nep-for New Economics Papers
on Forecasting
Issue of 2015‒07‒25
twelve papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Accuracy Evaluation of Tourist Arrivals: Evidence from Parametric and Non-Parametric Techniques By Hossein Hassani; Emmanuel Sirimal Silva; Nikolaos Antonakakis; George Filis; Rangan Gupta
  2. Housing Market Forecasts with Factor Combinations By Charles Rahal
  3. Modeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-Type Volatility Models By Mawuli Segnon; Thomas Lux; Rangan Gupta
  4. Forecasting Prices in Regime-Switching Markets By Martín González-Rozada; Luis Pereiro
  5. A Critical Review of Posch, J. and F. Rumler (2015), 'Semi-Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve,' Journal of Forecasting 34(2): 145-62 By Medel, Carlos A.
  6. Forecasting Consumption: The Role of Consumer Confidence in Real Time with many Predictors By Kajal Lahiri; George Monokroussos; Yongchen Zhao
  7. Forecasting Elections: Do Prediction Markets Tells Us Anything More than the Polls? By Davis, Brent
  8. Multi-scaling of wholesale electricity prices By Francesco Caravelli; James Requeima; Cozmin Ududec; Ali Ashtari; Tiziana Di Matteo; Tomaso Aste
  9. Heterogeneity in Macroeconomic News Expectations: A disaggregate level analysis By Imane El Ouadghiri
  10. Endogenous derivation and forecast of lifetime PDs By Perederiy, Volodymyr
  11. The Performance of Conditional CAPMs based on Evidence from the European Union’s (EU) Financial Stock Markets before and after the Eurozone Financial Crisis By Serdar Neslihanoglu
  12. Financial Stock Market Co-movement and Correlation: Evidence in the European Union (EU) Area Before and After the October 2008 Financial Crisis By Serdar Neslihanoglu

  1. By: Hossein Hassani (Statistical Research Centre, Bournemouth University, 89 Holdenhurst Road, Bournemouth BH8 8EB, UK); Emmanuel Sirimal Silva (Statistical Research Centre, Bournemouth University, 89 Holdenhurst Road, Bournemouth BH8 8EB, UK); Nikolaos Antonakakis (Vienna University of Economics and Business, Department of Economics, Institute for International Economics, Welthandelsplatz 1, 1020, Vienna, Austria and University of Portsmouth, Economics and Finance Subject Group, Portsmouth Business School, Portland Street, Portsmouth, PO1 3DE, United Kingdom and Johannes Kepler University, Department of Economics, Altenbergerstrae 69, Linz, 4040, Austria); George Filis (Bournemouth University, Accounting, Finance and Economics Department, 89 Holdenhurst Road, Bournemouth, Dorset, BH8 8EB, United Kingdom); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: This paper evaluates the use of several parametric and nonparametric forecasting techniques for predicting tourism demand in selected European countries. ARIMA, Exponential Smoothing (ETS), Neural Networks (NN), Trigonometric Box-Cox ARMA Trend Seasonal (TBATS), Fractionalized ARIMA (ARFIMA) and both Singular Spectrum Analysis algorithms, i.e. recurrent SSA (SSA-R) and vector SSA (SSA-V), are adopted to forecast tourist arrivals in Germany, Greece, Spain, Cyprus, Netherlands, Austria, Portugal, Sweden and United Kingdom. This paper not only marks the introductory application of the TBATS model for tourism demand forecasting, but also marks the first instance in which the SSA-R model is effectively utilized for forecasting tourist arrivals. The data is tested rigorously for normality, seasonal unit roots and break points whilst the out-of-sample forecasts are tested for statistical significance. Our findings show that no single model can provide the best forecasts for any of the countries considered here in the short-, medium- and long-run. Moreover, forecasts from NN and ARFIMA models provide the least accurate predictions for European tourist arrivals, yet interestingly ARFIMA forecasts are better than the powerful NN model. SSA-R, SSA-V, ARIMA and TBATS are found to be viable options for modelling European tourist arrivals based on the most number of times a given model outperforms the competing models in the above order. The results enable forecasters to choose the most suitable model (from those evaluated here) based on the country and horizon for forecasting tourism demand. Should a single model be of interest, then, across all selected countries and horizons the SSA-R model is found to be the most efficient based on lowest overall forecasting error.
    Keywords: Tourist arrivals, Tourism demand, Forecasting, Singular Spectrum Analysis, ARIMA, Exponential Smoothing, Neural Networks, TBATS, ARFIMA.
    Date: 2015–07
  2. By: Charles Rahal
    Abstract: In this paper we take a computational approach to forecasting a macroeconometric model of housing markets across six original data sets with large cross-sectional dimensions. We compare a large number of models which vary by the choice of factors, 'observable endogenous variables' and the number of lags in addition to classical and modern (factor based) specifications. We utilize various optimal model selection and model averaging techniques, comparing them against classical benchmarks. Within a 'pseudo real-time' out of sample forecasting context, results show that the approximate BMA method is the best weighting and selection technique, generating forecasts able to outperform the automated univariate benchmark of Dyndman and Khandakar (2008) upwards of 58% of the time. However, the average forecast error is lower in magnitude over all recursions and countries for the benchmark compared with all models for all variables. We also provide results on the biased nature of this class of models in general, in addition to the forecast error increasing as a function of the underlying variance of the series being forecast.
    Keywords: Housing Markets, Forecasting, Factor Error Correction Models, FAVARs
    JEL: C53 R30
    Date: 2015–06
  3. By: Mawuli Segnon (Department of Economics, Univeristy of Kiel, Germany); Thomas Lux (Department of Economics, Univeristy of Kiel, Germany and Bank of Spain Chair of Computational Economics Department of Economics, Univeristy Jaume I Castellon, Spain); Rangan Gupta (Department of Economics, University of Pretoria)
    Abstract: This paper applies Markov-switching multifractal (MSM) processes to model and forecast carbon dioxide (CO2) emission price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean absolute error and the value-at-risk). We evaluate the performance of these models via the superior predictive ability test. We find that the forecasts based on the MSM model cannot be outperformed by its competitors under the vast majority of criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting procedures, we do, however, not find significant differences in the performance of the candidate models under this particular criterion. We also find that we cannot reject the null hypothesis of MSM forecasts encompassing those of GARCH-type models. In line with this result, optimally combined forecasts do indeed hardly improve upon the best single models in our sample.
    Keywords: Carbon dioxide emission allowance prices, GARCH, Markov-switching GARCH, FIGARCH, Multifractal Processes, SPA test, encompassing test, Backtesting
    JEL: Q47
    Date: 2015–07
  4. By: Martín González-Rozada; Luis Pereiro
    Abstract: Linear autoregressive (LAR) models poorly predict asset prices in nonlinear, regime-switching markets. We introduce SETAR, a threshold model that accounts for nonlinearities, to test for the existence of regime-switching in global equity markets. A comparison of SETAR‘s predictive power against that of LAR models suggests that SETAR yields more accurate long forecasts, in both emerging and developed stock markets. We discuss extensions of threshold models into portfolio management, corporate valuation, and the long-term forecasting of financial indicators.
  5. By: Medel, Carlos A.
    Abstract: This article critically reviews and proposes further extensions to Posch, J. and F. Rumler (2015), 'Semi-Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve,' Journal of Forecasting 34(2): 145-62.
    Keywords: New Keynesian Phillips Curve; inflation forecasts
    JEL: C22 C53 E31 E37 E47
    Date: 2015–07–17
  6. By: Kajal Lahiri (Department of Economics, University at Albany, State University of New York); George Monokroussos (European Comission, Joint Research Centre (JRC)); Yongchen Zhao (Department of Economics, Towson University)
    Abstract: We study the role of consumer confidence in forecasting real personal consumption expenditure, and contribute to the extant literature in three substantive ways: First, we reexamine existing empirical models of consumption and consumer confidence not only at the quarterly frequency, but using monthly data as well. Second, we employ real-time data in addition to commonly used revised vintages. Third, we investigate the role of consumer confidence in a rich information context. We produce forecasts of consumption expenditures with and without consumer confidence measures using a dynamic factor model and a large, real-time, jagged-edge data set. In a robust way, we establish the important role of confidence surveys in improving the accuracy of consumption forecasts, manifesting primarily through the services component. During the recession of 2007-09, sentiment is found to have a more pervasive effect on all components of aggregate consumption - durables, non-durables and services.
    Keywords: Forecasting, Consumption, Consumer Sentiment, Factor Models, Kalman Filter, Real-Time Data, Fluctuation test.
    JEL: C53 E21 E27
    Date: 2015–07
  7. By: Davis, Brent
    Abstract: Election forecasting is an expanding domain within political science, moving from the outer edges (as a novelty pursued by a few ‘quants’) toward the mainstream of the discipline. Amongst the most high profile of election forecasting techniques are prediction markets and vote-intention polls. While the weight of scholarly opinion appears to favour prediction markets over polls for election forecasting, there remain challengers and critics. This article joins with the challengers and the critics, looking at whether this ‘horse race’ competition between election forecasting approaches is valid. Using data from the 2013 Australian federal election, we conclude such comparisons-of-forecasts are misplaced in the Australian context, as prediction markets and vote-intention polls appear to be independent of each other given information from one appears to have no impact on the other.
    Keywords: voting behaviour/ choice; election forecasting
    JEL: C00 C20
    Date: 2015–07
  8. By: Francesco Caravelli; James Requeima; Cozmin Ududec; Ali Ashtari; Tiziana Di Matteo; Tomaso Aste
    Abstract: We empirically analyze the most volatile component of the electricity price time series from two North-American wholesale electricity markets. We show that these time series exhibit fluctuations which are not described by a Brownian Motion, as they show multi-scaling, high Hurst exponents and sharp price movements. We use the generalized Hurst exponent (GHE, $H(q)$) to show that although these time-series have strong cyclical components, the fluctuations exhibit persistent behaviour, i.e., $H(q)>0.5$. We investigate the effectiveness of the GHE as a predictive tool in a simple linear forecasting model, and study the forecast error as a function of $H(q)$, with $q=1$ and $q=2$. Our results suggest that the GHE can be used as prediction tool for these time series when the Hurst exponent is dynamically evaluated on rolling time windows of size $\approx 50 - 100$ hours. These results are also compared to the case in which the cyclical components have been subtracted from the time series, showing the importance of cyclicality in the prediction power of the Hurst exponent.
    Date: 2015–07
  9. By: Imane El Ouadghiri
    Abstract: The aim of this paper is to investigate heterogeneity in macroeconomic news forecasts using disaggregate data of monthly expectation surveys conducted by Bloomberg on macroeconomic indicators from January 1999 to February 2013. We find three major results. First, we show that macroeconomic indicator forecasters are mostly heterogeneous and their expectations are found to violate the rational expectation hypothesis. Second, the use of the expectation mixed model –combining extrapolative, regressive and adaptive components– reveals a large dominance of the chartist profile among forecasters with a systematical persistence over time despite all the structural breaks determined endogenously by the Bai-Perron estimation method. Third, we find that forecasters whose forecasting models combine at least two or three anticipatory components (extrapolative, and regressive or/and adaptive) and display high temporal flexibility, thus adapting to different structural breaks, are those which provide the most accurate forecasts.
    Keywords: Announcements, heterogeneity, survey data, expectation formation.
    JEL: G14 G12 E44 C22
    Date: 2015
  10. By: Perederiy, Volodymyr
    Abstract: This paper proposes a simple technical approach for the derivation of future (forward) point-in-time PD forecasts, with minimal data requirements. The inputs required are the current and future through-the-cycle PDs of the obligors, their last known default rates, and a measure for the systematic dependence of the obligors. Technically, the forecasts are made from within a classical asset-based credit portfolio model, just with the assumption of a suitable autoregressive process for the systematic factor. The paper discusses in detail the practical issues of implementation, in particular the parametrization alternatives. The paper also shows how the approach can be naturally extended to low-default portfolios with volatile default rates, using Bayesian methodology. Furthermore, the expert judgments about the current macroeconomic state, although not necessary for the forecasts, can be embedded using the Bayesian technique. The presented forward PDs can be used for the derivation of lifetime credit losses required by the new accounting standard IFRS 9. In doing so, the presented approach is endogenous, as it does not require any exogenous macroeconomic forecasts which are notoriously unreliable and often subjective.
    Keywords: Prediction, Probability of Default, PD, Default Rates, Through-the-Cycle, TTC, Point-in-Time, PIT, Credit Portfolio Model, Systematic Factor, Macroeconomic Factor, Time Series, Autoregression, Bayesian Analysis, IFRS 9, Accounting, Financial Instruments, Lifetime, Expected Credit Losses
    JEL: C11 C13 C22 C51 C53 E32 E37 G33 M41
    Date: 2015–07–14
  11. By: Serdar Neslihanoglu (Eskisehir Osmangazi University)
    Abstract: This paper focuses on identifying the stochastic behavior of financial stock markets for the purpose of making profitable investment decisions. A time-varying version of the Linear Market Model (consistent with a conditional Capital Asset Pricing Model (CAPM)) which allows only for the time-varying beta risk parameter is the benchmark market model for this research. To validate and extend the time-varying Linear Market Model, two related extensions are defined. These are newly formulated forms of the time-varying Higher order Market Models (consistent with their equivalent conditional Higher order CAPMs (Neslihanoglu, 2014)) and are simple polynomial extensions of the time-varying Linear Market Model; namely, the time-varying Quadratic Market Model (which allows for the time-varying beta and time-varying co-skewness risk parameters) and the time-varying Cubic Market Model (which allows for the time-varying beta, time-varying co-skewness, and time-varying co-kurtosis risk parameters). Here, the time-varying risk parameters are estimated using the state space model. The data is based on several EU area financial stock markets before and after the Eurozone financial crisis as well as on forecasting made 2 years into the future. The empirical results found support the time-varying Linear Market Model which allows only for the time-varying beta risk parameter when modeling and forecasting EU area financial stock markets.
    Keywords: CAPM, EU Countries, Higher-Order Moments, State Space Model, Systematic Risk Measure Parameters
    JEL: C19 C58 C15
  12. By: Serdar Neslihanoglu (Eskisehir Osmangazi University)
    Abstract: This paper investigates the effect that the financial stock market had on co-movement and correlation when modeling and forecasting individual financial stock market. According to both the Capital Asset Pricing Model (CAPM) (Sharpe-Linter-Mossin, 1960’s) and portfolio theory (Markowitz, 1952), the likely presence of correlations between various financial stock markets is an important issue for stock market portfolio managers; for example, in terms of portfolio diversification, it could reduce overall portfolio risk. Hence, we propose a multivariate extension of the conditional CAPM with a time-varying beta using the state space model; this, in turn, would allow the correlation between financial stock markets to be utilized during the estimation process. This paper presents evidence based on monthly data generated by several EU area financial stock markets before and after the October 2008 financial crisis and forecasting 1 year into the future. The empirical results overwhelmingly support one’s considering the financial stock market co-movement and correlation structure when modeling and forecasting individual EU area financial stock markets.
    Keywords: CAPM, Co-movement and Correlation, EU Area Financial Stock Markets, October 2008 Financial Crisis, Multivariate State Space Model, Systematic Risk.
    JEL: C52 C58 C19

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