nep-for New Economics Papers
on Forecasting
Issue of 2020‒11‒23
fourteen papers chosen by
Rob J Hyndman
Monash University

  1. A Comparison of Monthly Global Indicators for Forecasting Growth By Christiane Baumeister; Pierre Guérin
  2. Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression By Sinha, Pankaj; Verma, Aniket; Shah, Purav; Singh, Jahnavi; Panwar, Utkarsh
  3. Encompassing tests for value at risk and expected shortfall multi-step forecasts based on inference on the boundary By Dimitriadis, Timo; Liu, Xiaochun; Schnaitmann, Julie
  4. Prediction for the 2020 United States Presidential Election using Linear Regression Model By Sinha, Pankaj; Verma, Aniket; Shah, Purav; Singh, Jahnavi; Panwar, Utkarsh
  5. Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods By Diunugala, Hemantha Premakumara; Mombeuil, Claudel
  6. Robust Forecasting By Timothy Christensen; Hyungsik Roger Moon; Frank Schorfheide
  7. Learning from Forecast Errors: A New Approach to Forecast Combinations By Tae-Hwy Lee; Ekaterina Seregina
  8. The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success By Steven Lehrer; Tian Xie
  9. Real-time forecasting of the Australian macroeconomy using Bayesian VARs By Zhang, Bo; Nguyen, Bao H.
  10. Testing forecast rationality for measures of central tendency By Dimitriadis, Timo; Patton, Andrew J.; Schmidt, Patrick W.
  11. A Review on the Leading Indicator Approach towards Economic Forecasting By Soh, Ann-Ni
  12. Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models By Constandina Koki; Stefanos Leonardos; Georgios Piliouras
  13. Modelling Loans to Non-Financial Corporations within the Eurozone: A Long-Memory Approach By Guglielmo Maria Caporale; Luis A. Gil-Alana
  14. Sparse time-varying parameter VECMs with an application to modeling electricity prices By Niko Hauzenberger; Michael Pfarrhofer; Luca Rossini

  1. By: Christiane Baumeister; Pierre Guérin
    Abstract: This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecast accuracy, while other monthly measures have more mixed success. This global economic conditions indicator contains valuable information also for assessing the current and future state of the economy for a set of individual countries and groups of countries. We use this indicator to track the evolution of the nowcasts for the US, the OECD area, and the world economy during the coronavirus pandemic and quantify the main factors driving the nowcasts.
    Keywords: MIDAS models, global economic conditions, world GDP growth, nowcasting, forecasting, mixed frequency
    JEL: C20 C52 E37
    Date: 2020
  2. By: Sinha, Pankaj; Verma, Aniket; Shah, Purav; Singh, Jahnavi; Panwar, Utkarsh
    Abstract: This paper aims at determining the various economic and non-economic factors that can influence the voting behaviour in the forthcoming United States Presidential Election using Lasso regression, a Machine learning algorithm. Even though contemporary discussions on the subject of the United States Presidential Election suggest that the level of unemployment in the economy will be a significant factor in determining the result of the election, in our study, it has been found that the rate of unemployment will not be the only significant factor in forecasting the election. However, various other economic factors such as the inflation rate, rate of economic growth, and exchange rates will not have a significant influence on the election result. The June Gallup Rating, is not the only significant factor for determining the result of the forthcoming presidential election. In addition to the June Gallup Rating, various other non-economic factors such as the performance of the contesting political parties in the midterm elections, Campaign spending by the contesting parties and scandals of the Incumbent President will also play a significant role in determining the result of the forthcoming United States Presidential Election. The paper explores the influence of all the aforementioned economic and non-economic factors on the voting behaviour of the voters in the forthcoming United States Presidential Election. The proposed Lasso Regression model forecasts that the vote share for the incumbent Republican Party to be 41.63% in the 2020 US presidential election. This means that the incumbent party is most likely to lose the upcoming election.
    Keywords: US Presidential Election, Machine Learning, Lasso Regression, Economic Factors, None Economic Factor, Forecasting, Prediction
    JEL: C10 C13 C15 C6 C61 C63 C8
    Date: 2020–10–13
  3. By: Dimitriadis, Timo; Liu, Xiaochun; Schnaitmann, Julie
    Abstract: We propose forecast encompassing tests for the Expected Shortfall (ES) jointly with the Value at Risk (VaR) based on flexible link (or combination) functions. Our setup allows testing encompassing for convex forecast combinations and for link functions which preclude crossings of the combined VaR and ES forecasts. As the tests based on these link functions involve parameters which are on the boundary of the parameter space under the null hypothesis, we derive and base our tests on nonstandard asymptotic theory on the boundary. Our simulation study shows that the encompassing tests based on our new link functions outperform tests based on unrestricted linear link functions for one-step and multi-step forecasts. We further illustrate the potential of the proposed tests in a real data analysis for forecasting VaR and ES of the S&P 500 index.
    Keywords: asymptotic theory on the boundary,joint elicitability,multi-step ahead and aggregate forecasts,forecast evaluation and combinations
    JEL: C12 C52 C58
    Date: 2020
  4. By: Sinha, Pankaj; Verma, Aniket; Shah, Purav; Singh, Jahnavi; Panwar, Utkarsh
    Abstract: The paper identifies various crucial factors, economic and non-economic, essential for predicting the 2020 United States presidential election results. Although it has been suggested by the contemporary discussions on the subject of United States presidential election that inflation rate, unemployment rate, and other such economic factors will play an important role in determining who will win the forthcoming United States Presidential Elections in November, it has been found in this study that, non-economic variables have a significant influence on the voting behaviour. Various non-economic factors like the performance of the contesting political parties in the midterm elections, the June Gallup Rating for the incumbent President, Average Gallup rating during the tenure of the incumbent President, Gallup Index, and Scandals of the Incumbent President were found to have a massive impact on the election outcomes. In the research conducted by Lewis-Beck and Rice (1982) , it was proposed that the Gallup rating for the Incumbent President, obtained in the month of June of the election year, is a significant factor in determining the results of the Presidential Elections. The major reason behind obtaining the Gallup Rating in June of the election year, post-primaries and pre-conventions, is that it is a relative political calm period. However, it has been found in this study that despite the existence of a relationship between the vote share of the incumbent President and his Gallup rating for June, the said Gallup rating cannot be used as the only factor for forecasting the results of the Presidential Election. The influence of all the aforementioned economic and non-economic factors and some other factors on the voter's voting behavior in the forthcoming United States Presidential Election is analyzed in this paper. The proposed regression model in the paper forecasts that Republican party candidate Donald Trump would receive a vote share of 46.74 ± 2.638%.
    Keywords: United States Presidential Election, Economic Factor, Regression Model, Forecasting, Prediction
    JEL: C10 C13 C51 C53
    Date: 2020–09–15
  5. By: Diunugala, Hemantha Premakumara; Mombeuil, Claudel
    Abstract: Purpose: This study compares three different methods to predict foreign tourist arrivals (FTAs) to Sri Lanka from top-ten countries and also attempts to find the best-fitted forecasting model for each country using five model performance evaluation criteria. Methods: This study employs two different univariate-time-series approaches and one Artificial Intelligence (AI) approach to develop models that best explain the tourist arrivals to Sri Lanka from the top-ten tourist generating countries. The univariate-time series approach contains two main types of statistical models, namely Deterministic Models and Stochastic Models. Results: The results show that Winter’s exponential smoothing and ARIMA are the best methods to forecast tourist arrivals to Sri Lanka. Furthermore, the results show that the accuracy of the best forecasting model based on MAPE criteria for the models of India, China, Germany, Russia, and Australia fall between 5 to 9 percent, whereas the accuracy levels of models for the UK, France, USA, Japan, and the Maldives fall between 10 to 15 percent. Implications: The overall results of this study provide valuable insights into tourism management and policy development for Sri Lanka. Successful forecasting of FTAs for each market source provide a practical planning tool to destination decision-makers.
    Keywords: foreign tourist arrivals, winter’s exponential smoothing, ARIMA, simple recurrent neural network, Sri Lanka
    JEL: C45 C5 Z0
    Date: 2020–10–30
  6. By: Timothy Christensen; Hyungsik Roger Moon; Frank Schorfheide
    Abstract: We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspecification or structural breaks. We derive "robust" forecasts which minimize maximum risk or regret over the set of forecast distributions. We show that for a large class of models including semiparametric panel data models for dynamic discrete choice, the robust forecasts depend in a natural way on a small number of convex optimization problems which can be simplified using duality methods. Finally, we derive "efficient robust" forecasts to deal with the problem of first having to estimate the set of forecast distributions and develop a suitable asymptotic optimality theory.
    Date: 2020–11
  7. By: Tae-Hwy Lee; Ekaterina Seregina
    Abstract: This paper studies forecast combination (as an expert system) using the precision matrix estimation of forecast errors when the latter admit the approximate factor model. This approach incorporates the facts that experts often use common sets of information and hence they tend to make common mistakes. This premise is evidenced in many empirical results. For example, the European Central Bank's Survey of Professional Forecasters on Euro-area real GDP growth demonstrates that the professional forecasters tend to jointly understate or overstate GDP growth. Motivated by this stylized fact, we develop a novel framework which exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic components of the forecast errors. The proposed algorithm is called Factor Graphical Model (FGM). Our approach overcomes the challenge of obtaining the forecasts that contain unique information, which was shown to be necessary to achieve a "winning" forecast combination. In simulation, we demonstrate the merits of the FGM in comparison with the equal-weighted forecasts and the standard graphical methods in the literature. An empirical application to forecasting macroeconomic time series in big data environment highlights the advantage of the FGM approach in comparison with the existing methods of forecast combination.
    Date: 2020–11
  8. By: Steven Lehrer; Tian Xie (Queen's University)
    Abstract: There exists significant hype regarding how much machine learning and incorporating social media data can improve forecast accuracy in commercial applications. To assess if the hype is warranted, we use data from the film industry in simulation experiments that contrast econometric approaches with tools from the predictive analytics literature. Further, we propose new strategies that combine elements from each literature in a bid to capture richer patterns of heterogeneity in the underlying relationship governing revenue. Our results demonstrate the importance of social media data and value from hybrid strategies that combine econometrics and machine learning when conducting forecasts with new big data sources. Specifically, while both least squares support vector regression and recursive partitioning strategies greatly outperform dimension reduction strategies and traditional econometrics approaches in forecast accuracy, there are further significant gains from using hybrid approaches. Further, Monte Carlo experiments demonstrate that these benefits arise from the significant heterogeneity in how social media measures and other film characteristics influence box office outcomes.
    Keywords: Machine Learning, Model Specification, Heteroskedasticity, Heterogeneity, Social Media, Big Data
    JEL: C52 L82 D03 M21 C53
    Date: 2020–10
  9. By: Zhang, Bo (School of Economics, Shanghai University); Nguyen, Bao H. (Tasmanian School of Business & Economics, University of Tasmania)
    Abstract: This paper evaluates the real-time forecast performance of alternative Bayesian Vector Autoregressive (VAR) models for the Australian macroeconomy. To this end, we construct an updated vintage database and estimate a set of model specifications with different covariance structures. The results suggest that a large VAR model with 20 variables tends to outperform a small VAR model when forecasting GDP growth, CPI inflation and unemployment rate. We find consistent evidence that the models with more flexible error covariance structures forecast GDP growth and inflation better than the standard VAR, while the standard VAR does better than its counterparts for unemployment rate. The results are robust under alternative priors and when the data includes the early stage of the COVID-19 crisis.
    Keywords: Australia, real-time forecast, non-Gaussian, stochastic volatility
    JEL: C11 C32 C53 C55
    Date: 2020
  10. By: Dimitriadis, Timo; Patton, Andrew J.; Schmidt, Patrick W.
    Abstract: Rational respondents to economic surveys may report as a point forecast any measure of the central tendency of their (possibly latent) predictive distribution, for example the mean, median, mode, or any convex combination thereof. We propose tests of forecast rationality when the measure of central tendency used by the respondent is unknown. We overcome an identification problem that arises when the measures of central tendency are equal or in a local neighborhood of each other, as is the case for (exactly or nearly) symmetric distributions. As a building block, we also present novel tests for the rationality of mode forecasts. We apply our tests to survey forecasts of individual income, Greenbook forecasts of U.S. GDP, and random walk forecasts for exchange rates. We find that the Greenbook and random walk forecasts are best rationalized as mean, or near-meanforecasts, while the income survey forecasts are best rationalized as mode forecasts.
    Keywords: forecast evaluation,weak identification,survey forecasts,mode forecasts
    JEL: D84 E27
    Date: 2020
  11. By: Soh, Ann-Ni
    Abstract: Economic cycle is defined as the fluctuation of an economy via expansion and contraction periods, influenced by varies kinds of macroeconomic indicators. The repeatable movement of economic indicators enables the accurate detection of these cycles with a forecasting approach that aims to improve economic development, especially by specific industries. Thus, economists and researchers have focused on the usefulness of the composite leading indicator in economic forecasting. It is regarded as a good illustration of an economic cycle or trend. This is due to its ease of use during the interpretation process, as several indicators can be aggregated and explained at once. This may provide useful insights for policy planning, risk monitoring and community development using the information gained from macroeconomic aggregates.
    Keywords: leading indicator; growth cycle; forecasting; composite indicator; early warning system
    JEL: C53 E32 E37
    Date: 2020–10–29
  12. By: Constandina Koki; Stefanos Leonardos; Georgios Piliouras
    Abstract: In this paper, we consider a variety of multi-state Hidden Markov models for predicting and explaining the Bitcoin, Ether and Ripple returns in the presence of state (regime) dynamics. In addition, we examine the effects of several financial, economic and cryptocurrency specific predictors on the cryptocurrency return series. Our results indicate that the 4-states Non-Homogeneous Hidden Markov model has the best one-step-ahead forecasting performance among all the competing models for all three series. The superiority of the predictive densities, over the single regime random walk model, relies on the fact that the states capture alternating periods with distinct returns' characteristics. In particular, we identify bull, bear and calm regimes for the Bitcoin series, and periods with different profit and risk magnitudes for the Ether and Ripple series. Finally, we observe that conditionally on the hidden states, the predictors have different linear and non-linear effects.
    Date: 2020–11
  13. By: Guglielmo Maria Caporale; Luis A. Gil-Alana
    Abstract: This paper uses fractional integration and cointegration methods to analyse the determinants of the amount of loans provided to non-financial corporations (NFCs) during the last three decades in four Eurozone countries, namely Germany, France, Italy and Spain. More specifically, ARFIMA (AutoRegressive Fractionally Integrated Moving Average) and FCVAR (Fractionally Cointegrated Vector Autoregression) models are estimated and then forecasts are also produced. All series are found to be highly persistent and long-run equilibrium relationships between them are also identified, confirming the role of real GDP and real gross investment as determinants of loans to NFCs. The forecasting accuracy of the FCVAR was also assessed by comparing it to that of the ARFIMA specifications, and the former were found to outperform the latter in all cases.
    Keywords: non-financial corporations, loans, Eurozone, long-memory, fractional integration and cointegration
    JEL: C22 C32 C51 H81
    Date: 2020
  14. By: Niko Hauzenberger; Michael Pfarrhofer; Luca Rossini
    Abstract: In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroscedastic disturbances. We combine a set of econometric techniques for dynamic model specification in an automatic fashion. We employ continuous global-local shrinkage priors for pushing the parameter space towards sparsity. In a second step, we post-process the cointegration relationships, the autoregressive coefficients and the covariance matrix via minimizing Lasso-type loss functions to obtain truly sparse estimates. This two-step approach alleviates overfitting concerns and reduces parameter estimation uncertainty, while providing estimates for the number of cointegrating relationships that varies over time. Our proposed econometric framework is applied to modeling European electricity prices and shows gains in forecast performance against a set of established benchmark models.
    Date: 2020–11

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