nep-for New Economics Papers
on Forecasting
Issue of 2021‒11‒01
six papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Inflation and Output Growth with Credit-Card-Augmented Divisia Monetary Aggregates By William Barnett; Sohee Park
  2. Forecasting International REITs Volatility: The Role of Oil-Price Uncertainty By Jiqian Wang; Rangan Gupta; Oguzhan Cepni; Feng Ma
  3. Forecasting Financial Market Structure from Network Features using Machine Learning By Douglas Castilho; Tharsis T. P. Souza; Soong Moon Kang; Jo\~ao Gama; Andr\'e C. P. L. F. de Carvalho
  4. The ENSO Cycle and Forecastability of Global Inflation and Output Growth: Evidence from Standard and Mixed-Frequency Multivariate Singular Spectrum Analyses By Hossein Hassani; Mohammad Reza Yeganegi; Rangan Gupta
  5. Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach By Nuttanan Wichitaksorn
  6. Nowcasting India's Quarterly GDP Growth: A Factor Augmented Time-Varying Coefficient Regression Model (FA-TVCRM). By Bhattacharya, Rudrani; Bhandari, Bornali; Mundle, Sudipto

  1. By: William Barnett (Department of Economics, University of Kansas and Center for Financial Stability, New York City); Sohee Park (Department of Economics, University of Kansas)
    Abstract: This paper investigates the performance of the Credit-Card-Augmented Divisia monetary aggregates in forecasting U.S. inflation and output growth at the 12-month horizon. We compute recursive and rolling out-of-sample forecasts using an Autoregressive Distributed Lag (ADL) model based on Divisia monetary aggregates. We use the three available versions of those monetary aggregate indices, including the original Divisia aggregates, the credit card-augmented Divisia, and the credit-card-augmented Divisia inside money aggregates. The source of each is the Center for Financial Stability (CFS). We find that the smallest Root Mean Square Forecast Errors (RMSFE) are attained with the credit-card-augmented Divisia indices used as the forecast indicators. We also consider Bayesian vector autoregression (BVAR) for forecasting annual inflation and output growth.
    Keywords: Divisia, Credit-Card-Augmented Divisia, Monetary Aggregates, Forecasting, Bayesian vector autoregression, Inflation, Output Growth.
    JEL: C32 C53 E31 E47 E51
    Date: 2021–10
  2. By: Jiqian Wang (School of Economics and Management, Southwest Jiaotong University, Chengdu, China); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa); Oguzhan Cepni (Copenhagen Business School, Department of Economics, Porcelaenshaven 16A, Frederiksberg DK-2000, Denmark; Central Bank of the Republic of Turkey, Haci Bayram Mah. Istiklal Cad. No:10 06050, Ankara, Turkey); Feng Ma (School of Economics and Management, Southwest Jiaotong University, Chengdu, China)
    Abstract: We forecast realized variance (RV) of Real Estate Investment Trusts (REITs) for ten leading markets and regions, derived from 5-minutes-interval intraday data, based on the information content of two alternative metrics of daily oil-price uncertainty. Based on the period of the analysis covering January 2008 to July 2020, and using variants of the popular MIDAS-RV model, augmented to include oil market uncertainties, captured by its RV (also derived from 5-minute intraday data) and implied volatility (i.e., the oil VIX), we report evidence of significant statistical and economic gains in the forecasting performance. The result is robust to the size of the forecasting samples, including that of the COVID-19 period, jump risks, lag-length, nonlinearities, and asymmetric effects, and forecast horizon. Our results have important implications for investors and policymakers.
    Keywords: REITs, International data, Realized volatility, Oil-Price Uncertainty, Forecasting
    JEL: C22 C53 G15 Q02
    Date: 2021–10
  3. By: Douglas Castilho; Tharsis T. P. Souza; Soong Moon Kang; Jo\~ao Gama; Andr\'e C. P. L. F. de Carvalho
    Abstract: We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST) and Dynamic Threshold Networks (DTN). Experimental results show that the proposed model can forecast market structure with high predictive performance with up to $40\%$ improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.
    Date: 2021–10
  4. By: Hossein Hassani (The Research Institute of Energy Management and Planning (RIEMP), University of Tehran, No. 9, Ghods St., Tehran, Iran); Mohammad Reza Yeganegi (Department of Accounting, Central Tehran Branch, Islamic Azad University, Tehran, Iran); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa)
    Abstract: In this paper the role of the El Niño-Southern Oscillation (ENSO), measured by the Equatorial Southern Oscillation Index (EQSOI), is used to formally forecast the inflation and GDP growth rates of the United States (US), advanced (excluding the US) and emerging countries, as well as the world economy (barring the US). We rely on univariate and multivariate Singular Spectrum Analyses (SSA), as well as mixed-frequency version of the latter since the EQSOI is monthly, while GDP is available only at quarterly frequency unlike monthly inflation rates. We find statistically significant evidence of the ability of the EQSOI in forecasting inflation and GDP growth rates of the four economic blocs, though there are exceptions in terms of forecasting gains associated with inflation rate of emerging economies and the growth rate of the US. Our results have important implications for policymakers.
    Keywords: GDP growth, Inflation, ENSO, Forecastibility, Mixed-Frequency Multivariate SSA, Continuous Wavelet Transform
    JEL: C22 C32 E31 E32 E37 Q54
    Date: 2021–10
  5. By: Nuttanan Wichitaksorn
    Abstract: Macroeconomic data are an important piece of information in decision making for both the public and private sectors in Thailand. However, the release of key macroeconomic data, usually in a lower frequency such as quarterly, is not always in a timely manner. Using the higher frequency data such as monthly and daily to analyze or forecast the lower frequency data can mitigate the release timing effect. This study applies the mixed-frequency data approach to analyze and forecast Thai key macroeconomic data. The mixed data sampling regressions with various specifications are employed and implemented through some macroeconomic data such as gross domestic product and inflation. The results show that in most cases the mixed-frequency models outperform the autoregressive integrated moving average model, which we used as the benchmark model, even during the COVID-19 period. Some policy implications can also be drawn from the analysis.
    Keywords: Thai Macroeconomic Data; Mixed-frequency; Forecasting; Vector Autoregression; COVID-19
    JEL: C22 C32 C53 E17 E27
    Date: 2020–12
  6. By: Bhattacharya, Rudrani (National Institute of Public Finance and Policy); Bhandari, Bornali (National Council of Applied Economic Research); Mundle, Sudipto (National Council of Applied Economic Research)
    Abstract: Governments, central banks, private firms and others need high frequency information on the state of the economy for their decision making. However, a key indicator like GDP is only available quarterly and that too with a lag. Hence decision makers use high frequency daily, weekly or monthly information to project GDP growth in a given quarter. This method, known as nowcasting, which started out in advanced country central banks using bridge models. Nowcasting is now based on more advanced techniques, mostly dynamic factor models. In this paper we use a novel approach, a Factor Augmented Time Varying Coefficient Regression (FA-TVCR) model, which allows us to extract information from a large number of high frequency indicators and at the same time inherently addresses the issue of frequent structural breaks encountered in Indian GDP growth. One specification of the FA-TVCR model is estimated using 19 variables available for a long period starting in 2007-08:Q1. Another specification estimates the model using a larger set of 28 indicators available for a shorter period starting in 2015-16:Q1. Comparing our model with two alternative models, we find that the FA-TVCR model outperforms a DFM model in terms of both in-sample and out-of-sample RMSE. The RMSE of the ARIMA model is somewhat lower than the FA-TVCR model within the sample period but is higher than the out-of-sample of the FA-TVCR model. Further, comparing the predictive power of the three models using the Diebold-Mariano test, we find that FA-TVCR model out-performs DFM consistently. In terms of out-of-sample forecast accuracy both the FA-TVC model and the ARIMA model have the same predictive accuracy under normal conditions. However, the FA-TVCR model outperforms the ARIMA model when applied for nowcasting in periods of major shocks like the Covid-19 shock of 2020-21.
    Keywords: Nowcasting ; Quarterly Year-on-Year GDP growth ; State-Space Model, India
    JEL: C52 C53 O40
    Date: 2021–10

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