nep-for New Economics Papers
on Forecasting
Issue of 2021‒01‒04
seven papers chosen by
Rob J Hyndman
Monash University

  1. Political Budget Forecast cycles By Frank Bohn; Francisco José Veiga
  2. Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area By Chalmovianský, Jakub; Porqueddu, Mario; Sokol, Andrej
  3. Adaptative predictability of stock market returns By Lopes Moreira Da Veiga, María Helena; Mao, Xiuping; Casas Villalba, Maria Isabel
  4. Forecasting expected and unexpected losses By Juselius, Mikael; Tarashev, Nikola
  5. Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects By Jiti Gao; Fei Liu; Bin peng
  6. Forecasting Value-at-Risk and Expected Shortfall in Large Portfolios: a General Dynamic Factor Approach By Marc Hallin; Carlos Trucíos
  7. Out of sample predictability in predictive regressions with many predictor candidates By Pitarakis, Jean-Yves; Gonzalo, Jesús

  1. By: Frank Bohn (Radboud University); Francisco José Veiga (Universidade do Minho and NIPE)
    Abstract: By forecasting overly optimistic revenues opportunistic governments can increase spending in order to appear more competent prior to elections. Ex post deficits emerge in election years, thereby producing political forecast cycles - as also found for US states in the empirical literature. In our theoretical moral hazard model we obtain three additional results which are tested with panel data for Portuguese municipalities. The extent of manipulations is reduced when (i) the winning margin is expected to widen; (ii) the incumbent is not re-running; and/or (iii) the share of informed voters (proxied by education) goes up.
    Keywords: opportunistic political cycles; political budget cycles; revenue forecasts; deficit; transfers; asymmetric information; political economy.
    JEL: D72 H68 E32
    Date: 2019–06
  2. By: Chalmovianský, Jakub; Porqueddu, Mario; Sokol, Andrej
    Abstract: We compare direct forecasts of HICP and HICP excluding energy and food in the euro area and five member countries to aggregated forecasts of their main components from large Bayesian VARs with a shared set of predictors. We focus on conditional point and density forecasts, in line with forecasting practices at many policy institutions. Our main findings are that point forecasts perform similarly using both approaches, whereas directly forecasting aggregate indices tends to yield better density forecasts. In the aftermath of the Great Financial Crisis, relative forecasting performance was typically only affected temporarily. Inflation forecasts made by Eurosystem/ECB staff perform similarly or slightly better than those from our models for the euro area. JEL Classification: C11, C32, C53, E37
    Keywords: aggregation, Bayesian VAR model, inflation forecasting
    Date: 2020–12
  3. By: Lopes Moreira Da Veiga, María Helena; Mao, Xiuping; Casas Villalba, Maria Isabel
    Abstract: We revisit the stock market return predictability using the variance risk premium and conditional variance as predictors of classical predictive regressions and time-varying coefficient predictive regressions. Also, we propose three new models to forecast the conditional variance and estimate the variance risk premium. Our empirical results show, first, that the flexibility provided by time-varying coefficient regressions often improve the ability of the variance risk premium, the conditional variance, and other control variables to predict stock market returns. Second, the conditional variance and variance risk premium obtained from varying coefficient models perform consistently well at predicting stock market returns. Finally, the time-varying coefficient predictive regressions show that the variance risk premium is a predictor of stock market excess returns before the global financial crisis of 2007, but its predictability decreases in the post global financial crisis period at the 3-month horizon. At the 12-month horizon, both the variance risk premium and conditional variance are predictors of stock excess returns during most of 2000-2015.
    Keywords: Variance Risk Premium; Time-Varying Coefficient Predictive Regressions; Time-Varying Coefficient Har-Type Models; Realized Variance; Predictability; Nonparametric Methods
    JEL: G1 C53 C52 C51 C22
    Date: 2020–12–18
  4. By: Juselius, Mikael; Tarashev, Nikola
    Abstract: Extending a standard credit-risk model illustrates that a single factor can drive both expected losses and the extent to which they may be exceeded in extreme scenarios, ie “unexpected losses.” This leads us to develop a framework for forecasting these losses jointly. In an application to quarterly US data on loan charge-offs from 1985 to 2019, we find that financial-cycle indicators – notably, the debt service ratio and credit-to-GDP gap – deliver reliable real-time forecasts, signalling turning points up to three years in advance. Provisions and capital that reflect such forecasts would help reduce the procyclicality of banks’ loss-absorbing resources.
    JEL: G17 G21 G28
    Date: 2020–12–21
  5. By: Jiti Gao; Fei Liu; Bin peng
    Abstract: In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we establish a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and achieve the asymptotic distributions accordingly. In addition, we conduct intensive simulations to examine the theoretical findings. In the empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis. By implementing rolling-window based out–of– sample forecasts, we show the finite–sample performance and demonstrate the practical relevance of the proposed model and estimation method.
    Keywords: binary response, heterogeneous panel, interactive fixed effects, portfolio analysis
    JEL: C18 C23 G11
    Date: 2020
  6. By: Marc Hallin; Carlos Trucíos
    Abstract: Beyond their importance from a regulatory policy point of view, Value-at-Risk (VaR) and Expected Shortfall (ES) play an important role in risk management, portfolio allocation, capital level requirements, trading systems, and hedging strategies. Unfortunately, due to the curse of dimensionality, their accurate estimation in large portfolios is quite a challenge. To tackle this problem, we propose a filtered historical simulation method in which high-dimensional conditional covariance matrices are estimated via a general dynamic factor model with infinite-dimensional factor space and conditionally heteroscedastic factors. The procedure is applied to a panel with concentration ratio close to one. Back-testing and scoring results indicate that both VaR and ES are accurately estimated under our method, which outperforms alternative approaches available in the literature.
    Keywords: conditional covariance; high-dimensional time series; large panels; risk measures; volatility
    JEL: C10 C32 C53 G17 G32
    Date: 2020–12
  7. By: Pitarakis, Jean-Yves; Gonzalo, Jesús
    Abstract: This paper is concerned with detecting the presence of out of sample predictability in linear predictive regressions with a potentially large set of candidate predictors. We propose a procedure based on out of sample MSE comparisons that is implementedin a pairwise manner using one predictor at a time and resulting in an aggregate test statistic that is standard normally distributed under the none hypothesis of no linear predictability. Predictors can be highly persistent, purely stationary or a combination of both. Upon rejection of the none hypothesis we subsequently introduce a predictor screening procedure designed to identify the most active predictors.
    Keywords: High Dimensional Predictability; Predictive Regressions; Forecasting
    JEL: C53 C52 C32 C12
    Date: 2020–12–09

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