A NOTE ON ENDOGENOUS PROPAGATION IN ONE-SECTOR BUSINESS CYCLE MODELS WITH DYNAMIC COMPLEMENTARITIES

2012 ◽  
Vol 16 (5) ◽  
pp. 791-801 ◽  
Author(s):  
Mao-Wei Hung ◽  
Shue-Jen Wu
Keyword(s):  
Business Cycle ◽  
Production Function ◽  
Impulse Response ◽  
Growth Rates ◽  
Correlation Coefficients ◽  
Propagation Mechanism ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Proposed Model ◽  
Autocorrelated Growth

When the production function includes dynamic complementarities and a Cobb--Douglas form, dynamic complementarities are an endogenous propagation mechanism of shocks. The proposed model explains several stylized facts of aggregate variables of interest, including (i) hump-shaped impulse response functions, (ii) positively autocorrelated growth rates of aggregate variables, and (iii) correlation coefficients of forecastable movements in aggregate variables.

scholarly journals Google Trends Topic-Based Uncertainty: A Multi-National Approach

2020 ◽  
Author(s):  
Florian Schütze
Keyword(s):  
Standard Deviation ◽  
Business Cycle ◽  
Impulse Response ◽  
Google Trends ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Helpful Tool ◽  
Uncertainty Measurement ◽  
Business Cycle Dynamics ◽  
National Approach

Several studies have shown that uncertainty among economic actors influences business cycle dynamics. This paper uses Google Trends topic queries to construct an uncertainty proxy that can be applied to every country where Google is active. Using a VAR approach, this paper demonstrates that the obtained impulse-response functions of main economic indicators to a one-standard deviation shock to the constructed indicator, are similar to those from an already-existing uncertainty proxy, the EPU. This is true for the G7 countries and Russia. On average, the uncertainty indicator constructed for this paper leads to more statistically significant responses than does the EPU. Thus, this paper shows that Google Trends is a helpful tool for obtaining timely information about uncertainty among economic actors. The main improvement in this uncertainty proxy is in its language independence. Existing uncertainty-measurement approaches, in contrast, rely on certain keywords that often vary across countries.

scholarly journals The Shape of Eurozone’s Uncertainty: Its Impact and Predictive Value on GDP

2020 ◽  
Vol 7 (6) ◽  
pp. 1
Author(s):  
Ralf Fendel ◽  
Nicola Mai ◽  
Oliver Mohr
Keyword(s):  
Standard Deviation ◽  
Business Cycle ◽  
Financial Markets ◽  
Impulse Response ◽  
Two Dimensions ◽  
Point Estimate ◽  
Impulse Response Functions ◽  
Response Functions ◽  
The Business Cycle ◽  
The Impact

This paper examines the role of uncertainty in the context of the business cycle in the euro area. To gain a more granular perspective on uncertainty, the paper decomposes uncertainty along two dimensions: First, we construct the four different moments of uncertainty, including the point estimate, the standard deviation, the skewness and the kurtosis. The second dimension of uncertainty spans along three distinct groups of economic agents, including consumers, corporates and financial markets. Based on this taxonomy, we construct uncertainty indices and assess the impact on real GDP via impulse response functions and further investigate their informational value in rolling out-of-sample GDP forecasts. The analysis lends evidence to the hypothesis that higher uncertainty expressed through the point estimate, a larger standard deviation among confidence estimates, positive skewness and a higher kurtosis are all negatively correlated with the business cycle. The impulse response functions reveal that in particular the first and the second moment of uncertainty cause a permanent effect on GDP with an initial decline and a subsequent overshoot. We find uncertainty in the corporate sector to be the main driver behind this observation, followed by financial markets’ uncertainty whose initial effect on GDP is comparable but receding much faster. While the first two moments of uncertainty improve GDP forecasts significantly, both the skewness and the kurtosis do not augment the forecast quality any further.

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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