nep-ets New Economics Papers
on Econometric Time Series
Issue of 2019‒10‒28
thirteen papers chosen by
Jaqueson K. Galimberti
KOF Swiss Economic Institute

  1. Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models By Niko Hauzenberger; Florian Huber; Gary Koop; Luca Onorante
  2. Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models By Matteo Barigozzi; Matteo Luciani
  3. Trend, Seasonal, and Sectoral Inflation in the Euro Area By James H. Stock; Mark W. Watson
  4. Information, VARs and DSGE Models By Paul Levine; Joseph Pearlman; Stephen Wright; Bo Yang
  5. BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R By Nikolas Kuschnig; Lukas Vashold
  6. Inference of Binary Regime Models with Jump Discontinuities By Milan Kumar Das; Anindya Goswami; Sharan Rajani
  7. Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production By Licht, Adrian; Escribano, Álvaro; Blazsek, Szabolcs
  8. A new unit root analysis for testing hysteresis in unemployment By Yaya, OlaOluwa S; Ogbonna, Ephraim A; Furuoka, Fumitaka; Gil-Alana, Luis A.
  9. Forecasting under Long Memory and Nonstationarity By Uwe Hassler; Marc-Oliver Pohle
  10. Forecasting Observables with Particle Filters: Any Filter Will Do! By Patrick Leung; Catherine S. Forbes; Gael M Martin; Brendan McCabe
  11. Feature-based Forecast-Model Performance Prediction By Thiyanga S. Talagala; Feng Li; Yanfei Kang
  12. Forecasting Swiss Exports using Bayesian Forecast Reconciliation By Florian Eckert; Rob Hyndman; Anastasios Panagiotelis
  13. Forecast Reconciliation: A geometric View with New Insights on Bias Correction By Anastasios Panagiotelis; Puwasala Gamakumara; George Athanasopoulos; Rob J Hyndman

  1. By: Niko Hauzenberger; Florian Huber; Gary Koop; Luca Onorante
    Abstract: In this paper, we write the time-varying parameter regression model involving K explanatory variables and T observations as a constant coefficient regression model with TK explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, this specification does not restrict the form that the time-variation in coefficients can take. We develop computationally efficient Bayesian econometric methods based on the singular value decomposition of the TK regressors. In artificial data, we find our methods to be accurate and much faster than standard approaches in terms of computation time. In an empirical exercise involving inflation forecasting using a large number of predictors, we find our methods to forecast better than alternative approaches and document different patterns of parameter change than are found with approaches which assume random walk evolution of parameters.
    Date: 2019–10
  2. By: Matteo Barigozzi; Matteo Luciani
    Abstract: This paper considers estimation of large dynamic factor models with common and idiosyncratic trends by means of the Expectation Maximization algorithm, implemented jointly with the Kalman smoother. We show that, as the cross-sectional dimension $n$ and the sample size $T$ diverge to infinity, the common component for a given unit estimated at a given point in time is $\min(\sqrt n,\sqrt T)$-consistent. The case of local levels and/or local linear trends trends is also considered. By means of a MonteCarlo simulation exercise, we compare our approach with estimators based on principal component analysis.
    Date: 2019–10
  3. By: James H. Stock; Mark W. Watson
    Abstract: An unobserved components model with stochastic volatility is used to decompose aggregate Euro area HICP inflation into a trend, seasonal and irregular components. Estimates of the components based only on aggregate data are imprecise: the width of 68% error bands for the seasonally adjusted value of aggregate inflation is 1.0 percentage points in the final quarter of the sample. Estimates are more precise using a multivariate model for a 13-sector decomposition of aggregate inflation, which yields a corresponding error band that is roughly 40% narrower. Trend inflation exhibited substantial variability during the 2001-2018 period and this variability closely mirrored variation in real activity.
    Date: 2019–10
  4. By: Paul Levine (University of Surrey and CIMS); Joseph Pearlman (City University); Stephen Wright (Birkbeck College); Bo Yang (Swansea University)
    Abstract: How informative is a time series representation of a given vector of observables about the structural shocks and impulse response functions in a DSGE model? In this paper we refer to this econometrician's problem as “E-invertibility" and consider the corresponding information problem of the agents in the assumed DGP, the DSGE model, which we refer to as “A-invertibility" We consider how the general nature of the agents' signal extraction problem under imperfect information impacts on the econometrician's problem of attempting to infer the nature of structural shocks and associated impulse responses from the data. We also examine a weaker condition of recoverability. A general conclusion is that validating a DSGE model by comparing its impulse response functions with those of a data VAR is more problematic when we drop the common assumption in the literature that agents have perfect information as an endowment. We develop measures of approximate fundamentalness for both perfect and imperfect information cases and illustrate our results using analytical and numerical examples.
    JEL: C11 C18 C32 E32
    Date: 2019–10
  5. By: Nikolas Kuschnig (Vienna University of Economics and Business, Institute of Ecological Economics); Lukas Vashold (Vienna University of Economics and Business, Department of Economics)
    Abstract: Vector autoregression (VAR) models are widely used models for multivariate time series analysis, but often suffer from their dense parameterization. Bayesian methods are commonly employed as a remedy by imposing shrinkage on the model coefficients via informative priors, thereby reducing parameter uncertainty. The subjective choice of the informativeness of these priors is often criticized and can be alleviated via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models in a hierarchical fashion. It incorporates functionalities that permit addressing a wide range of research problems while retaining an easy-to-use and transparent interface. It features the most commonly used priors in the context of multivariate time series analysis as well as an extensive set of standard methods for analysis. Further functionalities include a framework for defining custom dummy-observation priors, the computation of impulse response functions, forecast error variance decompositions and forecasts.
    Keywords: Vector autoregression, VAR, Bayesian, multivariate, hierarchical, R, package
    JEL: C87 C30 C11
    Date: 2019–10
  6. By: Milan Kumar Das; Anindya Goswami; Sharan Rajani
    Abstract: We have developed a statistical technique to test the model assumption of binary regime switching extension of the geometric L\'{e}vy process (GLP) by proposing a new discriminating statistics. The statistics is sensitive to the transition kernel of the regime switching model. With this statistics, given a time series data, one can test the hypothesis on the nature of regime switching. Furthermore, we have implemented this statistics for testing the regime switching hypothesis with Indian sectoral indices and have reported the result here. The result shows a clear indication of presence of multiple regimes in the data.
    Date: 2019–10
  7. By: Licht, Adrian; Escribano, Álvaro; Blazsek, Szabolcs
    Abstract: In this paper, new Seasonal-QVAR (quasi-vector autoregressive) and Markov switching (MS) Seasonal-QVAR (MS-Seasonal-QVAR) models are introduced. Seasonal-QVAR is an outlierrobust score-driven state space model, which is an alternative to classical multivariate Gaussian models (e.g. basic structural model; Seasonal-VARMA). Conditions of the maximum likelihood estimator and impulse response functions are shown. Dynamic relationships between world crude oil production and US industrial production are studied for the period of 1973 to 2019. Statistical performances of alternative models are analyzed. MS-Seasonal-QVAR identies structural changes and extreme observations in the dataset. MS-Seasonal-QVAR is superior to Seasonal-QVAR and, and both are superior to Gaussian alternatives.
    Keywords: Markov Regime-Switching Models; Score-Driven Multivariate Stochastic Location And Stochastic Seasonality Models; Score Models; Dynamic Conditional; United States Industrial Production; World Crude Oil Production
    JEL: C52 C51 C32
    Date: 2019–10
  8. By: Yaya, OlaOluwa S; Ogbonna, Ephraim A; Furuoka, Fumitaka; Gil-Alana, Luis A.
    Abstract: This paper proposes a nonlinear unit root test based on the artificial neural network-augmented Dickey-Fuller (ANN-ADF) test for testing hysteresis in unemployment. In this new unit root test, the linear, quadratic and cubic components of the neural network process are used to capture the nonlinearity in the time-series data. Fractional integration methods, based on linear and nonlinear trends are also used in the paper. By considering five European countries such as France, Italy, Netherland, Sweden, and the United Kingdom, the empirical findings indicate that there is still hysteresis in these countries. Among batteries of unit root tests applied, both the ARNN-ADF and fractional integration tests fail to reject the hypothesis of unemployment hysteresis in all the countries.
    Keywords: Unit root process; Nonlinearity; Neuron network: Time-series; Hysteresis; Unemployment; Europe; Labour market.
    JEL: C22
    Date: 2019–10–19
  9. By: Uwe Hassler; Marc-Oliver Pohle
    Abstract: Long memory in the sense of slowly decaying autocorrelations is a stylized fact in many time series from economics and finance. The fractionally integrated process is the workhorse model for the analysis of these time series. Nevertheless, there is mixed evidence in the literature concerning its usefulness for forecasting and how forecasting based on it should be implemented. Employing pseudo-out-of-sample forecasting on inflation and realized volatility time series and simulations we show that methods based on fractional integration clearly are superior to alternative methods not accounting for long memory, including autoregressions and exponential smoothing. Our proposal of choosing a fixed fractional integration parameter of $d=0.5$ a priori yields the best results overall, capturing long memory behavior, but overcoming the deficiencies of methods using an estimated parameter. Regarding the implementation of forecasting methods based on fractional integration, we use simulations to compare local and global semiparametric and parametric estimators of the long memory parameter from the Whittle family and provide asymptotic theory backed up by simulations to compare different mean estimators. Both of these analyses lead to new results, which are also of interest outside the realm of forecasting.
    Date: 2019–10
  10. By: Patrick Leung; Catherine S. Forbes; Gael M Martin; Brendan McCabe
    Abstract: We investigate the impact of filter choice on forecast accuracy in state space models. The filters are used both to estimate the posterior distribution of the parameters, via a particle marginal Metropolis-Hastings (PMMH) algorithm, and to produce draws from the filtered distribution of the final state. Multiple filters are entertained, including two new data-driven methods. Simulation exercises are used to document the performance of each PMMH algorithm, in terms of computation time and the efficiency of the chain. We then produce the forecast distributions for the one-stepahead value of the observed variable, using a fixed number of particles and Markov chain draws. Despite distinct differences in efficiency, the filters yield virtually identical forecasting accuracy, with this result holding under both correct and incorrect specification of the model. This invariance of forecast performance to the specification of the filter also characterizes an empirical analysis of S&P500 daily returns.
    Keywords: Bayesian prediction, particle MCMC; non-Gaussian time series, state space models, unbiased likelihood estimation, sequential Monte Carlo.
    JEL: C11 C22 C58
    Date: 2019
  11. By: Thiyanga S. Talagala; Feng Li; Yanfei Kang
    Abstract: This paper introduces a novel meta-learning algorithm for time series forecasting. The efficient Bayesian multivariate surface regression approach is used to model forecast error as a function of features calculated from the time series. The minimum predicted forecast error is then used to identify an individual model or combination of models to produce forecasts. In general, the performance of any meta-learner strongly depends on the reference dataset used to train the model. We further examine the feasibility of using GRATIS (a feature-based time series simulation approach) in generating a realistic time series collection to obtain a diverse collection of time series for our reference set. The proposed framework is tested using the M4 competition data and is compared against several benchmarks and other commonly used forecasting approaches. The new approach obtains performance comparable to the second and the third rankings of the M4 competition.
    Keywords: tme series, meta-learning, mixture autoregressive models, surface regression, M4 competition
    JEL: C10 C14 C22
    Date: 2019
  12. By: Florian Eckert (KOF Swiss Economic Institute, ETH Zurich, Switzerland); Rob Hyndman (Department of Econometrics & Business Statistics, Monash University, Australia); Anastasios Panagiotelis (Department of Econometrics & Business Statistics, Monash University, Australia)
    Abstract: This paper conducts an extensive forecasting study on 13,118 time series measuring Swiss goods exports, grouped hierarchically by export destination and product category. We apply existing state of the art methods in forecast reconciliation and introduce a novel Bayesian reconciliation framework. This approach allows for explicit estimation of reconciliation biases, leading to several innovations: Prior judgment can be used to assign weights to specific forecasts and the occurrence of negative reconciled forecasts can be ruled out. Overall we find strong evidence that in addition to producing coherent forecasts, reconciliation also leads to improvements in forecast accuracy.
    Keywords: Hierarchical Forecasting, Bayesian Forecast Reconciliation, Swiss Exports, Optimal Forecast Combination.
    JEL: C32 C53 E17
    Date: 2019–07
  13. By: Anastasios Panagiotelis; Puwasala Gamakumara; George Athanasopoulos; Rob J Hyndman
    Abstract: A geometric interpretation is developed for so-called reconciliation methodologies used to forecast time series that adhere to known linear constraints. In particular, a general framework is established nesting many existing popular reconciliation methods within the class of projections. This interpretation facilitates the derivation of novel results that explain why and how reconciliation via projection is guaranteed to improve forecast accuracy with respect to a specific class of loss functions. The result is also demonstrated empirically. The geometric interpretation is further used to provide a new proof that forecast reconciliation results in unbiased forecasts provided the initial base forecasts are also unbiased. Approaches for dealing with biased base forecasts are proposed and explored in an extensive empirical study on Australian tourism flows. Overall, the method of bias-correcting before carrying out reconciliation is shown to outperform alternatives that only bias-correct or only reconcile forecasts.
    Date: 2019

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