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on Forecasting |
| By: | Hagenhoff, Tim; Lustenhouwer, Joep |
| Abstract: | We propose a simple model of expectation formation with three distinct deviations from fully rational expectations. In particular, forecasters' expectations are sticky, extrapolate the most recent news about the current period, and depend on the lagged consensus forecast about the period being forecast. We find that all three biases are present in the Survey of Professional Forecasters as well as in the Livingston Survey, and that their magnitudes depend on the forecasting horizon. Moreover, in an over-identified econometric specification, we find that the restriction on coefficients implied by our model is always close to being satisfied and in most cases not rejected. We also stress the point that using the past consensus forecast to form expectations is a reasonable thing to do if a forecaster is not able to come up with fully rational expectations all by herself. |
| Keywords: | expectation formation,sticky expectations,extrapolation,consensus forecasts,survey data |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:bamber:163&r=all |
| By: | Na Guo; Bo Zhang; Jamie Cross |
| Abstract: | We investigate whether a class of trend models with various error term structures can improve upon the forecast performance of commonly used time series models when forecasting CPI inflation in Australia. The main result is that trend models tend to provide more accurate point and density forecasts compared to conventional autoregressive and Phillips curve models. The best short-term forecasts come from a trend model with stochastic volatility in the transitory component, while medium to long-run forecasts are better made by specifying a moving average component. We also find that trend models can capture various dynamics in periods of significance which conventional models cannot. This includes the dramatic reduction in inflation when the RBA adopted inflation targeting, the one-off 10 per cent Goods and Services Tax inflationary episode in 2000, and the gradual decline in inflation since 2014. |
| Keywords: | trend model, inflation forecast, Bayesian analysis, stochastic volatility |
| JEL: | C11 C52 E31 E37 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2020-99&r=all |
| By: | Afees A. Salisu (Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria); Juncal Cuñado (University of Navarra, School of Economics, Edificio Amigos, E-31080 Pamplona, Spain); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, South Africa) |
| Abstract: | This paper examines the vulnerability of BRICS exchange rates to geopolitical risks (GPR) using alternative measures ranging from global (historical and recent) GPR data to country-specific GRP data. We construct a GARCH-MIDAS-X model in order to accommodate available data frequencies for relevant series and by extension circumvent information loss and any associated bias. Using the long range data, we find that, on average, the BRICS exchange rates are less vulnerable to geopolitical risks, however, recent (short range) data suggest otherwise. We also find contrasting evidence between the recent global GPR data and the country-specific GPR data implying that the BRICS exchange rates are more vulnerable to global than domestic (country-specific) geopolitical risks in recent times while China seems to be the least vulnerable. The GARCH-MIDAS model that accounts for the GPR data outperforms the benchmark (the conventional GARCH-MIDAS model without the GPR predictor) both for the in-sample and out-of-sample forecasts. We also highlight some similarities in the results of long range GPR and oil price uncertainty and further note the sensitivity of the results to alternative data samples for GPR. Finally, our results have implications for portfolio diversification strategies in the BRICS foreign exchange markets and in particular, we document economic gains of accounting for GPR in the valuation of foreign exchange portfolio. |
| Keywords: | Geopolitical risk; Exchange rate volatility; BRICS; GARCH-MIDAS; Forecast evaluation |
| JEL: | C53 F31 G17 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:pre:wpaper:2020105&r=all |
| By: | Nuno Goncalves; Domingos Seward |
| Abstract: | This paper applies a labour market flows approach to forecasting the unemployment rate, initially developed by Barnichon and Nekarda (2012) and subsequently extended by Barnichon and Garda (2016), to the Portuguese labour market. We start by implementing a simple two-state labour market forecasting model and then extend it to a three-state labour market forecasting model which incorporates movements in and out of the labour force. We test the forecasting accuracy of each of these models and find that the two-state flow-based forecasting model performs slightly better than the other tested models. We conclude that worker flow data is a valuable input for forecasting the unemployment in Portugal. |
| Keywords: | forecast, labour market dynamics, unemployment rate, worker flows |
| JEL: | C53 E27 J60 |
| Date: | 2019–11 |
| URL: | http://d.repec.org/n?u=RePEc:alf:wpaper:2019-01&r=all |
| By: | Lake, A. |
| Abstract: | Trying to estimate rational expectations does not usually minimise forecast error when forecasting macroeconomic or financial variables in reality. This is because, with samples of realistic length, optimal feasible forecasts contain conditional biases that reduce forecast variance. I demonstrate this by using penalised factor models to show that statistically simple inflation forecasts, primarily based on past inflation, are optimal even when other relevant financial and economic variables are available. I also show that US household inflation forecasts display many similarities to these simple optimal forecasts, but also contain mistakes that increase forecast error. Therefore a combination of `optimal feasible expectations' and behavioural errors explain US household inflation forecasts. This suggests that optimal feasible expectations, with additional behavioural errors in some cases, could explain forecast formation across economics and finance. |
| Keywords: | Forecasting, Expectations, Uncertainty, Shrinkage, Ination, Nominal Rigidities, Factor Models |
| JEL: | E37 D84 C53 |
| Date: | 2020–11–11 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:20105&r=all |
| By: | Cameron Roach; Rob J Hyndman; Souhaib Ben Taieb |
| Abstract: | Buildings are typically equipped with smart meters to measure electricity demand at regular intervals. Smart meter data for a single building have many uses, such as forecasting and assessing overall building performance. However, when data are available from multiple buildings, there are additional applications that are rarely explored. For instance, we can explore how different building characteristics influence energy demand. If each building is treated as a random effect and building characteristics are handled as fixed effects, a mixed effects model can be used to estimate how characteristics affect energy usage. In this paper we demonstrate that producing one day ahead demand predictions for 123 commercial office buildings using mixed models can improve forecasting accuracy. We experiment with random intercept, random intercept and slope, and nonlinear mixed models. The predictive performance of the mixed effects models are tested against naive, linear and nonlinear benchmark models fitted to each building separately. This research justifies using mixed models to improve forecasting accuracy and to quantify changes in energy consumption under different building configuration scenarios. |
| Keywords: | time series forecasting, mixed-effects models, smart meters, energy, electricity |
| JEL: | C10 C14 C52 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:msh:ebswps:2020-41&r=all |
| By: | Megaritis, Anastasios; Vlastakis, Nikolaos; Triantafyllou, Athanasios |
| Abstract: | In this paper we examine the predictive power of latent macroeconomic uncertainty on US stock market volatility and jump tail risk. We find that increasing macroeconomic uncertainty predicts a subsequent rise in volatility and price jumps in the US equity market. Our analysis shows that the latent macroeconomic uncertainty measure of Jurado et al. (2015) has the most significant and long-lasting impact on US stock market volatility and jumps in the equity market when compared to the respective impact of the VIX and other popular observable uncertainty proxies. Our study is the first to show that the latent macroeconomic uncertainty factor outperforms the VIX when forecasting volatility and jumps after the 2007 US Great Recession. We additionally find that latent macroeconomic uncertainty is a common forecasting factor of volatility and jumps of the intraday returns of S&P 500 constituents and has higher predictive power on the volatility and jumps of the equities which belong to the financial sector. Overall, our empirical analysis shows that stock market volatility is significantly affected by the rising degree of unpredictability in the macroeconomy, while it is relatively immune to shocks in observable uncertainty proxies. |
| Keywords: | Jumps, Bipower variation, Realized volatility, Macroeconomic Uncertainty |
| Date: | 2020–11–26 |
| URL: | http://d.repec.org/n?u=RePEc:esy:uefcwp:29200&r=all |
| By: | David Kohns; Arnab Bhattacharjee (Centre for Energy Economics Research and Policy, Heriot-Watt University) |
| Abstract: | More and more are Big Data sources, such as Google Trends, being used to augment nowcast models. An often neglected issue within the previous literature, which is especially pertinent to policy environments, is the interpretability of the Big Data source included in the model. We provide a Bayesian modeling framework which is able to handle all usual econometric issues involved in combining Big Data with traditional macroeconomic time series such as mixed frequency and ragged edges, while remaining computationally simple and allowing for a high degree of interpretability. In our model, we explicitly account for the possibility that the Big Data and macroeconomic data set included have different degreesof sparsity. We test our methodology by investigating whether Google trends in real time increase nowcast fit of US real GDP growth compared to traditional macroeconomic time series. We find that search terms improve performance of both point forecast accuracy as well as forecast density calibration not only before official information is released but alsolater into GDP reference quarters. Our transparent methodology shows that the increased fit stems from search terms acting as early warning signals to large turning points in GDP. |
| Keywords: | Big Data; Machine Learning; Interpretability; Illusion of Sparsity; Density Nowcast; Google Search Terms |
| JEL: | C31 C53 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:hwc:wpaper:010&r=all |
| By: | Alexey Averkin; Sergey Yarushev |
| Abstract: | In tis paper we consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets. Also, we make short review of researches in forecasting based on various models of ANFIS models. Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Also, we propose our models of DL and Neuro-Fuzzy Networks for this task. Finally, we show possibility of using these models for data science tasks. This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.05588&r=all |
| By: | Hamidreza Arian; Hossein Poorvasei; Azin Sharifi; Shiva Zamani |
| Abstract: | Extreme Value Theory (EVT) is one of the most commonly used approaches in finance for measuring the downside risk of investment portfolios, especially during financial crises. In this paper, we propose a novel approach based on EVT called Uncertain EVT to improve its forecast accuracy and capture the statistical characteristics of risk beyond the EVT threshold. In our framework, the extreme risk threshold, which is commonly assumed a constant, is a dynamic random variable. More precisely, we model and calibrate the EVT threshold by a state-dependent hidden variable, called Break-Even Risk Threshold (BRT), as a function of both risk and ambiguity. We will show that when EVT approach is combined with the unobservable BRT process, the Uncertain EVT's predicted VaR can foresee the risk of large financial losses, outperforms the original EVT approach out-of-sample, and is competitive to well-known VaR models when back-tested for validity and predictability. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.06693&r=all |
| By: | Yuanhua Feng (Paderborn University); Jan Beran (University of Konstanz); Sebastian Letmathe (Paderborn University); Sucharita Ghosh (Swiss Federal Research Institute WSL) |
| Abstract: | Volatility modelling is applied in a wide variety of disciplines, namely finance, en- vironment and societal disciplines, where modelling conditional variability is of in- terest e.g. for incremental data. We introduce a new long memory volatility model, called FI-Log-GARCH. Conditions for stationarity and existence of fourth moments are obtained. It is shown that any power of the squared returns shares the same memory parameter. Asymptotic normality of sample means is proved. The practical performance of the proposal is illustrated by an application to one-day rolling forecasts of the VaR (value at risk) and ES (expected shortfall). Comparisons with FIGARCH, FIEGARCH and FIAPARCH models are made using a criterion based on different traffic light test. The results of this paper indicate that the FI-Log- GARCH often outperforms the other models, and thus provides a useful alternative to existing long memory volatility models. |
| Keywords: | FI-Log-GARCH, stationary solutions, finite fourth moments, covariance structure, rolling forecasting VaR and ES, traffic light test of ES |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:pdn:ciepap:137&r=all |
| By: | Jong, Meng-Chang |
| Abstract: | Tourism is one of the most remarkable multi-faceted phenomena that contributes enormously to economic development for most countries around the globe. The steady growth of the world economy, rapid development in transportation systems, and visa facilitation have bolstered the industry by facilitating higher accessibility for tourists. However, tourism is a vulnerable and competitive industry that need to accommodate the rapid changes of tourist demand and economies as well as consider environment effects. Apart from these dynamic needs, an unexpected health crisis may also lead to devastating impacts on the tourism industry. The recent pandemic caused by the novel coronavirus of 2019 (COVID-19) has brought severe disruptions to the global economy, and specifically caused a tremendous decline in the tourism industry. It is one of the industries tremendously impacted by the outbreak, grounding airplanes and severely limiting the ability of people to travel abroad. Once the vaccines are available and movement restrictions are lifted, the tourism sector can be one of the key industries for economic recovery. More than ever, studies on tourism demand modelling and forecasting are crucial. A review of literature on tourism demand takes into account recent studies on the unprecedented COVID-19 pandemic. |
| Keywords: | Tourism demand; COVID-19; Panel analysis; ARDL; Forecasting; Gravity model |
| JEL: | C33 C87 E17 Z0 |
| Date: | 2020–11–08 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:103919&r=all |