Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series
2018-03-19
Semiparametric detection of changes in long range dependence
http://d.repec.org/n?u=RePEc:qmw:qmwecw:830&r=ets
We consider changes in the degree of persistence of a process when the degree of persistence is characterized as the order of integration of a strongly dependent process. To avoid the risk of incorrectly specifing the data generating process we employ local Whittle estimates which uses only frequencies local at zero. The limit distribution of the test statistic under the null is not standard but it is well known in the literature. A Monte Carlo study shows that this inference procedure performs well in finite samples.
Fabrizio Iacone
Stepana Lazarova
Long memory, persistence, break, local Whittle estimate
2017-08-18
Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks
http://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-64&r=ets
I propose a flexible nonlinear method for studying the time series properties of macroeconomic variables. In particular, I focus on a class of Artificial Neural Networks (ANN) called the Radial Basis Functions (RBF). To assess the validity of the RBF approach in the macroeconomic time series analysis, I conduct a Monte Carlo experiment using the data generated from a nonlinear New Keynesian (NK) model. I find that the RBF estimator can uncover the structure of the nonlinear NK model from the simulated data whose length is as small as 300 periods. Finally, I apply the RBF estimator to the quarterly US data and show that the response of the macroeconomic variables to a positive supply shock exhibits a substantial time variation. In particular, the positive supply shocks are found to have significantly weaker expansionary effects during the zero lower bound periods as well as periods between 2003 and 2004. The finding is consistent with a basic NK model, which predicts that the higher real interest rate due to the monetary policy inaction weakens the effects of supply shocks.
KANAZAWA, Nobuyuki
Neural Networks, Radial Basis Functions, Zero Lower Bound, Supply Shocks
2018-03
Finite Sample Theory and Bias Correction of Maximum Likelihood Estimators in the EGARCH Model
http://d.repec.org/n?u=RePEc:aue:wpaper:1802&r=ets
We derive analytical expressions of bias approximations for maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of the EGARCH(1; 1) parameters that enable us to correct after the bias of all estimators. The bias correction mechanism is constructed under the specification of two methods that are analytically described. We also evaluate the residual bootstrapped estimator as a measure of performance. Monte Carlo simulations indicate that, for given sets of parameters values, the bias corrections work satisfactory for all parameters. The proposed full-step estimator performs better than the classical one and is also faster than the bootstrap. The results can be also used to formulate the approximate Edgeworth distribution of the estimators.
Antonis Demos
Dimitra Kyriakopoulou
Exponential GARCH, maximum likelihood estimation, finite sample properties, bias approximations, bias correction, Edgeworth expansion, bootstrap
2018-02-23
Finite Sample Theory and Bias Correction of MLEs in the EGARCH Model (Technical Appendix I)
http://d.repec.org/n?u=RePEc:aue:wpaper:1803&r=ets
Antonis Demos
Dimitra Kyriakopoulou
2018-02-23
Finite Sample Theory and Bias Correction of MLEs in the EGARCH Model (Technical Appendix II)
http://d.repec.org/n?u=RePEc:aue:wpaper:1804&r=ets
Antonis Demos
Dimitra Kyriakopoulou
2018-02-23
Negative Binomial Autoregressive Process
http://d.repec.org/n?u=RePEc:upn:wpaper:2018-01&r=ets
We introduce Negative Binomial Autoregressive (NBAR) processes for (univariate and bivariate) count time series. The univariate NBAR process is defined jointly with an underlying intensity process, which is autoregressive gamma. The resulting count process is Markov, with negative binomial conditional and marginal distributions. The process is then extended to the bivariate case with a Wishart autoregressive matrix intensity process. The NBAR processes are Compound Autoregressive, which allows for simple stationarity condition and quasi-closed form nonlinear forecasting formulas at any horizon, as well as a computationally tractable generalized method of moment estimator. The model is applied to a pairwise analysis of weekly occurrence counts of a contagious disease between the greater Paris region and other French regions.
Yang Lu
Christian Gourieroux
Compound Autoregressive, Poisson-gamma conjugacy
2018-03
Permutation Tests for Equality of Distributions of Functional Data
http://d.repec.org/n?u=RePEc:arx:papers:1803.00798&r=ets
Economic data are often generated by stochastic processes that take place in continuous time, though observations may occur only at discrete times. For example, electricity and gas consumption take place in continuous time. Data generated by a continuous time stochastic process are called functional data. This paper is concerned with comparing two or more stochastic processes that generate functional data. The data may be produced by a randomized experiment in which there are multiple treatments. The paper presents a test of the hypothesis that the same stochastic process generates all the functional data. In contrast to existing methods, the test described here applies to both functional data and multiple treatments. The test is presented as a permutation test, which ensures that in a finite sample, the true and nominal probabilities of rejecting a correct null hypothesis are equal. The paper also presents the asymptotic distribution of the test statistic under alternative hypotheses. The results of Monte Carlo experiments and an application to an experiment on billing and pricing of natural gas illustrate the usefulness of the test.
Federico A. Bugni
Joel L. Horowitz
2018-03
Bootstrap-Assisted Unit Root Testing With Piecewise Locally Stationary Errors
http://d.repec.org/n?u=RePEc:arx:papers:1802.05333&r=ets
In unit root testing, a piecewise locally stationary process is adopted to accommodate nonstationary errors that can have both smooth and abrupt changes in second- or higher-order properties. Under this framework, the limiting null distributions of the conventional unit root test statistics are derived and shown to contain a number of unknown parameters. To circumvent the difficulty of direct consistent estimation, we propose to use the dependent wild bootstrap to approximate the non-pivotal limiting null distributions and provide a rigorous theoretical justification for bootstrap consistency. The proposed method is compared through finite sample simulations with the recolored wild bootstrap procedure, which was developed for errors that follow a heteroscedastic linear process. Further, a combination of autoregressive sieve recoloring with the dependent wild bootstrap is shown to perform well. The validity of the dependent wild bootstrap in a nonstationary setting is demonstrated for the first time, showing the possibility of extensions to other inference problems associated with locally stationary processes.
Yeonwoo Rho
Xiaofeng Shao
2018-02
Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions
http://d.repec.org/n?u=RePEc:mar:magkse:201810&r=ets
This article investigates the construction of skewness-adjusted confidence intervals and joint confidence bands for impulse response functions from vector autoregressive models. Three different implementations of the skewness adjustment are investigated. The methods are based on a bootstrap algorithm that adjusts mean and skewness of the bootstrap distribution of the autoregressive coefficients before the impulse response functions are computed. Using extensive Monte Carlo simulations, the methods are shown to improve the coverage accuracy in small and medium sized samples and for unit root processes for both known and unknown lag orders.
Daniel Grabowski
Anna Staszewska-Bystrova
Peter Winker
Bootstrap, confidence intervals, joint confidence bands, vector autoregression
2018
Forecasting dynamically asymmetric fluctuations of the U.S. business cycle
http://d.repec.org/n?u=RePEc:pav:demwpp:demwp0156&r=ets
The Generalized Smooth Transition Auto-Regression (GSTAR) parametrizes the joint asymmetry in the duration and length of cycles in macroeconomic time series by using particular generalizations of the logistic function. The symmetric smooth transition and linear auto-regressions are peculiar cases of the new parametrization. A test for the null hypothesis of dynamic symmetry is discussed. Two case studies indicate that dynamic asymmetry is a key feature of the U.S. economy. Our model beats its competitors in point forecasting, but this superiority becomes less evident in density forecasting and in uncertain forecasting environments.
Emilio Zanetti Chini
Density forecasts, Econometric modelling, Evaluating forecasts, Generalized logistic, Industrial production, Nonlinear time series, Point forecasts, Statistical tests, Unemployment.
2018-03
Comparing different data descriptors in Indirect Inference tests on DSGE models
http://d.repec.org/n?u=RePEc:cdf:wpaper:2018/7&r=ets
Indirect inference testing can be carried out with a variety of auxiliary models. Asymptotically these different models make no difference. However, in small samples power can differ. We explore small sample power and estimation bias both with different variable combinations and models of description --- Vector Auto Regressions, Impulse Response Functions or Moments (corresponding to the Simulated Methods of Moments) --- in the auxiliary model. We find that VAR and IRF descriptors perform slightly better than Moments but that different three variable combinations make little difference. More than three variables raises power and lowers bias but reduces the chances of finding a tractable model that passes the test.
Meenagh, David
Minford, Patrick
Wickens, Michael
Xu, Yongdeng
Indirect Inference, DGSE model, Auxiliary Models, Simulated Moments Method, Impulse Response Functions, VAR, Moments, power, bias
2018-03
An Overview of Modified Semiparametric Memory Estimation Methods
http://d.repec.org/n?u=RePEc:han:dpaper:dp-628&r=ets
Several modified estimation methods of the memory parameter have been introduced in the past years. They aim to decrease the upward bias of the memory parameter in cases of low frequency contaminations or an additive noise component, especially in situations with a short-memory process being contaminated. In this paper, we provide an overview and compare the performance of nine semiparametric estimation methods. Among them are two standard methods, four modified approaches to account for low frequency contaminations and three procedures developed for perturbed fractional processes. We conduct an extensive Monte Carlo study for a variety of parameter constellations and several DGPs. Furthermore, an empirical application of the log-absolute return series of the S&P 500 shows that the estimation results combined with a long-memory test indicate a spurious long-memory process.
Busch, Marie
Sibbertsen, Philipp
Spurious Long Memory; Semiparametric estimation; Low frequency contamination; Pertubation;Monte Carlo simulation
2018-03