nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒12‒09
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Asymptotics for Stationary Very Nearly Unit Root Processes (joint with D.W.K. Andrews), this version November 2006 By Patrik Guggenberger
  2. Adaptive Estimation of Autoregressive Models with Time-Varying Variances By Ke-Li Xu; Peter C.B. Phillips
  3. Asymptotic Theory for Local Time Density Estimation and Nonparametric Cointegrating Regression By Qiying Wang; Peter C.B. Phillips
  4. Extracting Business Cycles using Semi-parametric Time-varying Spectra with Applications to US Macroeconomic Time Series By Siem Jan Koopman; Soon Yip Wong
  5. Improving Consistent Moment Selection Procedures for Generalized Method of Moments Estimation By Jean-Bernard Chatelain
  6. Nonparametric Density Estimation for Positive Time Series By Taoufik Bouezmarni; Jeroen V.K. Rombouts
  7. Econometrics: A Bird’s Eye View By John F. Geweke; Joel L. Horowitz; M. Hashem Pesaran
  8. Interest rate pass-through estimates from vector autoregressive models By Johann Burgstaller

  1. By: Patrik Guggenberger
  2. By: Ke-Li Xu (Dept. of Economics, Yale University); Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: Stable autoregressive models of known finite order are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution or combinations of the two. This paper develops kernel-based estimators of the residual variances and associated adaptive least squares (ALS) estimators of the autoregressive coefficients. These are shown to be asymptotically efficient, having the same limit distribution as the infeasible generalized least squares (GLS). Comparisons of the efficient procedure and ordinary least squares (OLS) reveal that least squares can be extremely inefficient in some cases while nearly optimal in others. Simulations show that, when least squares work well, the adaptive estimators perform comparably well, whereas when least squares work poorly, major efficiency gains are achieved by the new estimators.
    Keywords: Adaptive estimation, Autoregression, Heterogeneity, Weighted regression
    JEL: C14 C22
    Date: 2006–10
  3. By: Qiying Wang (School of Mathematics and Statistics, University of Sydney); Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: We provide a new asymptotic theory for local time density estimation for a general class of functionals of integrated time series. This result provides a convenient basis for developing an asymptotic theory for nonparametric cointegrating regression and autoregression. Our treatment directly involves the density function of the processes under consideration and avoids Fourier integral representations and Markov process theory which have been used in earlier research on this type of problem. The approach provides results of wide applicability to important practical cases and involves rather simple derivations that should make the limit theory more accessible and useable in econometric applications. Our main result is applied to offer an alternative development of the asymptotic theory for non-parametric estimation of a non-linear cointegrating regression involving non-stationary time series. In place of the framework of null recurrent Markov chains as developed in recent work of Karlsen, Myklebust and Tjostheim (2007), the direct local time density argument used here more closely resembles conventional nonparametric arguments, making the conditions simpler and more easily verified.
    Keywords: Brownian Local time, Cointegration, Integrated process, Local time density estimation, Nonlinear functionals, Nonparametric regression, Unit root
    JEL: C14 C22
    Date: 2006–12
  4. By: Siem Jan Koopman (Vrije Universiteit Amsterdam); Soon Yip Wong (Vrije Universiteit Amsterdam)
    Abstract: A growing number of empirical studies provides evidence that dynamic properties of macroeconomic time series have been changing over time. Model-based procedures for the measurement of business cycles should therefore allow model parameters to adapt over time. In this paper the time dependencies of parameters are implied by a time dependent sample spectrum. Explicit model specifications for the parameters are therefore not required. Parameter estimation is carried out in the frequency domain by maximising the spectral likelihood function. The time dependent spectrum is specified as a semi-parametric smoothing spline ANOVA function that can be formulated in state space form. Since the resulting spectral likelihood function is time-varying, model parameter estimates become time-varying as well. This new and simple approach to business cycle extraction includes bootstrap procedures for the computation of confidence intervals and real-time procedures for the forecasting of the spectrum and the business cycle. We illustrate the methodology by presenting a complete business cycle analysis for two U.S. macroeconomic time series. The empirical results are promising and provide significant evidence for the great moderation of the U.S. business cycle.
    Keywords: Frequency domain estimation; frequency domain bootstrap; time-varying parameters; unobserved components models
    JEL: C13 C14 C22 E32
    Date: 2006–11–29
  5. By: Jean-Bernard Chatelain (PSE - Paris-Jourdan Sciences Economiques - [CNRS : UMR8545] - [Ecole des Hautes Etudes en Sciences Sociales][Ecole Nationale des Ponts et Chaussées][Ecole Normale Supérieure de Paris], EconomiX - [CNRS : UMR7166] - [Université de Paris X - Nanterre])
    Abstract: This paper proposes consistent moment selection procedures for generalized method of moments estimation based on the J test of over-identifying restrictions (Hansen [1982]) and on the Eichenbaum, Hansen and Singleton [1988] test of the validity of a subset of moment conditions.
    Keywords: Generalized method of moments, test of over-identifying restrictions, test of subset of over-identifying restrictions, Consistent Moment Selection
    Date: 2006–11–30
  6. By: Taoufik Bouezmarni; Jeroen V.K. Rombouts (IEA, HEC Montréal)
    Abstract: The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For i.i.d. data several solutions have been put forward to solve this boundary problem. In this paper we propose the gamma kernel estimator as density estimator for positive data from a stationary ?-mixing process. We derive the mean integrated squared error, almost sure convergence and asymptotic normality. In a Monte Carlo study, where we generate data from an autoregressive conditional duration model and a stochastic volatility model, we find that the gamma kernel outperforms the local linear density estimator. An application to data from financial transaction durations, realized volatility and electricity price data is provided.
    Keywords: Gamma kernel, nonparametric density estimation, mixing process, transaction durations, realised volatility.
    JEL: C11 C22 C52
    Date: 2006–09
  7. By: John F. Geweke (University of Iowa); Joel L. Horowitz (Northwestern University); M. Hashem Pesaran (CIMF, University of Cambridge and IZA Bonn)
    Abstract: As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of "real time econometrics". This paper attempts to provide an overview of some of these developments.
    Keywords: history of econometrics, microeconometrics, macroeconometrics, Bayesian econometrics, nonparametric and semi-parametric analysis
    JEL: C1 C2 C3 C4 C5
    Date: 2006–11
  8. By: Johann Burgstaller (Department of Economics, Johannes Kepler University Linz, Austria)
    Abstract: The empirical literature on interest rate transmission presents diverse and sometimes conflicting estimates. By discussing methodological and specification-related issues, the results of this paper contribute to the understanding of these differences. Eleven Austrian bank lending and deposit rates are utilized to illustrate the pass-through of impulses from monetary policy and banks’ cost of funds. Results from vector autoregressions suggest that the long-run pass-through is higher for movements in the bond market than of changes in money market rates. Deposit rates have no predictive content for lending rates beyond that of market interest rates.
    Keywords: Monetary policy transmission; interest rate pass-through; retail interest rates; vector autoregression; impulse-response functions
    JEL: E43 E52 G21
    Date: 2005–12

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