
on Econometric Time Series 
By:  Licht, Adrian; Escribano Saez, Alvaro; Blazsek, Szabolcs Istvan 
Abstract:  Relevant works from the literature on crude oil market use structural vector autoregressive(SVAR) models with several lags to approximate the true model for the variables change in globalcrude oil production, global real economic activity and log real crude oil prices. Those variables involveseasonality, cointegration, structural changes, and outliers. We introduce nonlinear Markovswitchingscoredriven models with common trends of the multivariate tdistribution (MSSeasonaltQVAR), forwhich filters are optimal according to the KullbackLeibler divergence. We find that MSSeasonaltQVAR provides a better approximation of the true data generating process and more precise shortrunand longrun impulse responses than SVAR. 
Keywords:  Markov RegimeSwitching Models; Outliers And Structural Changes; Nonlinear CoIntegration; ScoreDriven Models; Global Crude Oil Market 
JEL:  C52 C51 C32 
Date:  2020–05–07 
URL:  http://d.repec.org/n?u=RePEc:cte:werepe:30346&r=all 
By:  Licht, Adrian; Escribano Saez, Alvaro; Blazsek, Szabolcs Istvan 
Abstract:  In this paper, the benefits of statistical inference of scoredriven statespacemodels are incorporated into the inference of dynamic stochastic general equilibrium (DSGE)models. We focus on DSGE models, for which a Gaussian ABCD representation exists. Precisionof statistical estimation is improved, by using a scoredriven multivariate tdistribution for theerrors. First, the updating term of the transition equation of the ABCD representation isreplaced by the conditional score of the loglikelihood (LL) with respect to location. Second,the timeconstant scale parameters of the error terms in the measurement equation of the ABCDrepresentation are replaced by a dynamic parameter that is updated by the conditional score ofthe LL with respect to scale. Impulse response functions (IRFs) and conditions of the maximumlikelihood (ML) estimator are presented. In the empirical application, a benchmark DSGE modelis estimated for real data on US economic output, inflation and interest rate for the period of19542019. The scoredriven ABCD representation improves the estimation precision of theGaussian ABCD representation. The scoredriven ABCD representation with dynamic scaleprovides the best description of the time series data, by identifying a structural change in thesample period and providing the most precise IRF estimates. 
Keywords:  BetaTEgarch; Generalized Autoregressive Score (Gas); Dynamic Conditional Score (Dcs); Dynamic Stochastic General Equilibrium (Dsge) 
Date:  2020–05–07 
URL:  http://d.repec.org/n?u=RePEc:cte:werepe:30347&r=all 
By:  Tobias Hartl; Rolf Tschernig; Enzo Weber 
Abstract:  We develop a generalization of unobserved components models that allows for a wide range of longrun dynamics by modelling the permanent component as a fractionally integrated process. The model does not require stationarity and can be cast in state space form. In a multivariate setup, fractional trends may yield a cointegrated system. We derive the Kalman filter estimator for the common fractionally integrated component and establish consistency and asymptotic (mixed) normality of the maximum likelihood estimator. We apply the model to extract a common longrun component of three US inflation measures, where we show that the $I(1)$ assumption is likely to be violated for the common trend. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2005.03988&r=all 
By:  Danilo LeivaLeon; Luis Uzeda 
Abstract:  We introduce a new class of timevarying parameter vector autoregressions (TVPVARs) where the identified structural innovations are allowed to influence — contemporaneously and with a lag — the dynamics of the intercept and autoregressive coefficients in these models. An estimation algorithm and a parametrization conducive to model comparison are also provided. We apply our framework to the US economy. Scenario analysis suggests that the effects of monetary policy on economic activity are larger and more persistent in the proposed models than in an otherwise standard TVPVAR. Our results also indicate that costpush shocks play an important role in understanding historical changes in inflation persistence. 
Keywords:  Econometric and statistical methods; Inflation and prices; Transmission of monetary policy 
JEL:  C32 E52 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:bca:bocawp:2016&r=all 
By:  Sium Bodha Hannadige; Jiti Gao; Mervyn J. Silvapulle; Param Silvapulle 
Abstract:  This paper develops a method for forecasting a nonstationary time series, such as GDP, using a set of highdimensional panel data as predictors. To this end, we use what is known as a factor augmented regression [FAR] model that contains a small number of estimated factors as predictors; the factors are estimated using time series data on a large number of potential predictors. The validity of this method for forecasting has been established when all the variables are stationary and also when they are all nonstationary, but not when they consist of a mixture of stationary and nonstationary ones. This paper fills this gap. More specifically, we develop a method for constructing an asymptotically valid prediction interval using the FAR model when the predictors include a mixture of stationary and nonstationary factors; we refer to this as mixtureFAR model. This topic is important because typically time series data on a large number of economic variables is likely to contain a mixture of stationary and nonstationary variables. In a simulation study, we observed that the mixtureFAR performed better than its competitor that requires all the variables to be nonstationary. As an empirical illustration, we evaluated the aforementioned methods for forecasting the nonstationary variables, GDP and Industrial Production [IP], using the quarterly panel data on US macroeconomic variables, known as FREDD. We observed that the mixtureFAR model proposed in this paper performed better than its aforementioned competitors. 
Keywords:  bootstrap,generated factors, panel data, prediction interval. 
JEL:  C22 C33 C38 C53 
Date:  2020 
URL:  http://d.repec.org/n?u=RePEc:msh:ebswps:202019&r=all 
By:  Samuel Brien; Michael Jansson (UC Berkeley and CREATES); Morten Ørregaard Nielsen (Queen's University and CREATES) 
Abstract:  We study largesample properties of likelihood ratio tests of the unit root hypothesis in an autoregressive model of arbitrary, finite order. Earlier research on this testing problem has developed likelihood ratio tests in the autoregressive model of order one, but resorted to a plugin approach when dealing with higherorder models. In contrast, we consider the full model and derive the relevant largesample properties of likelihood ratio tests under a localtounity asymptotic framework. As in the simpler model, we show that the full likelihood ratio tests are nearly efficient, in the sense that their asymptotic local power functions are virtually indistinguishable from the Gaussian power envelopes. 
Keywords:  Efficiency, Likelihood ratio test, Nuisance parameters, Unit root hypothesis 
JEL:  C12 C22 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:qed:wpaper:1429&r=all 
By:  Niko Hauzenberger; Florian Huber; Gary Koop 
Abstract:  Timevarying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well. 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2005.03906&r=all 
By:  Franses, Ph.H.B.F. 
Abstract:  This paper introduces a new autoregressive model, with the specific feature that the lag structure can vary over time. More precise, and to keep matters simple, the autoregressive model sometimes has lag 1, and sometimes lag 2. Representation, autocorrelation, specification, inference, and the creation of forecasts are presented. A detailed illustration for annual inflation rates for eight countries in Africa shows the empirical relevance of the new model. Various potential extensions are discussed. 
Keywords:  Autoregression, Timevarying lags, Forecasting 
JEL:  C22 C53 
Date:  2020–04–01 
URL:  http://d.repec.org/n?u=RePEc:ems:eureir:126706&r=all 
By:  Bago, JeanLouis; Akakpo, Koffi; Rherrad, Imad; Ouédraogo, Ernest 
Abstract:  This paper provides new empirical evidence on housing bubbles timing, volatility spillover and bubbles contagion between Japan and its economics partners, namely, the United States, the Eurozone, and the United Kingdom. First, we apply a generalized sup ADF (GSADF) test developed by Phillips et al. (2015) to quarterly pricetorent ratio from 1970Q1 to 2018Q4 to detect explosive behaviors in housing prices. Second, we analyze the volatility spillover in housing prices between Japan and its economic partners using the multivariate timevarying DCCGARCH model developed by Engle (2002). Third, we assess bubbles contagion using the nonparametric model with timevarying coefficients developed by GreenawayMcGrevy and Phillips (2016). We document two historical bubble episodes from 1970 to 2018 in the Japan’s housing market. Moreover, we find evidence of volatility spillover and bubbles contagion between Japan’s real estate market and its most important economic partners during several periods. 
Keywords:  Bubble, Contagion, Real estate, Japan, DCCGARCH 
JEL:  C14 G12 
Date:  2020–05 
URL:  http://d.repec.org/n?u=RePEc:pra:mprapa:100098&r=all 
By:  Jamshid Ardalankia; Jafar Askari; Somaye Sheykhali; Emmanuel Haven; G. Reza Jafari 
Abstract:  For the sake of extracting hidden mutual and coupled information from possibly uncoupled timeseries, we explored the profound measures of network science on timeseries. Alongside common methods in timeseries analysis of coupling between financial and economic markets, mapping coupled timeseries onto networks is an outstanding measure to provide insight into hidden aspects embedded in couplings intrinsically. In this manner, we discretize the amplitude of coupled timeseries and investigate relative simultaneous locations of the corresponding amplitudes (nodes). The transmissions between simultaneous amplitudes are clarified by edges in the network. In this sense, by segmenting magnitudes, the scaling features, volatilities' size and also the direction of the coupled amplitudes can be described. The frequency of occurrences of the coupled amplitudes is illustrated by the weighted edges, that is to say, some coupled amplitudes in the timeseries can be identified as communities in the network. The results show that despite apparently uncoupled joint probabilities, the couplings possess some aspects which diverge from random Gaussian noise. Thereby, with the aid of the network's topological and statistical measurements, we distinguished basic structures of coupling of crossmarket networks. Meanwhile, it was discovered that even two possibly known uncoupled markets may possess coupled patterns with each other. Thereby, those markets should be examined as coupled and weakly coupled markets! 
Date:  2020–04 
URL:  http://d.repec.org/n?u=RePEc:arx:papers:2004.13536&r=all 