The Utility of Impulse Response Functions in Regional Analysis: Some Critical Issues

1993 ◽  
Vol 15 (2) ◽  
pp. 199-222 ◽  
Author(s):  
Jeff B. Cromwell ◽  
Michael J. Hannan
Keyword(s):  
Impulse Response ◽  
Regional Analysis ◽  
Model Specification ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressive ◽  
Critical Issues ◽  
Order Determination ◽  
Selection Order ◽  
Actual Size

Regional scientists have long been interested in measuring the effects of various external and internal stimuli on a regional economy. Measuring the actual size and timing of exogenous and endogenous impacts has been of special interest, as numerical or estimation techniques allow regional actors (governments, business, and others) to make policy-type probability statements and actions in response to changes to these stimuli. Recently, the use of vector autoregressive (VAR) models and, consequently, impulse response functions has become increasingly popular. This paper will closely examine the VAR methodology and its assumptions and will address the types of empirical issues that arise from actual regional implementation. The issues of stationarity, model specification and selection, order determination, and impulse responses are discussed.

Results and Discussion 2

2014 ◽  
pp. 180-225
Keyword(s):  
Financial Development ◽  
Impulse Response ◽  
Productivity Growth ◽  
Variance Decomposition ◽  
Dynamic Responses ◽  
Response Analysis ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressive ◽  
The Empirical Analysis

The empirical analysis of this chapter provides insights into the functioning of the economies of three selected countries. Later in the chapter, the dynamic responses of the model to shocks in indicators of financial development are investigated. To obtain credible impulse response analysis, economic theory is used to set the required identifying restrictions instead of using an “unrestricted” vector autoregressive model. The structural form of the model then is summarised in the chapter by the variance decomposition and impulse response functions. The general results from impulse response functions advocate the theory of financial intermediation arguing that the development of the financial market helps to promote economic growth. Furthermore, the results of variance decomposition shows that different measures of financial development influence the variation of growth variables, particularly investment, savings, and productivity growth.

Estimating Impulse-Response Functions for Macroeconomic Models using Directional Quantiles

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gabriel Montes-Rojas
Keyword(s):  
Impulse Response ◽  
Reduced Form ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Long Term Effects ◽  
Vector Autoregressive ◽  
Conditional Effects ◽  
Pass Through ◽  
Exchange Rate Pass Through ◽  
Multivariate Quantile

Abstract A multivariate vector autoregressive model is used to construct the distribution of the impulse-response functions of macroeconomics shocks. In particular, the paper studies the distribution of the short-, medium-, and long-term effects after a shock. Structural and reduced form quantile vector autoregressive models are developed where heterogeneity in conditional effects can be evaluated through multivariate quantile processes. The distribution of the responses can then be obtained by using uniformly distributed random vectors. An empirical example of exchange rate pass-through in Argentina is presented.

Study of Short-Run Grain Movements on the Inland Waterway System

2005 ◽  
Vol 1909 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Mark A. Thoma ◽  
Wesley W. Wilson
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Important Variable ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressive ◽  
Planning Models ◽  
Long Run ◽  
Short Run ◽  
Variance Decompositions

Time series techniques—particularly impulse–response functions and variance decompositions—are used to characterize the short-run relationships between 17 variables in a vector autoregressive model designed to trace the short-run interconnections among variables affecting lockages on the Mississippi and Illinois Rivers. The model contains five categories of variables: lockages, barge rates, grain bids, rail rates, and rail deliveries. Variance decompositions are constructed that identify barge rates as the most important variable affecting lockages at both short and long horizons. Barge rates are, in turn, explained largely by lockages and rail rates, indicating two-way feedback or bidirectional causality between lockages and barge rates. Impulse–response functions are also examined. The variance decompositions indicate that barge rates are important in explaining lockages, and the impulse–response functions show how lockages and other variables respond to such shocks. In general, there is a substitution away from barge transportation and toward rail transportation when barge rates increase. The results are useful for illuminating the causal relationships among variables in the model and for understanding behavioral relationships present in the data and can be used to guide short- and long-run planning models. For example, many planning models assume that barge traffic does not respond significantly to changes in barge rates; however, results obtained here imply that barge traffic and rail deliveries do respond to such changes. This potentially important implication illustrates the usefulness of the time series techniques used.

Impulse-response functions and anthropogenic CO2

1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer
Keyword(s):  
Impulse Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Anthropogenic Co2

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

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