reports |
| JEL: | C53 E32 E37 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:pp1859:22&r=all |
| By: | Clements, Michael P. (University of Reading); Galvao, Ana Beatriz (University of Warwick) |
| Abstract: | Macroeconomic data are subject to data revisions as later vintages are released. Yet, the usual way of generating real-time density forecasts from BVAR models makes no allowance for this form of data uncertainty. We evaluate two methods that consider data uncertainty when forecasting with BVAR models with/without stochastic volatility. First, the BVAR forecasting model is estimated on real-time vintages. Second, a model of data revisions is included, so that the BVAR is estimated on, and the forecasts conditioned on, estimates of the revised values. We show that both these methods improve the accuracy of density forecasts for US and UK output growth and inflation. We also investigate how the characteristics of the underlying data and revisions processes affect forecasting performance, and provide guidance that may benefit professional forecasters. |
| Keywords: | real-time forecasting ; inflation and output growth predictive densities ; real-time vintages ; stochastic volatility ; |
| JEL: | C53 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:wrk:wrkemf:36&r=all |
| By: | Anthoulla Phella |
| Abstract: | This paper considers forecasts of the growth and inflation distributions of the United Kingdom with factor-augmented quantile autoregressions under a model averaging framework. We investigate model combinations across models using weights that minimise the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Quantile Regression Information Criterion (QRIC) as well as the leave-one-out cross validation criterion. The unobserved factors are estimated by principal components of a large panel with N predictors over T periods under a recursive estimation scheme. We apply the aforementioned methods to the UK GDP growth and CPI inflation rate. We find that, on average, for GDP growth, in terms of coverage and final prediction error, the equal weights or the weights obtained by the AIC and BIC perform equally well but are outperformed by the QRIC and the Jackknife approach on the majority of the quantiles of interest. In contrast, the naive QAR(1) model of inflation outperforms all model averaging methodologies. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.12263&r=all |
| By: | Tahir Miriyev; Alessandro Contu; Kevin Schafers; Ion Gabriel Ion |
| Abstract: | In this work we considered several hybrid modelling approaches for forecasting energy spot prices in EPEC market. Hybridization is performed through combining a Naive model, Fourier analysis, ARMA and GARCH models, a mean-reversion and jump-diffusion model, and Recurrent Neural Networks (RNN). Training data was given in terms of electricity prices for 2013-2014 years, and test data as a year of 2015. |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2010.08400&r=all |
| By: | G.M. Gallo; D. Lacava; E. Otranto |
| Abstract: | As a response to the Great Recession, many central banks resorted to unconventional monetary policies, in the form of a balance sheet expansion. Our research aims at analyzing the impact of the ECB policies on stock market volatility in four Eurozone countries (France, Germany, Italy and Spain) within the Multiplicative Error Model framework. We propose a model which allows us to quantify the part of market volatility depending directly on unconventional policies by distinguishing between the announcement the implementation effects. While we observe an increase in volatility on announcement days, we find a negative implementation effect, which causes a remarkable reduction in volatility in the long term. A Model Confidence Set approach finds how the forecasting power of the proxy improves significantly after the policy announcement; a multi–step ahead forecasting exercise estimates the duration of the effect, and, by shocking the policy variable, we are able to quantify the reduction in volatility which is more marked for debt–troubled countries. |
| Keywords: | Unconventional monetary policy;realized volatility;Multiplicative Error Model;Model Confidence Set;Financial market |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:cns:cnscwp:202006&r=all |
| By: | Yevgeniy Goryakin (OECD - The Organisation for Economic Coopération and Development); Sophie Thiébaut (Imperial College Business School London); Sébastien Cortaredona (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université); M. Aliénor Lerouge (OECD - The Organisation for Economic Coopération and Development); Michele Cecchini (OECD - The Organisation for Economic Coopération and Development); Andrea Feigl (OECD - The Organisation for Economic Coopération and Development); Bruno Ventelou (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université) |
| Abstract: | Background: Ageing populations and rising prevalence of non-communicable diseases (NCDs) increasingly contribute to the growing cost burden facing European healthcare systems. Few studies have attempted to quantify the future magnitude of this burden at the European level, and none of them consider the impact of potential changes in risk factor trajectories on future health expenditures. Methods: The new microsimulation model forecasts the impact of behavioural and metabolic risk factors on NCDs, longevity and direct healthcare costs, and shows how changes in epidemiological trends can modify those impacts. Economic burden of NCDs is modelled under three scenarios based on assumed future risk factors trends: business as usual (BAU); best case and worst case predictions (BCP and WCP). Findings: The direct costs of NCDs in the EU 27 countries and the UK (in constant 2014 prices) will grow under all scenarios. Between 2014 and 2050, the overall healthcare spending is expected to increase by 0.8% annually under BAU. In the all the countries, 605 billion Euros can be saved by 2050 if BCP is realized compared to the BAU, while excess spending under the WCP is forecast to be around 350 billion. Interpretation: Although the savings realised under the BCP can be substantial, population ageing is a stronger driver of rising total healthcare expenditures in Europe compared to scenario-based changes in risk factor prevalence. |
| Keywords: | Health economics,Europe,Medical risk factors,European Union,Noncommunicable diseases,Cancer risk factors,Cardiovascular disease risk,Obesity |
| Date: | 2020–09 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-02964995&r=all |
| By: | Andrew C. Harvey |
| Abstract: | This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. A class of univariate time series models was developed by Harvey and Kattuman (2020). Here the framework is extended to modelling the relationship between two or more series. The role of common trends is discussed, and it is shown that when there is balanced growth in the logarithms of the growth rates of the cumulated series, simple regression models can be used to forecast using leading indicators. Data on daily deaths from Covid-19 in Italy and the UK provides an example. When growth is not balanced, the model can be extended by including a stochastic trend: the viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden's soft lockdown coronavirus policy. |
| Keywords: | Balanced growth, Co-integration, Covid-19, Gompertz curve, Kalman filter, Stochastic trend |
| JEL: | C22 C32 |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:nsr:niesrd:517&r=all |