nep-for New Economics Papers
on Forecasting
Issue of 2020‒02‒17
nine papers chosen by
Rob J Hyndman
Monash University

  1. Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial By Mahmut Gunay
  2. Nowcasting Real GDP Growth: Comparison between Old and New EU Countries By Evzen Kocenda; Karen Poghosyan
  3. Time-Varying Spillover of US Trade War on the Growth of Emerging Economies By Oguzhan Cepni; David Gabauer; Rangan Gupta; Khuliso Ramabulana
  4. Individual Forecaster Perceptions of the Persistence of Shocks to GDP By Michael P. Clements
  5. Forecasting NIFTY 50 benchmark Index using Seasonal ARIMA time series models By Amit Tewari
  6. Advance Layoff Notices and Labor Market Forecasting By Pawel Krolikowski; Kurt Graden Lunsford
  7. Generalized Forecasr Averaging in Autoregressions with a Near Unit Root By Mohitosh Kejriwal; Xuewen Yu
  8. A Data-Rich Measure of Underlying Inflation for Brazil By Vicente da Gama Machado; Raquel Nadal; Fernando Ryu Ramos Kawaoka
  9. Introducing Macro-Financial Variables into a Semi-Structural Model By Dominika Ehrenbergerova; Simona Malovana

  1. By: Mahmut Gunay
    Abstract: In this paper, we analyze short-term forecasts of Turkish GDP growth using Mixed DAta Sampling (MIDAS) approach. We consider six alternatives for functional form of the lag polynomial in the MIDAS equation, five to twelve lags of the explanatory high frequency variables and produce short-term forecasts for nine forecast horizons starting with the release of data for six months before the start of the target quarter to the release of the data for the last month of the quarter. Our results indicate that functional form of the lag polynomials play non-negligible role on the short-term forecast performance but a specific functional form does not perform globally well for all forecast horizons, for all lag lengths or for all indicators. Import quantity indices perform relatively better until first month’s data for the target quarter become available. As data accumulate for the monthly indicators for the target quarter, real domestic turnover and industrial production indicators stand out in terms of short-term forecasting performance. When all of the three months’ realizations for the monthly indicators become available for the quarter that we want to forecast, unrestricted MIDAS type equations with around five lags with real domestic turnover and industrial production indicators track the GDP growth relatively successfully.
    Keywords: GDP, Forecasting, MIDAS, Polynomial form
    JEL: C53 E37
    Date: 2020
  2. By: Evzen Kocenda (Institute of Economic Studies, Faculty of Social Sciences, Charles University in Prague, Czech Republic. Address: IES, Opletalova 26, 110 00 Prague.; Institute of Theory of Information and Automation, Czech Academy of Sciences, Prague; CESifo Munich; IOS Regensburg.); Karen Poghosyan (Central Bank of Armenia, Economic Research Department, V. Sargsyan 6, 0010, Yerevan, Armenia)
    Abstract: We analyze the performance of a broad range of nowcasting and short-term forecasting models for a representative set of twelve old and six new member countries of the European Union (EU) that are characterized by substantial differences in aggregate output variability. In our analysis, we generate ex-post out-of-sample nowcasts and forecasts based on hard and soft indicators that come from a comparable set of identical data. We show that nowcasting works well for the new EU countries because, although that variability in their GDP growth data is larger than that of the old EU economies, the economic significance of nowcasting is on average somewhat larger.
    Keywords: Bayesian VAR, dynamic and static principal components, European OECD countries, factor augmented VAR, nowcasting, real GDP growth, short-term forecasting
    JEL: C33 C38 C52 C53 E37 E52
    Date: 2020–02
  3. By: Oguzhan Cepni (Central Bank of the Republic of Turkey, Anafartalar Mah. Istiklal Cad. No:10 06050 Ankara, Turkey); David Gabauer (Business and Management, Webster Vienna Private University, Praterstraße 23, 1020 Vienna, Austria); Rangan Gupta (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa); Khuliso Ramabulana (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)
    Abstract: In the wake of an unprecedented increase in the trade policy-related uncertainty of the US since 2017, we analyze the ability of a newspaper-based trade policy uncertainty index of the US in predicting the growth rate of emerging market economies using a novel multivariate time-varying causality framework. We provide overwhelming evidence of the role of trade uncertainty in negatively impacting the growth of emerging markets in a statistically significant manner, with the effect being on the rise since the Great Recession. Our results are robust to the usage of an alternative econometric methodology, metric of trade uncertainty, and also over an out-of-sample forecasting exercise. Policy conclusions of our results are discussed.
    Keywords: Trade Policy Uncertainty of the United States, Time-Varying Multivariate Causality, Emerging Market Economies
    JEL: C32 E32
    Date: 2020–01
  4. By: Michael P. Clements (ICMA Centre, Henley Business School, University of Reading)
    Abstract: We analyze individual professional forecasters' beliefs concerning the persistence of GDP shocks. Despite substantial apparent heterogeneity in perceptions, with around one half of the sample of professional forecasters believing shocks do not have permanent effects, we show that these apparent differences may be largely due to short-samples and survey respondents being active at different times. When we control for these effects, using a bootstrap, we formally do not reject the null that individuals' long-horizon expectations are interchangeable at a given point in time. When we apply the same bootstrap approach to their medium-term expectations, we do reject the null. We explore this difference between long and medium-horizon forecasts by decomposing revisions in forecasts into permanent and transitory components.
    Keywords: long-horizon forecasts, output persistence, heterogeneous beliefs, bootstrap test
    JEL: C53 C55 C83 E32
    Date: 2020–01
  5. By: Amit Tewari
    Abstract: This paper analyses how Time Series Analysis techniques can be applied to capture movement of an exchange traded index in a stock market. Specifically, Seasonal Auto Regressive Integrated Moving Average (SARIMA) class of models is applied to capture the movement of Nifty 50 index which is one of the most actively exchange traded contracts globally [1]. A total of 729 model parameter combinations were evaluated and the most appropriate selected for making the final forecast based on AIC criteria [8]. NIFTY 50 can be used for a variety of purposes such as benchmarking fund portfolios, launching of index funds, exchange traded funds (ETFs) and structured products. The index tracks the behaviour of a portfolio of blue chip companies, the largest and most liquid Indian securities and can be regarded as a true reflection of the Indian stock market [2].
    Date: 2020–01
  6. By: Pawel Krolikowski; Kurt Graden Lunsford
    Abstract: We collect rich establishment-level data about advance layoff notices filed under the Worker Adjustment and Retraining Notification (WARN) Act since January 1990. We present in-sample evidence that the number of workers affected by WARN notices leads state-level initial unemployment insurance claims, changes in the unemployment rate, and changes in private employment. The effects are strongest at the one and two-month horizons. After aggregating state-level information to a national-level “WARN factor” using a dynamic factor model, we find that the factor substantially improves out-of-sample forecasts of changes of manufacturing employment in real time.
    Keywords: WARN act; mass layoffs; plant closings; unemployment; employment; initial UI claims.
    JEL: E27 J65 K31
    Date: 2020–01–31
  7. By: Mohitosh Kejriwal; Xuewen Yu
    Abstract: This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging.
    JEL: C22
    Date: 2019–12
  8. By: Vicente da Gama Machado; Raquel Nadal; Fernando Ryu Ramos Kawaoka
    Abstract: This paper proposes a new measure of underlying inflation for Brazil based on a generalized dynamic factor model (GDFM). The approach summarizes a wide set of indicators, which the Banco Central do Brasil (BCB) regularly monitors in its assessment of the inflation scenario, such as data on prices, activity, financial and monetary variables. Differently from most core inflation approaches, the model takes account of the time series dimension – by extracting the lower frequency component – as well as the cross-section dimension and is able to handle end-of-sample unbalances. To our knowledge, it is the first application of this procedure for Brazil. The resulting series exhibits lower variability, unbiasedness and a relatively good forecasting performance compared to various other measures of trend inflation. Overall, the findings suggest the novel underlying inflation measure may be an important complement to the information set used by the BCB.
  9. By: Dominika Ehrenbergerova; Simona Malovana
    Abstract: This paper outlines a flexible and consistent model-based framework suitable for forecasting selected macro-financial variables of the Czech economy and conducting policy analysis to support the decision-making process. We enhance an existing semi-structural model of the Czech economy in order to replicate some of the characteristics of the financial cycle, i.e. co-movement between credit and house prices, higher persistence of respective macro-financial variables and a pronounced impact of shocks on the business cycle.
    Keywords: Financial cycle, forecasting, macro-financial variables, semi-structural model
    JEL: C32 E47 E58 G21
    Date: 2019–12

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