
on Econometric Time Series 
By:  Leopoldo Catania; Nima Nonejad 
Abstract:  We compare the predictive ability of several volatility models for a long series of weekly logreturns of the Dow Jones Industrial Average Index from 1902 to 2016. Our focus is particularly on predicting one and multistep ahead conditional and aggregated conditional densities. Our set of competing models includes: Wellknown GARCH specifications, Markov switching GARCH, sempiparametric GARCH, Generalised Autoregressive Score (GAS), the plain stochastic volatility (SV) as well as its more flexible extensions such as SV with leverage, inmean effects and Studentt distributed errors. We find that: (i) SV models generally outperform the GARCH specifications, (ii): The SV model with leverage effect provides very strong outofsample performance in terms of one and multisteps ahead density prediction, (iii) Differences in terms of ValueatRisk (VaR) predictions accuracy are less evident. Thus, our results have an important implication: the best performing model depends on the evaluation criterion 
Date:  2016–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:1605.00230&r=ets 
By:  Hernández Juan R. 
Abstract:  In this paper I propose a Likelihood Ratio test for a unit root (LR) with a localtounity Autoregressive parameter embedded in ARMA(1,1) models. By dealing explicitly with dependence in a time series through the Moving Average, as opposed to the long Autorregresive lag approximation, the test shows gains in power and has good smallsample properties. The asymptotic distribution of the test is shown to be independent of the shortrun parameters. The Monte Carlo experiments show that the LR test has higher power than the Augmented Dickey Fuller test for several sample sizes and true values of the Moving Average parameter. The exception is the case when this parameter is very close to 1 with a considerably small sample size. 
Keywords:  Likelihood ratio test; ARMA model; Unit root test. 
JEL:  C22 
Date:  2016–04 
URL:  http://d.repec.org/n?u=RePEc:bdm:wpaper:201603&r=ets 
By:  Johannsen, Benjamin K.; Mertens, Elmar 
Abstract:  Modeling interest rates over samples that include the Great Recession requires taking stock of the effective lower bound (ELB) on nominal interest rates. We propose a flexible time– series approach which includes a “shadow rate”—a notional rate that is less than the ELB during the period in which the bound is binding—without imposing no–arbitrage assumptions. The approach allows us to estimate the behavior of trend real rates as well as expected future interest rates in recent years. 
Keywords:  Bayesian Econometrics ; Effective Lower Bound ; Shadow Rate ; StateSpace Model ; Term Structure of Interest Rates 
JEL:  C32 C34 C53 E43 E47 
Date:  2016–04–04 
URL:  http://d.repec.org/n?u=RePEc:fip:fedgfe:201633&r=ets 
By:  Yongchen Zhao (Towson University) 
Abstract:  Based on a set of carefully designed Monte Carlo exercises, this paper document the behavior and performance of several newly developed advanced forecast combination algorithms in unstable environments, where performance of candidate forecasts are crosssectionally heterogeneous and dynamically evolving over time. Results from these exercises provide guidelines regarding the selection of forecast combination method based on the nature, frequency, and magnitude of instabilities in forecasts as well as the target variable. Following these guidelines, a simple forecast combination exercise using the U.S. Survey of Professional Forecasters, where combined forecasters are shown to have superior performance that is not only statistically significant but also of practical importance. 
Keywords:  Forecast combination; exponential reweighting; shrinkage; estimation error; performance stability; realtime data 
JEL:  C53 C22 C15 
Date:  2015–12 
URL:  http://d.repec.org/n?u=RePEc:gwc:wpaper:2015005&r=ets 
By:  Mario Forni; Alessandro Giovannelli; Marco Lippi; Stefano Soccorsi 
Abstract:  The paper compares the pseudo realtime forecasting performance of three Dynamic Factor Models: (i) The standard principalcomponent model, Stock and Watson (2002a), (ii) The model based on generalized principal components, Forni et al. (2005), (iii) The model recently proposed in Forni et al. (2015) and Forni et al. (2016). We employ a large monthly dataset of macroeconomic and financial time series for the US economy, which includes the Great Moderation, the Great Recession and the subsequent recovery. Using a rolling window for estimation and prediction, we find that (iii) neatly outperforms (i) and (ii) in the Great Moderation period for both Industrial Production and Inflation, and for Inflation over the full sample. However, (iii) is outperformed by (i) and (ii) over the full sample for Industrial Production. 
Date:  2016–04 
URL:  http://d.repec.org/n?u=RePEc:mod:recent:120&r=ets 
By:  Fernando E. Alvarez; Katarína Borovičková; Robert Shimer 
Abstract:  We develop a dynamic model of transitions in and out of employment. A worker finds a job at an optimal stopping time, when a Brownian motion with drift hits a barrier. This implies that the duration of each worker's jobless spells has an inverse Gaussian distribution. We allow for arbitrary heterogeneity across workers in the parameters of this distribution and prove that the distribution of these parameters is identified from the duration of two spells. We use social security data for Austrian workers to estimate the model. We conclude that dynamic selection is a critical source of duration dependence. 
JEL:  E24 J64 
Date:  2016–04 
URL:  http://d.repec.org/n?u=RePEc:nbr:nberwo:22188&r=ets 
By:  Kollmann, Robert 
Abstract:  This paper discusses a tractable approach for computing the likelihood function of nonlinear Dynamic Stochastic General Equilibrium (DSGE) models that are solved using second and third order accurate approximations. By contrast to particle filters, no stochastic simulations are needed for the method here. The method here is, hence, much faster and it is thus suitable for the estimation of mediumscale models. The method assumes that the number of exogenous innovations equals the number of observables. Given an assumed vector of initial states, the exogenous innovations can thus recursively be inferred from the observables. This easily allows to compute the likelihood function. Initial states and model parameters are estimated by maximizing the likelihood function. Numerical examples suggest that the method provides reliable estimates of model parameters and of latent state variables, even for highly nonlinear economies with big shocks. 
Keywords:  Likelihoodbased estimation of nonlinear DSGE models, higherorder approximations, pruning, latent state variables 
JEL:  C6 E3 
Date:  2016 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:70350&r=ets 
By:  Givens, Gregory 
Abstract:  This paper checks whether the coefficient estimates of a famous DSGE model are robust to macroeconomic data revisions. The effects of revisions are captured by rerunning the estimation on a realtime data set compiled using the latest time series available each quarter from 1997 through 2015. Results show that point estimates of the structural parameters are generally robust to changes in the data that have occurred over the past twenty years. By comparison, estimates of the standard errors are relatively more sensitive to revisions. The latter implies that judgements about the statistical significance of certain parameters depend on which data vintage is used for estimation. 
Keywords:  Data Revisions, RealTime Data, DSGE Estimation 
JEL:  C32 C82 E32 E52 
Date:  2016–04–22 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:70932&r=ets 
By:  Richard T. Baillie (Department of Economics, Michigan State University, USA; School of Economics and Finance, Queen Mary University of London, UK; The Rimini Centre for Economic Analysis, Italy); George Kapetanios (School of Economics and Finance, Queen Mary University of London, UK); Fotis Papailias (Queen's University Management School, Queen's University Belfast, UK; quantf research, www.quantf.com) 
Abstract:  This paper considers a multivariate system of fractionally integrated time series and investigates the most appropriate way for estimating Impulse Response (IR) coefficients and their associated confidence intervals. The paper extends the univariate analysis recently provided by Baillie and Kapetanios (2013), and uses a semi parametric, time domain estimator, based on a vector autoregression (VAR) approximation. Results are also derived for the orthogonalized estimated IRs which are generally more practically relevant. Simulation evidence strongly indicates the desirability of applying the Kilian small sample bias correction, which is found to improve the coverage accuracy of confidence intervals for IRs. The most appropriate order of the VAR turns out to be relevant for the lag length of the IR being estimated. 
Date:  2015–12 
URL:  http://d.repec.org/n?u=RePEc:rim:rimwps:1546&r=ets 
By:  Richard T. Baillie (Department of Economics, Michigan State University, USA; School of Economics and Finance, Queen Mary University of London, UK; The Rimini Centre for Economic Analysis, Italy); Kun Ho Kim (Department of Economics, Hanyang University, Republic of Korea) 
Abstract:  It has become commonplace in applied time series econometric work to estimate regressions with consistent, but asymptotically inefficient OLS and to base inference of conditional mean parameters on robust standard errors. This approach seems mainly to have occurred due to concern at the possible violation of strict exogeneity conditions from applying GLS. We first show that even in the case of the violation of contemporaneous exogeneity, that the asymptotic bias associated with GLS will generally be less than that of OLS. This result extends to Feasible GLS where the error process is approximated by a sieve autoregression. The paper also examines the tradeoffs between asymptotic bias and efficiency related to OLS, feasible GLS and inference based on full system VAR. We also provide simulation evidence and several examples including tests of efficient markets, orange juice futures and weather and a control engineering application of furnace data. The evidence and general conclusion is that the widespread use of OLS with robust standard errors is generally not a good research strategy. Conversely, there is much to recommend FGLS and VAR system based estimation. 
Date:  2016–03 
URL:  http://d.repec.org/n?u=RePEc:rim:rimwps:1604&r=ets 
By:  Elena Andreou 
Abstract:  Many empirical studies link mixed data frequency variables such as low frequency macroeconomic or financial variables with high frequency financial indicators’ volatilities, especially within a predictive regression model context. The objective of this paper is threefold: First, we relate the standard Least Squares (LS) regression model with high frequency volatility predictors, with the corresponding Mixed Data Sampling Nonlinear LS (MIDASNLS) regression model (Ghysels et al., 2005, 2006), and evaluate the properties of the regression estimators of these models. We also consider alternative high frequency volatility measures as well as various continuous time models using their corresponding relevant higherorder moments to further analyze the properties of these estimators. Second, we derive the relative MSE efficiency of the slope estimator in the standard LS and MIDAS regressions, we provide conditions for relative efficiency and present the numerical results for different continuous time models. Third, we extend the analysis of the bias of the slope estimator in standard LS regressions with alternative realized measures of risk such as the Realized Covariance, Realized Beta and the Realized Skewness when the true DGP is a MIDAS model. 
Keywords:  MIDAS regression model, highfrequency volatility estimators, bias, efficiency. 
JEL:  C22 C53 G22 
Date:  2016–04 
URL:  http://d.repec.org/n?u=RePEc:ucy:cypeua:032016&r=ets 