nep-ets New Economics Papers
on Econometric Time Series
Issue of 2016‒02‒12
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. Generalized Efficient Inference on Factor Models with Long-Range Dependence By Yunus Emre Ergemen
  2. Approximating time varying structural models with time invariant structures By Fabio Canova; Christian Matthes
  3. Are Small-Scale SVARs Useful for Business Cycle Analysis? Revisiting Non-Fundamentalness By Fabio Canova
  4. Solution and Estimation Methods for DSGE Models By Fernández-Villaverde, Jesús; Rubio-Ramírez, Juan Francisco; Schorfheide, Frank
  5. Autoregressive Spatial Spectral Estimates By Gupta, Abhimanyu
  6. Estimation of Spatial Autoregressions with Stochastic Weight Matrices By Gupta, Abhimanyu
  7. Pseudo Maximum Likelihood Estimation of Spatial Autoregressive Models with Increasing Dimension By Gupta, Abhimanyu; Robinson, Peter M
  8. The Estimation of Continuous Time Models with Mixed Frequency Data By Chambers, Marcus J

  1. By: Yunus Emre Ergemen (Aarhus University and CREATES)
    Abstract: A dynamic factor model is considered that contains stochastic time trends allowing for stationary and nonstationary long-range dependence. The model nests standard I(0) and I(1) behaviour smoothly in common factors and residuals, removing the necessity of a priori unit-root and stationarity testing. Short-memory dynamics are allowed in the common factor structure and possibly heteroskedastic error term. In the estimation, a generalized version of the principal components (PC) approach is proposed to achieve efficiency. Asymptotics for efficient common factor and factor loading as well as long-range dependence parameter estimates are justified at standard parametric convergence rates. The use of the method for the selection of number of factors and testing for latent components is discussed. Finite-sample properties of the estimates are explored via Monte-Carlo experiments, and an empirical application to U.S. economy diffusion indices is included.
    Keywords: Factor models, long-range dependence, principal components, efficiency, hypothesis testing
    JEL: C12 C13 C33
    Date: 2016–01–29
  2. By: Fabio Canova; Christian Matthes
    Abstract: The paper studies how parameter variation affects the decision rules of a DSGE model and structural inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. Identification and inferential distortions when a constant parameter model is incorrectly assumed are examined. Likelihood and VAR-based estimates of the structural dynamics when parameter variations are neglected are compared. Time variations in the financial frictions of Gertler and Karadiís (2010) model are studied.
    Keywords: Structural model, Time-varying coefficients, Endogenous variations, Misspecification
    Date: 2016–01
  3. By: Fabio Canova
    Abstract: Non-fundamentalness arises when observables do not contain enough information to recover the vector of structural shocks. Using Granger causality tests, the literature suggested that many small scale VAR models are non-fundamental and thus not useful for business cycle analysis. We show that causality tests are problematic when VAR variables are cross sectionally aggregated or proxy for non-observables. We provide an alternative testing procedure, illustrate its properties with a Monte Carlo exercise, and reexamine the properties of two prototypical VAR models.
    Keywords: Aggregation, Non-Fundamentalness, Granger causality, Small scale VARs
    Date: 2016–02
  4. By: Fernández-Villaverde, Jesús; Rubio-Ramírez, Juan Francisco; Schorfheide, Frank
    Abstract: This paper provides an overview of solution and estimation techniques for dynamic stochastic general equilibrium (DSGE) models. We cover the foundations of numerical approximation techniques as well as statistical inference and survey the latest developments in the field.
    Keywords: approximation error analysis; Bayesian inference; DSGE model; frequentist inference; GMM estimation; impulse response function matching; likelihood-based inference; Metropolis-Hastings algorithm; minimum distance estimation; particle filter; perturbation methods; projection methods; sequential Monte Carlo
    JEL: C11 C13 C32 C52 C61 C63 E32 E52
    Date: 2015–12
  5. By: Gupta, Abhimanyu
    Abstract: Autoregressive spectral density estimation for stationary random fields on a regular spatial lattice has many advantages relative to kernel based methods. It provides a guaranteed positive-definite estimate even when suitable edge-effect correction is employed, is simple to compute using least squares and necessitates no choice of kernel. We truncate a true half-plane infinite autoregressive representation to estimate the spectral density. The truncation length is allowed to diverge in all dimensions in order to avoid the potential bias which would accrue due to truncation at a fixed lag-length. Consistency and strong consistency of the proposed estimator, both uniform in frequencies, are established. Under suitable conditions the asymptotic distribution of the estimate is shown to be zero-mean normal and independent at fixed distinct frequencies, mirroring the behaviour for time series. A small Monte Carlo experiment examines finite sample performance. We illustrate the technique by applying it to Los Angeles house price data and a novel analysis of voter turnout data in a US presidential election. Technically the key to the results is the covariance structure of stationary random fields defined on regularly spaced lattices. We study this in detail and show the covariance matrix to satisfy a generalization of the Toeplitz property familiar from time series analysis.
    Date: 2015
  6. By: Gupta, Abhimanyu
    Abstract: We examine a higher-order spatial autoregressive model with stochastic, but exogenous, spatial weight matrices. Allowing a general spatial linear process form for the disturbances that permits many common types of error specifications as well as potential ‘long memory’, we provide sufficient conditions for consistency and asymptotic normality of instrumental variables and ordinary least squares estimates. The implications of popular weight matrix normalizations and structures for our theoretical conditions are discussed. A set of Monte Carlo simulations examines the behaviour of the estimates in a variety of situations and suggests, like the theory, that spatial weights generated from distributions with ‘smaller’ moments yield better estimates. Our results are especially pertinent in situations where spatial weights are functions of stochastic economic variables.
    Date: 2015
  7. By: Gupta, Abhimanyu; Robinson, Peter M
    Abstract: Pseudo maximum likelihood estimates are developed for higher-order spatial autoregres- sive models with increasingly many parameters, including models with spatial lags in the dependent variables and regression models with spatial autoregressive disturbances. We consider models with and without a linear or nonlinear regression component. Sufficient conditions for consistency and asymptotic normality are provided, the results varying ac- cording to whether the number of neighbours of a particular unit diverges or is bounded. Monte Carlo experiments examine nite-sample behaviour.
    Date: 2015
  8. By: Chambers, Marcus J
    Abstract: This paper derives exact representations for discrete time mixed frequency data generated by an underlying multivariate continuous time model. Allowance is made for different combinations of stock and flow variables as well as deterministic trends, and the variables themselves may be stationary or nonstationary (and possibly co-integrated). The resulting discrete time representations allow for the information contained in high frequency data to be utilised alongside the low frequency data in the estimation of the parameters of the continuous time model. Monte Carlo simulations explore the finite sample performance of the maximum likelihood estimator of the continuous time system parameters based on mixed frequency data, and a comparison with extant methods of using data only at the lowest frequency is provided. An empirical application demonstrates the methods developed in the paper and it concludes with a discussion of further ways in which the present analysis can be extended and refined.
    Keywords: Continuous time; mixed frequency data; exact discrete time models; stock and flow variables.
    Date: 2016

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