Impulse Response Estimation by Smooth Local Projections

2019 ◽  
Vol 101 (3) ◽  
pp. 522-530 ◽  
Author(s):  
Regis Barnichon ◽  
Christian Brownlees
Keyword(s):  
Impulse Response ◽  
Simulation Study ◽  
Structural Identification ◽  
Monetary Shocks ◽  
Spline Smoothing ◽  
Impulse Responses ◽  
B Spline ◽  
Efficiency Cost ◽  
Local Projections

Local projections (LP) is a popular methodology for the estimation of impulse responses (IR). Compared to the traditional VAR approach, LP allow for more flexible IR estimation by imposing weaker assumptions on the dynamics of the data. The nonparametric nature of LP comes at an efficiency cost, and in practice, the LP estimator may suffer from excessive variability. In this work, we propose an IR estimation methodology based on B-spline smoothing called smooth local projections (SLP). The SLP approach preserves the flexibility of standard LP, can substantially increase precision, and is straightforward to implement. A simulation study shows that SLP can deliver substantial gains in IR estimation over LP. We illustrate our technique by studying the effects of monetary shocks where we highlight how SLP can easily incorporate commonly employed structural identification strategies.

scholarly journals Local Projections and VARs Estimate the Same Impulse Responses

Econometrica ◽  
2021 ◽  
Vol 89 (2) ◽  
pp. 955-980
Author(s):  
Mikkel Plagborg-Møller ◽  
Christian K. Wolf
Keyword(s):  
Structural Identification ◽  
Finite Sample ◽  
Impulse Responses ◽  
Vector Autoregressions ◽  
Reduction Techniques ◽  
Long Run ◽  
Finite Sample Properties ◽  
Short Run ◽  
Local Projections ◽  
Dimension Reduction Techniques

We prove that local projections (LPs) and Vector Autoregressions (VARs) estimate the same impulse responses. This nonparametric result only requires unrestricted lag structures. We discuss several implications: (i) LP and VAR estimators are not conceptually separate procedures; instead, they are simply two dimension reduction techniques with common estimand but different finite‐sample properties. (ii) VAR‐based structural identification—including short‐run, long‐run, or sign restrictions—can equivalently be performed using LPs, and vice versa. (iii) Structural estimation with an instrument (proxy) can be carried out by ordering the instrument first in a recursive VAR, even under noninvertibility. (iv) Linear VARs are as robust to nonlinearities as linear LPs.

Optimization of regularized B-spline smoothing for turbulent Lagrangian trajectories

2021 ◽  
pp. 110376
Author(s):  
Adam Cheminet ◽  
Yasar Ostovan ◽  
Valentina Valori ◽  
Christophe Cuvier ◽  
Fançois Daviaud ◽  
...  
Keyword(s):  
Spline Smoothing ◽  
B Spline

scholarly journals B-Spline Impulse Response Functions of Rigid Bodies for Fluid-Structure Interaction Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
S. Zhou-Bowers ◽  
D. C. Rizos
Keyword(s):  
Impulse Response ◽  
Dynamic Models ◽  
Interaction Analysis ◽  
Fluid Structure Interaction ◽  
Rigid Bodies ◽  
The Body ◽  
Impulse Response Functions ◽  
Fluid Structure ◽  
B Spline ◽  
Structure Interaction

Reduced 3D dynamic fluid-structure interaction (FSI) models are proposed in this paper based on a direct time-domain B-spline boundary element method (BEM). These models are used to simulate the motion of rigid bodies in infinite or semi-infinite fluid media in real, or near real, time. B-spline impulse response function (BIRF) techniques are used within the BEM framework to compute the response of the hydrodynamic system to transient forces. Higher-order spatial and temporal discretization is used in developing the kinematic FSI model of rigid bodies and computing its BIRFs. Hydrodynamic effects on the massless rigid body generated by an arbitrary transient acceleration of the body are computed by a mere superposition of BIRFs. Finally, the dynamic models of rigid bodies including inertia effects are generated by introducing the kinematic interaction model to the governing equation of motion and solve for the response in a time-marching scheme. Verification examples are presented and demonstrate the stability, accuracy, and efficiency of the proposed technique.

Vibration Based Modal Parameters Identification and Wear Fault Diagnosis Using Laplace Wavelet

2005 ◽  
Vol 293-294 ◽  
pp. 183-192 ◽  
Author(s):  
Yanyang Zi ◽  
Xue Feng Chen ◽  
Zheng Jia He ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Impulse Response ◽  
Combustion Engine ◽  
Intake Valve ◽  
Impulse Responses ◽  
Transient Responses ◽  
Vibration Signals ◽  
Modal Parameters Identification ◽  
Non Stationary Signal ◽  
Correlation Filtering

Wavelet transform is a powerful technique well suited to non-stationary signal processing. The properties of wavelet are determined by its basis function. In the fields of modal analysis, mechanical condition monitoring and fault diagnosis, impulse responses or transient responses are very common signals to be analyzed. The Laplace wavelet is a single-sided damped exponential wavelet and is a desirable wavelet basis to analyze signals of impulse response. A correlation filtering approach is introduced using the Laplace wavelet to identify the impulse response from vibration signals. Successful results are obtained in identifying the natural frequency of a hydro-generator shaft, and diagnosing the wear fault of intake valve of an internal combustion engine.

scholarly journals Impulse response of a generalized fractional second order filter

2012 ◽  
Vol 15 (1) ◽  
Author(s):  
Zhuang Jiao ◽  
YangQuan Chen
Keyword(s):  
Impulse Response ◽  
Stability Property ◽  
Asymptotic Properties ◽  
Second Order ◽  
Impulse Responses ◽  
Critical Stability ◽  
Numerical Examples ◽  
Order Filter

AbstractThe impulse response of a generalized fractional second order filter of the form (s 2α + as α + b)−γ is derived, where 0 < α ≤ 1, 0 < γ < 2. The asymptotic properties of the impulse responses are obtained for two cases, and within these two cases, the properties are shown when changing the value of γ. It is shown that only when (s 2α + as α + b)−1 has the critical stability property, the generalized fractional second order filter (s 2α + as α + b)−γ has different properties as we change the value of γ. Finally, numerical examples to illustrate the impulse response are provided to verify the obtained results.

THE PRICE PUZZLE AND VAR IDENTIFICATION

2014 ◽  
Vol 19 (8) ◽  
pp. 1880-1887 ◽  
Author(s):  
Arturo Estrella
Keyword(s):  
Monetary Policy ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Reduced Form ◽  
Impulse Responses ◽  
Minimal Set ◽  
Price Puzzle ◽  
Single Term ◽  
Structural Vars

In structural VARs, unexpected monetary tightening often leads to the price puzzle, a counterintuitive increase in inflation in the impulse response function. The identification of impulse responses requires at least a minimal set of structural assumptions, and models exhibiting the price puzzle typically use standard assumptions focusing mainly on relationships among contemporaneous disturbances. This note uses a well-established stylized fact, the long lags of monetary policy, to motivate a simple additional identifying assumption. The assumption eliminates a single term in one equation of the reduced form, and with it the price puzzle.

Analyzing operators of 3-D imaging software with impulse responses

Geophysics ◽  
1999 ◽  
Vol 64 (4) ◽  
pp. 1079-1092 ◽  
Author(s):  
William A. Schneider
Keyword(s):  
Impulse Response ◽  
Imaging Techniques ◽  
Velocity Model ◽  
Impulse Responses ◽  
Step Size ◽  
Data Set ◽  
Time Migration ◽  
Offset Time ◽  
Zero Offset ◽  
Algorithm Implementation

No processing step changes seismic data more than 3-D imaging. Imaging techniques such as 3-D migration and dip moveout (DMO) generally change the position, amplitude, and phase of reflections as they are converted into reflector images. Migration and DMO may be formulated in many different ways, and various algorithms are available for implementing each formulation. These algorithms all make physical approximations, causing imaging software to vary with algorithm choice. Imaging software also varies because of additional implementation approximations, such as those that trade accuracy for efficiency. Imaging fidelity, then, generally depends upon algorithm, implementation, specific software parameters (such as aperture, antialias filter settings, and downward‐continuation step size), specific acquisition parameters (such as nominal x- and y-direction trace spacings and wavelet frequency range), and, of course, the velocity model. Successfully imaging the target usually requires using appropriate imaging software, parameters, and velocities. Impulse responses provide an easy way to quantitatively understand the operators of imaging software and then predict how specific imaging software will perform with the chosen parameters. (An impulse response is the image computed from a data set containing only one nonzero trace and one arrival on that trace.) I have developed equations for true‐amplitude impulse responses of 3-D prestack time migration, 3-D zero‐offset time migration, 3-D exploding‐reflector time migration, and DMO. I use these theoretical impulse responses to analyze the operators of actual imaging software for a given choice of software parameters, acquisition parameters, and velocity model. The procedure is simple: compute impulse responses of some software; estimate position, amplitude, and phase of the impulse‐response events; and plot these against the theoretical values. The method is easy to use and has proven beneficial for analyzing general imaging software and for parameter evaluation with specific imaging software.

Digital simulation of photoacoustic impulse responses

1986 ◽  
Vol 64 (9) ◽  
pp. 1049-1052 ◽  
Author(s):  
Richard M. Miller
Keyword(s):  
Finite Difference ◽  
Impulse Response ◽  
Depth Distribution ◽  
Digital Simulation ◽  
Heat Diffusion ◽  
Finite Difference Approximation ◽  
Diffusion Equations ◽  
Difference Approximation ◽  
Impulse Responses ◽  
Solid Samples

Impulse-response photoacoustic spectroscopy provides information on the depth distribution of chromophores in solid samples. To gain an understanding of the way in which sample properties affect the impulse response, a digital model has been generated. This model is based on discretization of time and space coupled with a finite-difference approximation of the governing heat-diffusion equations. The simulations are compared with experimental results.

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