nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒06‒05
eleven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Generalised partial autocorrelations and the mutual information between past and future By Tommaso Proietti; Alessandra Luati
  2. Counting Biased Forecasters: An Application of Multiple Testing Techniques By Fabiana Gomez; David Pacini
  3. Dating Business Cycle Turning Points for the French Economy: a MS-DFM approach By Catherine Doz; Anna Petronevich
  4. Autoregressive Spatial Spectral Estimates By Abhimanyu Gupta
  5. Granger causality and regime inference in Bayesian Markov-Switching VARs By Droumaguet, Matthieu; Warne, Anders; Woźniak, Tomasz
  6. Expectation-driven cycles: time-varying effects By D'Agostino, Antonello; Mendicino, Caterina
  7. Forecasting with VAR models: fat tails and stochastic volatility By Chiu, Ching-Wai (Jeremy); Mumtaz, Haroon; Pinter, Gabor
  8. Identifying Noise Shocks: a VAR with Data Revisions By Riccardo M. Masolo; Alessia Paccagnini
  9. Moment Conditions for AR(1) Panel Data Models with Missing Outcomes By David Pacini; Frank Windmeijer
  10. Crowdsourcing of Economic Forecast – Combination of Forecasts Using Bayesian Model Averaging By Dongkoo Kim; Tae-hwan Rhee; Keunkwan Ryu; Changmock Shin
  11. Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis By Forni, Mario; Hallin, Marc; Lippi, Marco; Zaffaroni, Paolo

  1. By: Tommaso Proietti (University of Rome “Tor Vergata” and Creates); Alessandra Luati (University of Bologna)
    Abstract: The paper introduces the generalised partial autocorrelation (GPAC) coefficients of a stationary stochastic process. The latter are related to the generalised autocovariances, the inverse Fourier transform coefficients of a power transformation of the spectral density function. By interpreting the generalized partial autocorrelations as the partial autocorrelation coefficients of an auxiliary process, we derive their properties and relate them to essential features of the original process. Based on a parameterisation suggested by Barndorff-Nielsen and Schou (1973) and on Whittle likelihood, we develop an estimation strategy for the GPAC coefficients. We further prove that the GPAC coefficients can be used to estimate the mutual information between the past and the future of a time series.
    Keywords: Generalised autocovariance, Spectral models, Whittle likelihood, Reparameterisation
    JEL: C22 C52
    Date: 2015–05–25
  2. By: Fabiana Gomez; David Pacini
    Abstract: We investigate the problem of counting biased forecasters among a group of unbiased and biased forecasters of macroeconomic variables. The innovation is to implement a procedure controlling for the expected proportion of unbiased forecasters that could be erroneously classified as biased (i.e., the false discovery rate). Monte Carlo exercises illustrate the relevance of controlling the false discovery rate in this context. Using data from the Survey of Professional Forecasters, we find that up to 7 out of 10 forecasters classified as biased by a procedure not controlling the false discovery rate may actually be unbiased.
    Keywords: Biased Forecasters, Multiple Testing, False Discovery Rate.
    JEL: C12 C23 E17
    Date: 2015–05–27
  3. By: Catherine Doz (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics); Anna Petronevich (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS, EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics)
    Abstract: The official institutions (NBER, OECD, CEPR and others) provide business cycle chronology with a lag from 3 months up to several years. Markov-Switching Dynamic Factor Model (MS-DFM) allows to produce the turning points more timely. The Kalman filter estimates of the model can be obtained in one step with limited number of series or in two steps on a much richer dataset. While the choice of correct series is a challenge for the one-step method, the problem of the two-step method is the potential misspecification. In this paper we apply one-step and two-step approaches to the French data and compare their performance. Both methods give qualitatively similar results and prove to reproduce the OECSD business cycle chronology on the 1993-2014 monthly sample well. We find that the two-step method is more precise in determining the beginnings and the ends of recessions. Also, both methods produce extra signals corresponding to downturns which were too short to belong to OECD chronology of recessions.
    Date: 2015–02
  4. By: Abhimanyu Gupta
    Abstract: Autoregressive spectral density estimation for stationary random fields on a regular spatial lattice has the advantage of providing a guaranteed positive-definite estimate even when suitable edge-effect correction is employed.We consider processes with a half-plane infinite autoregressive representation and use truncated versions of this to estimate the spectral density. The truncation length is allowed to diverge in all dimensions in order to avoid the potential bias which would accrue due to truncation at a fixed lag-length. Consistency and strong consistency of the proposed estimator, both uniform in frequencies, are established. Under suitable conditions the asymptotic distribution of the estimate is shown to be zero-mean normal and independent at fixed distinct frequencies, mirroring the behaviour for time series. The key to the results is the covariance structure of stationary random fields defined on regularly spaced lattices. We study this in detail and show the covariance matrix to satisfy a generalization of the Toeplitz property familiar from time series analysis. A small Monte Carlo experiment examines finite sample performance.
    Date: 2015–05–01
  5. By: Droumaguet, Matthieu; Warne, Anders; Woźniak, Tomasz
    Abstract: We derive restrictions for Granger noncausality in Markov-switching vector autoregressive models and also show under which conditions a variable does not affect the forecast of the hidden Markov process. Based on Bayesian approach to evaluating the hypotheses, the computational tools for posterior inference include a novel block Metropolis-Hastings sampling algorithm for the estimation of the restricted models. We analyze a system of monthly US data on money and income. The test results in MS-VARs contradict those in linear VARs: the money aggregate M1 is useful for forecasting income and for predicting the next period’s state. JEL Classification: C11, C12, C32, C53, E32
    Keywords: Bayesian hypothesis testing, block Metropolis-Hastings sampling, Markov-switching models, mixture models, posterior odds ratio
    Date: 2015–05
  6. By: D'Agostino, Antonello; Mendicino, Caterina
    Abstract: This paper provides new insights into expectation-driven cycles by estimating a structural VAR with time-varying coefficients and stochastic volatility, as in Cogley and Sargent (2005) and Primiceri (2005). We use survey-based expectations of the unemployment rate to measure expectations of future developments in economic activity. We find that the effect of expectation shocks on the realized unemployment rate have been particularly large during the most recent recession. Unanticipated changes in expectations contributed to the gradual increase in the persistence of the unemployment rate and to the decline in the correlation between the inflation and the unemployment rate over time. Our results are robust to the introduction of financial variables in the model. JEL Classification: C32, E24, E32
    Keywords: economic fluctuations, stochastic volatility, survey expectations, time varying vector autoregression
    Date: 2015–04
  7. By: Chiu, Ching-Wai (Jeremy) (Bank of England); Mumtaz, Haroon (Queen Mary University of London); Pinter, Gabor (Bank of England)
    Abstract: In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature Student’s t distribution and time-varying variance. We estimate this model using US data on output growth, inflation, interest rates and stock returns. In terms of in-sample fit, the VAR model featuring both stochastic volatility and t-distributed disturbances outperforms restricted alternatives that feature either attributes. The VAR model with t disturbances results in density forecasts for industrial production and stock returns that are superior to alternatives that assume Gaussianity, and this difference is especially stark over the recent Great Recession. Further international evidence confirms that accounting for both stochastic volatility and Student’s t-distributed disturbances may lead to improved forecast accuracy.
    Keywords: Bayesian VAR; fat-tails; stochastic volatility; Great Recession
    JEL: C11 C32 C52
    Date: 2015–05–29
  8. By: Riccardo M. Masolo (Bank of England; Centre for Macroeconomics (CFM)); Alessia Paccagnini (Dipartimento di Economia, Metodi Quantitativi e Strategie d'Impresa (DEMS) Facoltà di Economia Università degli Studi di Milano-Bicocca)
    Abstract: We propose a new VAR identification strategy to study the impact of noise shocks on aggregate activity. We do so exploiting the informational advantage the econometrician has, relative to the economic agent. The latter, who is uncertain about the underlying state of the economy, responds to the noisy early data releases. The former, with the benefit of hindsight, has access to data revisions as well, which can be used to identify noise shocks. By using a VAR we can avoid making very specific assumptions on the process driving data revisions. We rather remain agnostic about it but make our identification strategy robust to whether data revisions are driven by noise or news. Our analysis shows that a surprising report of output growth numbers delivers a persistent and hump-shaped response of real output and unemployment. The responses are qualitatively similar but an order of magnitude smaller than those to a demand shock. Finally, our counterfactual analysis supports the view that it would not be possible to identify noise shocks unless different vintages of data are used.
    Keywords: Noise Shocks, Data Revisions, VAR, Impulse-Response Functions
    JEL: E3 C1 D8
    Date: 2015–05
  9. By: David Pacini; Frank Windmeijer
    Abstract: We derive moment conditions for dynamic, AR(1) panel data models when values of the outcome variable are missing. In this context, commonly used estimators only use data on individuals observed for at least three consecutive periods. We derive moment conditions for observations with at least three non-consecutive observations for estimation of the parameters by GMM.
    Keywords: Panel Data, Missing Values.
    JEL: C33 C51
    Date: 2015–05–26
  10. By: Dongkoo Kim; Tae-hwan Rhee; Keunkwan Ryu; Changmock Shin
    Abstract: Economic forecasts are quite essential in our daily lives, which is why many research institutions periodically make and publish forecasts of main economic indicators. We ask (1) whether we can consistently have a better prediction when we combine multiple forecasts of the same variable and (2) if we can, what will be the optimal method of combination. We linearly combine multiple linear combinations of existing forecasts to form a new forecast (“combination of combinations”), and the weights are given by Bayesian model averaging. In the case of forecasts on Germany’s real GDP growth rate, this new forecast dominates any single forecast in terms of root-mean-square prediction errors.
    Keywords: Combination of forecasts; Bayesian model averaging
    JEL: E32 E37
    Date: 2015–03
  11. By: Forni, Mario; Hallin, Marc; Lippi, Marco; Zaffaroni, Paolo
    Abstract: Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional factor space. A Monte Carlo experiment corroborates those theoretical results and demonstrates the excellent performance of those estimators in out-of-sample forecasting.
    Keywords: Consistency and rates.; Generalized dynamic factor models.; High -dimensional time series.; One-sided representations of dynamic factor models.; Vector processes with singular spectral density
    JEL: C0 C01 E0
    Date: 2015–05

This nep-ets issue is ©2015 by Yong Yin. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.