nep-for New Economics Papers
on Forecasting
Issue of 2022‒08‒29
fifteen papers chosen by
Rob J Hyndman
Monash University

  1. Constructing GDP Nowcasting Models Using Alternative Data By Takashi Nakazawa
  2. Global combinations of expert forecasts By Qian, Yilin; Thompson, Ryan; Vasnev, Andrey L
  3. Forecast combination puzzle in the HAR model By Clements, Adam; Vasnev, Andrey
  4. A model for predicting Finnish household loan stocks By Nyholm, Juho; Silvo, Aino
  5. Distributional neural networks for electricity price forecasting By Grzegorz Marcjasz; Micha{\l} Narajewski; Rafa{\l} Weron; Florian Ziel
  6. On the uncertainty of a combined forecast: The critical role of correlation By Magnus, Jan; Vasnev, Andrey
  7. Density forecast comparison in small samples By Laura Coroneo; Fabrizio Iacone; Fabio Profumo
  8. Prévision de l’inflation en Côte D’ivoire : Analyse Comparée des Modèles Arima, Holt-Winters, et Lstm By Koffi, Siméon
  9. Variations on two-parameter families of forecasting functions: seasonal/nonseasonal Models, comparison to the exponential smoothing and ARIMA models, and applications to stock market data By Nabil Kahouadji
  10. Crypto Coins and Credit Risk: Modelling and Forecasting their Probability of Death By Fantazzini, Dean
  11. Improving Inflation Forecasts Using Robust Measures By Randal Verbrugge; Saeed Zaman
  12. Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes By Gregory Benton; Wesley J. Maddox; Andrew Gordon Wilson
  13. Time-Varying Poisson Autoregression By Giovanni Angelini; Giuseppe Cavaliere; Enzo D'Innocenzo; Luca De Angelis
  14. Time-Varying Parameter Four-Equation DSGE Model By Rangan Gupta; Xiaojin Sun
  15. An Alternative Proof of Minimum Trace Reconciliation By Mr. Futoshi Narita; Mr. Sakai Ando

  1. By: Takashi Nakazawa (Bank of Japan)
    Abstract: With coronavirus (COVID-19) having a significant impact on economic activity, the existing GDP nowcasting model, using only monthly and quarterly economic data, has become difficult to forecast with high accuracy. In this paper, we attempt to improve the accuracy of GDP nowcasting models by using alternative data that are available more promptly. Specifically, we construct nowcasting models that incorporate sparse estimation by Elastic Net using weekly retail sales data and hundreds of daily Internet search volume data, in addition to conventional monthly economic data. For the model formulation and data selection, we prepare a large number of candidate models using the method of forecast combination, which combines multiple forecasting models, and select "Best models" which minimize the forecast error, including data after the spread of COVID-19. The analysis shows that the use of alternative data significantly improves the forecasting accuracy of the model, especially at the 2-month prior to release of GDP, when the availability of monthly and quarterly economic data are limited.
    Keywords: Nowcasting; Alternative Data; Elastic Net; Forecast Combination
    JEL: C52 C53 C55
    Date: 2022–07–28
  2. By: Qian, Yilin; Thompson, Ryan; Vasnev, Andrey L
    Abstract: Expert forecast combination—the aggregation of individual forecasts from multiple subject matter experts— is a proven approach to economic forecasting. To date, research in this area has exclusively concentrated on local combination methods, which handle separate but related forecasting tasks in isolation. Yet, it has been known for over two decades in the machine learning community that global methods, which exploit taskrelatedness, can improve on local methods that ignore it. Motivated by the possibility for improvement, this paper introduces a framework for globally combining expert forecasts. Through our framework, we develop global versions of several existing forecast combinations. To evaluate the efficacy of these new global forecast combinations, we conduct extensive comparisons using synthetic and real data. Our real data comparisons, which involve expert forecasts of core economic indicators in the Eurozone, are the first empirical evidence that the accuracy of global combinations of expert forecasts can surpass local combinations.
    Keywords: Forecast combination, local forecasting, global forecasting, multi-task learning, European Central Bank, Survey of Professional Forecasters
    Date: 2022–07–29
  3. By: Clements, Adam; Vasnev, Andrey
    Abstract: The Heterogeneous Autoregressive (HAR) model of Corsi (2009) has become the benchmark model for predicting realized volatility given its simplicity and consistent empirical performance. Many modifications and extensions to the original model have been proposed that often only provide incremental forecast improvements. In this paper, we take a step back and view the HAR model as a forecast combination that combines three predictors: previous day realization (or random walk forecast), previous week average, and previous month average. When applying the Ordinary Least Squares (OLS) to combine the predictors, the HAR model uses optimal weights that are known to be problematic in the forecast combination literature. In fact, the simple average forecast often outperforms the optimal combination in many empirical applications. We investigate the performance of the simple average forecast for the realized volatility of the Dow Jones Industrial Average equity index. We find dramatic improvements in forecast accuracy across all horizons and different time periods. This is the first time the forecast combination puzzle is identified in this context.
    Keywords: Realized volatility, forecast combination, HAR model
    JEL: C53 C58
    Date: 2021–02–24
  4. By: Nyholm, Juho; Silvo, Aino
    Abstract: We propose a new Bayesian VAR model for forecasting household loan stocks in Finland. The model is designed to work as a satellite model of a larger DSGE model for the Finnish economy, the Aino 2.0 model. The forecasts produced with the BVAR model can be conditioned on projections of several macro variables obtained from the Aino 2.0 model. We study several specifications for the set of variables and lags included in the BVAR, and evaluate their out-of-sample forecast accuracy with root mean squared forecasting errors (RMSFEs). We then select a preferred specification that performs best in predicting the loan stocks over forecast horizons ranging from one to twelve quarters ahead. The model adds to the existing toolkit of forecast models currently in use at the Bank of Finland and improves our understanding of household debt trends in Finland.
    Keywords: household debt,Bayesian estimation,conditional forecasting
    JEL: C11 C32 E37
    Date: 2022
  5. By: Grzegorz Marcjasz; Micha{\l} Narajewski; Rafa{\l} Weron; Florian Ziel
    Abstract: We present a novel approach to probabilistic electricity price forecasting (EPF) which utilizes distributional artificial neural networks. The novel network structure for EPF is based on a regularized distributional multilayer perceptron (DMLP) which contains a probability layer. Using the TensorFlow Probability framework, the neural network's output is defined to be a distribution, either normal or potentially skewed and heavy-tailed Johnson's SU (JSU). The method is compared against state-of-the-art benchmarks in a forecasting study. The study comprises forecasting involving day-ahead electricity prices in the German market. The results show evidence of the importance of higher moments when modeling electricity prices.
    Date: 2022–07
  6. By: Magnus, Jan; Vasnev, Andrey
    Abstract: The purpose of this paper is to show that the effect of the zero-correlation assumption in combining forecasts can be huge, and that ignoring (positive) correlation can lead to confidence bands around the forecast combination that are much too narrow. In the typical case where three or more forecasts are combined, the estimated variance increases without bound when correlation increases. Intuitively, this is because similar forecasts provide little information if we know that they are highly correlated. Although we concentrate on forecast combinations and confidence bands, our theory applies to any statistic where the observations are linearly combined. We apply our theoretical results to explain why forecasts by Central Banks (in our case, the Bank of Japan) are so frequently misleadingly precise. In most cases, a correlation above 0.7 is required to produce reasonable confidence bands.
    Keywords: Combining information, Correlation, Growth forecasting, Inflation forecasting, Central Banks
    JEL: C52 C53 E31 E37 O40
    Date: 2021–12–21
  7. By: Laura Coroneo; Fabrizio Iacone; Fabio Profumo
    Abstract: We apply fixed-b and fixed-m asymptotics to tests of equal predictive accuracy and of encompassing for density forecasts. We verify in an original Monte Carlo design that fixed-smoothing asymptotics delivers correctly sized tests in this framework, even when only a small number of out of sample observations is available. We use the proposed density forecast comparison tests with fixed-smoothing asymptotics to assess the predictive ability of density forecasts from the European Central Bank's Survey of Professional Forecasters (ECB SPF).
    Keywords: density forecast comparison, ECB SPF, Diebold-Mariano test, forecast encompassing, fixed-smoothing asymptotics
    JEL: C12 C22 E17
    Date: 2022–06
  8. By: Koffi, Siméon
    Abstract: This paper attempts to highlight the role of new short-term forecasting methods. It leads to the fact that artificial neural networks (LSTM) are more efficient than classical methods (ARIMA and HOLT-WINTERS) in forecasting the HICP of Côte d'Ivoire. The data are from the “Direction des Prévisions, des Politiques et des Statistiques Economiques (DPPSE)” and cover the period from January 2012 to May 2022. The root mean square error of the long-term memory recurrent neural network (LSTM) is the lowest compared to the other two techniques. Thus, one can assert that the LSTM method improves the prediction by more than 90%, ARIMA by 68%, and Holt-Winters by 61%. These results make machine learning techniques (LSTM) excellent forecasting tools.
    JEL: C15 C81 C88
    Date: 2022–08–01
  9. By: Nabil Kahouadji
    Abstract: We introduce twenty four two-parameter families of advanced time series forecasting functions using a new and nonparametric approach. We also introduce the concept of powering and derive nonseasonal and seasonal models with examples in education, sales, finance and economy. We compare the performance of our twenty four models to both Holt--Winters and ARIMA models for both nonseasonal and seasonal times series. We show in particular that our models not only do not require a decomposition of a seasonal time series into trend, seasonal and random components, but leads also to substantially lower sum of absolute error and a higher number of closer forecasts than both Holt--Winters and ARIMA models. Finally, we apply and compare the performance of our twenty four models using five-year stock market data of 467 companies of the S&P500.
    Date: 2022–07
  10. By: Fantazzini, Dean
    Abstract: This paper examined a set of over two thousand crypto-coins observed between 2015 and 2020 to estimate their credit risk by computing their probability of death. We employed different definitions of dead coins, ranging from academic literature to professional practice, alternative forecasting models, ranging from credit scoring models to machine learning and time series-based models, and different forecasting horizons. We found that the choice of the coin death definition affected the set of the best forecasting models to compute the probability of death. However, this choice was not critical, and the best models turned out to be the same in most cases. In general, we found that the \textit{cauchit} and the zero-price-probability (ZPP) based on the random walk or the Markov Switching-GARCH(1,1) were the best models for newly established coins, whereas credit scoring models and machine learning methods using lagged trading volumes and online searches were better choices for older coins. These results also held after a set of robustness checks that considered different time samples and the coins' market capitalization.
    Keywords: Bitcoin, Crypto-assets, Crypto-currencies, Credit risk, Default Probability, Probability of Death, ZPP, Cauchit, Logit, Probit, Random Forests, Google Trends.
    JEL: C32 C35 C51 C53 C58 G12 G17 G32 G33
    Date: 2022
  11. By: Randal Verbrugge; Saeed Zaman
    Abstract: Both theory and extant empirical evidence suggest that the cross-sectional asymmetry across disaggregated price indexes might be useful in the forecasting of aggregate inflation. Trimmed-mean inflation estimators have been shown to be useful devices for forecasting headline PCE inflation. But does this stem from their ability to signal the underlying trend, or does it mainly come from their implicit signaling of asymmetry (when included alongside headline PCE)? We address this question by augmenting a “hard to beat” benchmark inflation forecasting model of headline PCE price inflation with robust measures of trimmed-mean estimators of inflation (median PCE and trimmed-mean PCE) and robust measures of the cross-sectional asymmetry (Bowley skewness; Kelly skewness) computed using the 180+ components of the PCE price index. We also construct new trimmed-mean measures of goods and services PCE inflation and their accompanying robust skewness. Our results indicate significant gains in the point and density accuracy of PCE inflation forecasts over medium- and longer-term horizons, up through and including the COVID-19 pandemic. We find that improvements in accuracy stem mainly from the trend information implicit in trimmed-mean estimators, but that skewness is also useful. Median PCE slightly outperforms trimmed-mean PCE; both outperform core PCE. For point forecasts, Kelly skewness is preferred; but for estimating stochastic volatility, Bowley skewness is preferred. An examination of goods and services PCE inflation provides similar inference.
    Keywords: median PCE inflation; trimmed-mean PCE; disaggregate inflation; skewness; forecasting
    JEL: E31 E37 E52
    Date: 2022–08–03
  12. By: Gregory Benton; Wesley J. Maddox; Andrew Gordon Wilson
    Abstract: A broad class of stochastic volatility models are defined by systems of stochastic differential equations. While these models have seen widespread success in domains such as finance and statistical climatology, they typically lack an ability to condition on historical data to produce a true posterior distribution. To address this fundamental limitation, we show how to re-cast a class of stochastic volatility models as a hierarchical Gaussian process (GP) model with specialized covariance functions. This GP model retains the inductive biases of the stochastic volatility model while providing the posterior predictive distribution given by GP inference. Within this framework, we take inspiration from well studied domains to introduce a new class of models, Volt and Magpie, that significantly outperform baselines in stock and wind speed forecasting, and naturally extend to the multitask setting.
    Date: 2022–07
  13. By: Giovanni Angelini; Giuseppe Cavaliere; Enzo D'Innocenzo; Luca De Angelis
    Abstract: In this paper we propose a new time-varying econometric model, called Time-Varying Poisson AutoRegressive with eXogenous covariates (TV-PARX), suited to model and forecast time series of counts. {We show that the score-driven framework is particularly suitable to recover the evolution of time-varying parameters and provides the required flexibility to model and forecast time series of counts characterized by convoluted nonlinear dynamics and structural breaks.} We study the asymptotic properties of the TV-PARX model and prove that, under mild conditions, maximum likelihood estimation (MLE) yields strongly consistent and asymptotically normal parameter estimates. Finite-sample performance and forecasting accuracy are evaluated through Monte Carlo simulations. The empirical usefulness of the time-varying specification of the proposed TV-PARX model is shown by analyzing the number of new daily COVID-19 infections in Italy and the number of corporate defaults in the US.
    Date: 2022–07
  14. By: Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Xiaojin Sun (Department of Economics and Finance, University of Texas at El Paso, USA)
    Abstract: We build in the time-varying parameter feature into the Sims et al. (2020) four-equation Dynamic Stochastic General Equilibrium (DSGE) model in this paper. We find that both parameters and impulse responses of the variables in the four-equation DSGE model exhibit significant variation over time. Allowing model parameters to vary over time also improves the model's forecasting performance.
    Keywords: Four-Equation DSGE, Time-Varying Parameter, Forecasting
    JEL: E32 C52 C53
    Date: 2022–08
  15. By: Mr. Futoshi Narita; Mr. Sakai Ando
    Abstract: Minimum trace reconciliation, developed by Wickramasuriya et. al. (2019), is an innovation in the literature of forecast reconciliation. The proof, however, is indirect and not easy to extend to more general situations. This paper provides an alternative proof based on the first-order condition in the space of non-square matrix and argues that it is not only simpler but also can be extended to incorporate more general results on minimum weighted trace reconciliation in Panagiotelis et. al. (2021). Thus, our alternative proof not only has pedagogical value but also connects the results in the literature from a unified perspective.
    Keywords: Forecast Reconciliation; Minimum Trace Reconciliation; Hierarchical Time Series
    Date: 2022–07–08

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