nep-for New Economics Papers
on Forecasting
Issue of 2017‒04‒23
four papers chosen by
Rob J Hyndman
Monash University

  1. A near optimal test for structural breaks when forecasting under square error loss By Tom Boot; Andreas Pick
  2. High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed By F. Lilla
  3. Constrained principal components estimation of large approximate factor models By Rachida Ouysse
  4. Forecasting Chilean Inflation with the Hybrid New Keynesian Phillips Curve: Globalisation, Combination, and Accuracy By Medel, Carlos A.

  1. By: Tom Boot (University of Groningen); Andreas Pick (Erasmus University Rotterdam, De Nederlandsche Bank and CESifo Institute)
    Abstract: We propose a near optimal test for structural breaks of unknown timing when the purpose of the analysis is to obtain accurate forecasts under square error loss. A bias-variance trade-off exists under square forecast error loss, which implies that small structural breaks should be ignored. We study critical break sizes, assess the relevance of the break location, and provide a test to determine whether modeling a break will improve forecast accuracy. Asymptotic critical values and near optimality properties are established allowing for a break under the null, where the critical break size varies with the break location. The results are extended to a class of shrinkage forecasts with our test statistic as shrinkage constant. Empirical results on a large number of macroeconomic time series show that structural breaks that are relevant for forecasting occur much less frequently than indicated by existing tests.
    Keywords: structural break test, forecasting, squared error loss
    JEL: C12 C53
    Date: 2017–04–18
  2. By: F. Lilla
    Abstract: Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism and the existence of bid-ask spread. Moreover, these data are not always available and, even if they are, the asset’s liquidity may be not sufficient to allow for frequent transactions. This paper considers different variants of these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumps in prices and leverage effects for volatility. Findings suggest that daily-data models are preferred to HF-data models at 5% and 1% VaR level. Specifically, independently from the data frequency, allowing for jumps in price (or providing fat-tails) and leverage effects translates in more accurate VaR measure.
    JEL: C58 C53 C22 C01 C13
    Date: 2017–04
  3. By: Rachida Ouysse (School of Economics, UNSW Business School, UNSW)
    Abstract: Principal components (PC) are fundamentally feasible for the estimation of large factor models because consistency can be achieved for any path of the panel dimensions. The PC method is however inefficient under cross-sectional dependence with unknown structure. The approximate factor model of Chamberlain and Rothschild [1983] imposes a bound on the amount of dependence in the error term. This article proposes a constrained principal components (Cn-PC) estimator that incorporates this restriction as external information in the PC analysis of the data. This estimator is computationally tractable. It doesn't require estimating large covariance matrices, and is obtained as PC of a regularized form of the data covariance matrix. The paper develops a convergence rate for the factor estimates and establishes asymptotic normality. In a Monte Carlo study, we find that the Cn-PC estimators have good small sample properties in terms of estimation and forecasting performances when compared to the regular PC and to the generalized PC method (Choi [2012]).
    Keywords: High dimensionality, unknown factors, principal components, cross-sectional correlation, shrinkage regression, out-of-sample forecasting
    JEL: C11 C13 C33 C53 C55
    Date: 2017–04
  4. By: Medel, Carlos A.
    Abstract: This article analyses the multihorizon predictive power of the Hybrid New Keynesian Phillips Curve (HNKPC) covering the period from 2000.1 to 2014.12, for the Chilean economy. A distinctive feature of this article is the use of a Global Vector Autoregression (GVAR) specification of the HNKPC to enforce an open economy version. Another feature is the use of direct measures of inflation expectations--Consensus Forecasts--differing from a fully-founded rational expectations model. The HNKPC point forecasts are evaluated using the Mean Squared Forecast Error (MSFE) statistic and statistically compared with several benchmarks, including combined forecasts. The results indicate that there is evidence to do not reject the hypothesis of the HNKPC for the Chilean economy, and it is also robust to alternative specifications. In predictive terms, the results show that in a sample previous to the global financial crisis, the evidence is mixed between atheoretical benchmarks and the HNKPC by itself or participating in a combined prediction. However, when the evaluation sample is extended to include a more volatile inflation period, the results suggest that the HNKPC (and combined with the random walk) delivers the most accurate forecasts at horizons comprised within a year. In the long-run the HNKPC deliver accurate results, but not enough to outperform the candidate statistical models.
    Keywords: New Keynesian Phillips Curve; inflation forecasts; out-of-sample comparisons; survey data; Global VAR; structured time-series models; forecast combinations
    JEL: C22 C26 C53 E31 E37
    Date: 2017–04–16

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