nep-for New Economics Papers
on Forecasting
Issue of 2023‒01‒09
five papers chosen by
Rob J Hyndman
Monash University

  1. Machine Learning Algorithms for Time Series Analysis and Forecasting By Rameshwar Garg; Shriya Barpanda; Girish Rao Salanke N S; Ramya S
  2. Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies By Jiawen Luo; Oguzhan Cepni; Riza Demirer; Rangan Gupta
  3. Quantile regression analysis to predict GDP distribution using data from the US and UK By Thi Huyen Tran; Robert Ślepaczuk
  4. Predicting Chinese consumption series with Baidu By Zhongchen Song; Tom Coupé
  5. Score-based calibration testing for multivariate forecast distributions By Malte Kn\"uppel; Fabian Kr\"uger; Marc-Oliver Pohle

  1. By: Rameshwar Garg; Shriya Barpanda; Girish Rao Salanke N S; Ramya S
    Abstract: Time series data is being used everywhere, from sales records to patients' health evolution metrics. The ability to deal with this data has become a necessity, and time series analysis and forecasting are used for the same. Every Machine Learning enthusiast would consider these as very important tools, as they deepen the understanding of the characteristics of data. Forecasting is used to predict the value of a variable in the future, based on its past occurrences. A detailed survey of the various methods that are used for forecasting has been presented in this paper. The complete process of forecasting, from preprocessing to validation has also been explained thoroughly. Various statistical and deep learning models have been considered, notably, ARIMA, Prophet and LSTMs. Hybrid versions of Machine Learning models have also been explored and elucidated. Our work can be used by anyone to develop a good understanding of the forecasting process, and to identify various state of the art models which are being used today.
    Date: 2022–11
  2. By: Jiawen Luo (School of Business Administration, South China University of Technology, Guangzhou, China); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelænshaven 16A, Frederiksberg DK-2000, Denmark); Riza Demirer (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: We propose a procedure to forecast the realized covariance matrix for a given set of assets via spectral decomposition within a multivariate heterogeneous autoregressive (MHAR) framework. Utilizing high-frequency data for the U.S. aggregate and industry indexes and a large set of exogenous predictors that include financial, macroeconomic, sentiment, and climate-based factors, we evaluate the out-of-sample performance of industry portfolios constructed from forecasted realized covariance matrices across various univariate and multivariate forecasting models. While the climate and sentiment-based forecasting models generally yield more accurate forecasts of realized covariance compared to the macroeconomic and financial based models, particularly at the short forecast horizon, we find that the models that include industry-level information, generally yield better economic outcomes, in line with the established evidence of the predictive information captured at the industry level. Our results suggest that the MHAR framework coupled with DRD decomposition that splits the covariance matrix into a diagonal matrix of realized variances and realized correlations, can be utilized in a high-frequency setting to implement diversification and smart beta strategies for various investment horizons; however, the choice of the predictors should be aligned with the target investment horizon.
    Keywords: Volatility forecasting, Multivariate HAR model, Forecast evaluation, Beta forecasting, Economic analysis
    JEL: C32 C53 G10 G11
    Date: 2022–12
  3. By: Thi Huyen Tran (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group); Robert Ślepaczuk (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)
    Abstract: This paper aims to find the best models to forecast one-quarter-ahead and one-year-ahead US and UK real GDP growth distributions by employing quantile regression with skewed-t distribution on different sets of relevant near-term predictors. The research data period starts in 1947Q1/1955Q1 for US/UK data and ends in 2021Q3/2020Q4 for one-quarter-ahead/one-year-ahead prediction. The out-of-sample period ranges from 1996Q3 to 2021Q3 for one-quarter-ahead prediction and to 2020Q4 for one-year-ahead forecasting. The author applies a two-step testing procedure, in which models with the lowest average error in out-of-sample period are selected to the next step where the cumulative distribution functions of probability integral transforms are computed for the out-of-sample period, to select the best models. The improvement in the final forecasts of the tested models results, among others, from the use of new macroeconomic data with a higher frequency and focusing on the specific properties of the tested models separately for the US and UK. The chosen best models indicate that there exist better models than the model proposed by Adrian et al. (2016) to predict US growth distributions and that near-term predictors can produce good UK growth forecasts. Additionally, some simplified models associated with significantly lower portion of model risk are detected to produce meaningful forecasts for both US and UK case. For the US data, there exist several models that can produce timely predicted results.
    Keywords: GDP growth, density forecast, quantile regression, US GDP, UK GDP, cumulative distribution function, probability integral transform, out-of-sample forecasting
    JEL: E01 E17 C15 C31 C52 C53 C54 C58 F43
    Date: 2022
  4. By: Zhongchen Song; Tom Coupé (University of Canterbury)
    Abstract: There is a substantial literature that suggests that search behavior data from Google Trends can be used for both private and public sector decision-making. In this paper, we use search behavior data from Baidu, the internet search engine most popular in China, to analyze whether these can improve nowcasts and forecasts of the Chinese economy. Using a wide variety of estimation and variable selection procedures, we find that Baidu’s search data can improve nowcast and forecast performance of the sales of automobiles and mobile phones reducing forecast errors by more than 10%, as well as reducing forecast errors of total retail sales of consumptions goods in China by more than 40%. Google Trends data, in contrast, do not improve performance.
    Keywords: China, Baidu Index, Google Trends, forecasting, consumption.
    JEL: C53 E21 E27
    Date: 2022–12–01
  5. By: Malte Kn\"uppel; Fabian Kr\"uger; Marc-Oliver Pohle
    Abstract: Multivariate distributional forecasts have become widespread in recent years. To assess the quality of such forecasts, suitable evaluation methods are needed. In the univariate case, calibration tests based on the probability integral transform (PIT) are routinely used. However, multivariate extensions of PIT-based calibration tests face various challenges. We therefore introduce a general framework for calibration testing in the multivariate case and propose two new tests that arise from it. Both approaches use proper scoring rules and are simple to implement even in large dimensions. The first employs the PIT of the score. The second is based on comparing the expected performance of the forecast distribution (i.e., the expected score) to its actual performance based on realized observations (i.e., the realized score). The tests have good size and power properties in simulations and solve various problems of existing tests. We apply the new tests to forecast distributions for macroeconomic and financial time series data.
    Date: 2022–11

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