nep-ets New Economics Papers
on Econometric Time Series
Issue of 2023‒02‒27
eleven papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Inference in Non-stationary High-Dimensional VARs By Alain Hecq; Luca Margaritella; Stephan Smeekes
  2. (Almost) Recursive Identification of Monetary Policy Shocks with Economic Parameter Restrictions By Jan Pablo Burgard; Matthias Neuenkirch; Dennis Umlandt
  3. Reflections on “Testing for Unit Roots in Heterogeneous Panels” By Kyung So Im; M. Hashem Pesaran; Yongcheol Shin
  4. Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions By Alain Hecq; Marie Ternes; Ines Wilms
  5. Robust Inference on Correlation under General Heterogeneity By Liudas Giraitis; Yufei Li; Peter C.B. Phillips
  6. Bridging the Covid-19 Data and the Epidemiological Model using Time-Varying Parameter SIRD Model By Cem Cakmakli; Yasin Simsek
  7. Multidimensional dynamic factor models By Matteo Barigozzi; Filippo Pellegrino
  8. External Instrument SVAR Analysis forNoninvertible Shocks By Mario Forni; Luca Gambetti; Giovanni Ricco
  9. Sparse Trend Estimation By Richard K. Crump; Nikolay Gospodinov; Hunter Wieman
  10. Local Volatility in Interest Rate Models By V. M. Belyaev
  11. An MCMC Approach to Classical Estimation By Victor Chernozhukov; Han Hong

  1. By: Alain Hecq; Luca Margaritella; Stephan Smeekes
    Abstract: In this paper we construct an inferential procedure for Granger causality in high-dimensional non-stationary vector autoregressive (VAR) models. Our method does not require knowledge of the order of integration of the time series under consideration. We augment the VAR with at least as many lags as the suspected maximum order of integration, an approach which has been proven to be robust against the presence of unit roots in low dimensions. We prove that we can restrict the augmentation to only the variables of interest for the testing, thereby making the approach suitable for high dimensions. We combine this lag augmentation with a post-double-selection procedure in which a set of initial penalized regressions is performed to select the relevant variables for both the Granger causing and caused variables. We then establish uniform asymptotic normality of a second-stage regression involving only the selected variables. Finite sample simulations show good performance, an application to investigate the (predictive) causes and effects of economic uncertainty illustrates the need to allow for unknown orders of integration.
    Date: 2023–02
  2. By: Jan Pablo Burgard; Matthias Neuenkirch; Dennis Umlandt
    Abstract: Recursively identified vector autoregressive (VAR) models often lead to a counterintuitive response of prices (and output) shortly after a monetary policy shock. To overcome this problem, we propose to estimate the VAR parameters under the restriction that economic theory is not violated, while the shocks are still recursively identified. We solve this optimization problem under non-linear constraints using an augmented Lagrange solution approach, which adjusts the VAR coefficients to meet the theoretical requirements. In a generalization, we allow for a (minimal) rotation of the Cholesky matrix in addition to the parameter restrictions. Based on a Monte Carlo study and an empirical application, we show that particularly the “almost recursively identified approach with parameter restrictions” leads to a solution that avoids an estimation bias, generates theory-consistent impulse responses, and is as close as possible to the recursive scheme.
    Keywords: monetary policy transmission, non-linear optimization, price puzzle, recursive identification, rotation, sign restrictions
    JEL: C32 E52 E58
    Date: 2023
  3. By: Kyung So Im; M. Hashem Pesaran; Yongcheol Shin
    Abstract: This article is our personal perspective on the IPS test and the subsequent developments of unit root and cointegration tests in dynamic panels with and without cross-section dependence. In this note, we discuss the main idea behind the test and the publication process that led to Im, Pesaran and Shin (2003).
    Keywords: Dickey and Fuller statistic, stationarity, panel unit root tests, prevalence of unit roots
    JEL: C01 C23
    Date: 2023
  4. By: Alain Hecq; Marie Ternes; Ines Wilms
    Abstract: Reverse Unrestricted MIxed DAta Sampling (RU-MIDAS) regressions are used to model high-frequency responses by means of low-frequency variables. However, due to the periodic structure of RU-MIDAS regressions, the dimensionality grows quickly if the frequency mismatch between the high- and low-frequency variables is large. Additionally the number of high-frequency observations available for estimation decreases. We propose to counteract this reduction in sample size by pooling the high-frequency coefficients and further reduce the dimensionality through a sparsity-inducing convex regularizer that accounts for the temporal ordering among the different lags. To this end, the regularizer prioritizes the inclusion of lagged coefficients according to the recency of the information they contain. We demonstrate the proposed method on an empirical application for daily realized volatility forecasting where we explore whether modeling high-frequency volatility data in terms of low-frequency macroeconomic data pays off.
    Date: 2023–01
  5. By: Liudas Giraitis; Yufei Li; Peter C.B. Phillips (Cowles Foundation, Yale University)
    Abstract: Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or cross-correlation when time series are not independent identically dis-tributed random variables, pointing to the need for more robust procedures. Recent tests for serial correlation and cross correlation in Dalla, Giraitis, and Phillips (2022) provide a more robust approach, allowing for heteroskedasticity and dependence in un-correlated data under restrictions that require a smooth, slowly-evolving deterministic heteroskedasticity process. The present work removes those restrictions and validates the robust testing methodology for a wider class of heteroskedastic time series models and innovations. The updated analysis given here enables more extensive use of the method-ology in practical applications. Monte Carlo experiments conÞrm excellent Þnite sample performance of the robust test procedures even for extremely complex white noise pro-cesses. The empirical examples show that use of robust testing methods can materially reduce spurious evidence of correlations found by standard testing procedures.
    Date: 2023–02
  6. By: Cem Cakmakli; Yasin Simsek
    Abstract: This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the generalized autoregressive score modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases using a mixed-frequency setting. Results suggest that these cases' effects on the parameter estimates might be sizeable. Full sample results show that the flexible framework accurately captures the successive waves of the pandemic. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic's current stance. This superior performance, in turn, transforms into accurate predictions of the confirmed and death cases.
    Date: 2023–01
  7. By: Matteo Barigozzi; Filippo Pellegrino
    Abstract: This paper generalises dynamic factor models for multidimensional dependent data. In doing so, it develops an interpretable technique to study complex information sources ranging from repeated surveys with a varying number of respondents to panels of satellite images. We specialise our results to model microeconomic data on US households jointly with macroeconomic aggregates. This results in a powerful tool able to generate localised predictions, counterfactuals and impulse response functions for individual households, accounting for traditional time-series complexities depicted in the state-space literature. The model is also compatible with the growing focus of policymakers for real-time economic analysis as it is able to process observations online, while handling missing values and asynchronous data releases.
    Date: 2023–01
  8. By: Mario Forni (Università di Modena e Reggio Emilia, CEPR and RECent); Luca Gambetti (Universitat Autònoma de Barcelona, BSE, Università di Torino, CCA); Giovanni Ricco (École Polytechnique, University of Warwick, OFCE-SciencesPo, and CEPR)
    Keywords: Proxy-SVAR, SVAR-IV, Impulse response functions, Variance Decomposition, Historical Decomposition, Monetary Policy Shock
    JEL: C32 E32
    Date: 2022–01–29
  9. By: Richard K. Crump; Nikolay Gospodinov; Hunter Wieman
    Abstract: The low-frequency movements of many economic variables play a prominent role in policy analysis and decision-making. We develop a robust estimation approach for these slow-moving trend processes, which is guided by a judicious choice of priors and is characterized by sparsity. We present some novel stylized facts from longer-run survey expectations that inform the structure of the estimation procedure. The general version of the proposed Bayesian estimator with a slab-and-spike prior accounts explicitly for cyclical dynamics. The practical implementation of the method is discussed in detail, and we show that it performs well in simulations against some relevant benchmarks. We report empirical estimates of trend growth for U.S. output (and its components), productivity, and annual mean temperature. These estimates allow policymakers to assess shortfalls and overshoots in these variables from their economic and ecological targets.
    Keywords: sparsity; Bayesian inference; latent variable models; trend output growth; slow-moving trends
    JEL: C13 C30 C33 E27 E32
    Date: 2023–02–01
  10. By: V. M. Belyaev
    Abstract: A new approach to Local Volatility implementation in the interest rate model is presented. The major tool of this approach is a small volatility approximation. This approximation works very well and it can be used to calibrate all ATM swaptions. It works fast and accurate. In order to reproduce all available swaption prices we need to take into account the dependence of forward volatility on the current swap rate. Here we assume that forward volatility is a deterministic function on strike, tenor, and expiration at every point on the grid. We determine these functions and apply them in Monte-Carlo calculations. It was demonstrated that this approach works well. However, in the case of short term and low tenor swaptions we observed errors in swaption pricing. To fix this problem we need to modify the scenario generation process.
    Date: 2023–01
  11. By: Victor Chernozhukov; Han Hong
    Abstract: This paper studies computationally and theoretically attractive estimators called the Laplace type estimators (LTE), which include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods. The approach generates an alternative to classical extremum estimation and also falls outside the parametric Bayesian approach. For example, it offers a new attractive estimation method for such important semi-parametric problems as censored and instrumental quantile, nonlinear GMM and value-at-risk models. The LTE's are computed using Markov Chain Monte Carlo methods, which help circumvent the computational curse of dimensionality. A large sample theory is obtained for regular cases.
    Date: 2023–01

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