FEVDForecast Error Variance Decomposition
FEVDFixed-Effects Vector Decomposition (economics)
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The forecast error variance decompositions (FEVDs) for some countries are briefly described in the text below, though their detailed output is available upon request from the authors.
The short-run dynamics of exchange rate can be examined using IRFs, based on the structural identification and FEVD.
Because it is new to the tax evasion literature, we present the key concepts of the Fixed Effects Vector Decomposition (FEVD) approach (Plumper and Troeger, 2004) and explain its application to our case.
For this reason in addition to IRFs, we would like to analyze FEVDs as shown below since FEVD shows the proportion of the variability of the errors share of variance due to a variable.
The FEVD in Figure 9 describes the effect of a shock on the variables [RIC.sup.E], [RIC.sup.S], [RIC.sup.O], and ISM on the forecast error variance of ISM.
Later, FEVD results will reveal causality in longer horizon (still short-run causality when compared to causality within a cointegration relationship).
Columns (3) and (4) present estimated coefficients from a similar model, but use OLS and FEVD estimations, respectively.
These findings contradict the results of the FEVD analysis.
The decline in labor substantially reduces the impact of productivity on output, and helps generate the result presented in the next subsection that neutral productivity shocks do not contribute significantly to output fluctuations based on the FEVD of output.
OLS was used to estimate regressions without district fixed effects, but the fixed effect variance-decomposition (FEVD) method developed by Plumper and Troeger (2007) and described in the previous subsection was employed when district fixed effects were included.
The Forecast Error Variance Decomposition (henceforth FEVD) in figure 5 measures the contribution of different variables in the model to the volatility of the variable of interest.