Abstract
Few studies in the literature have argued or empirically shown that the link between domestic investment and an uncertainty measure is nonlinear, or that the response of investment to uncertainty is asymmetric. None have used asymmetric error-correction modeling and asymmetric cointegration to address the issue. In this paper, we fill this gap by first applying the symmetric and linear ARDL approach of Pesaran et al. (2001) and then applying the asymmetric and nonlinear ARDL approach of Shin et al. (2014) to show that the link between the aforementioned variables is indeed nonlinear in all G7 countries, and that the effects of policy uncertainty on domestic investment is asymmetric in the short run and in the long run, again, in all G7 countries.
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Notes
For more information and source of the data visit Economic Uncertainty Policy Group: http://www.policyuncertainty.com/europe_monthly.html.
Since data were monthly, Foerster used Chicago Fed National Activity Index as a measure of economic activity. Note also VIX denotes Chicago Board Options Exchange Volatility Index.
After all, they are the same by deduction. To see this we can easily solve (1) for εt and lag the solution by one period.
Once normalization is done we have \( \frac{{\hat{\lambda}}_2}{-{\hat{\lambda}}_1}=\hat{b},\frac{{\hat{\lambda}}_3}{-{\hat{\lambda}}_1}=\hat{c}, and\frac{{\hat{\lambda}}_4}{-{\hat{\lambda}}_1}=\hat{d}. \)
Note that the nonlinear relation between uncertainty and investment is also demonstrated by Lensink (2002) who used a volatility measure of stock returns as a measure of uncertainty and a panel model across many developed countries. Nonlinearity was introduced by including uncertainty measure and its square in the model. A similar approach using firm level data is also adopted by Bo and Lensink (2005).
See Shin et al. (2014, p. 219). This proposition is based on dependency between the two partial sum variables.
These unit root results are available upon request.
Note that we have excluded reporting the estimates attached to the dependent variable in all error-correction models to save space. However, the original version of this paper includes 14 tables where we report all coefficient estimates and diagnostics. They are available upon request from corresponding author.
Note that in some cases such as the U.S., cointegration is supported by both the F test and the t test, but that is due to significant income effect and not uncertainty effect.
Note that the LM statistic supported autocorrelation-free residuals in all nonlinear models, and the RESET statistic revealed misspecification only in two models. Finally, the CUSUM and CUSUMSQ tests supported stability of coefficient estimates in nearly all models. As mentioned, these results are available upon request.
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APPENDIX
APPENDIX
1.1 Data Definition and Sources
1.1.1 Canada
Quarterly data over the period 1985 I -2017 IV for the U.S. and Canada, 1997I – 2017 IV for the U.K. and Italy, 1993I-2017IV for Germany, 1987I-2017IV for France and 1994I-2017IV for Japan are used to carry out the estimation. The main restriction for selected years is availability of data on policy uncertainty. Data are collected from the following sources:
(a) International Financial Statistics (IFS) of International Monetary Fund (IMF).
(b) Economic Policy Uncertainty Group:
http://www.policyuncertainty.com/us_monthly.html
(c) Federal Reserve Bank of St. Louis (FRED)
Variables
I = Real Gross Capital Formation Index. Data come from Source (c).
Y = Real GDP Index. Data come from Source (c).
R = Interest rate. Interest rate on 3-Month Treasury Bill from Source (a).
PU = Policy uncertainty. Data come from Source (b).
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Bahmani-Oskooee, M., Maki-Nayeri, M. Asymmetric Effects of Policy Uncertainty on Domestic Investment in G7 Countries. Open Econ Rev 30, 675–693 (2019). https://doi.org/10.1007/s11079-019-09523-z
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DOI: https://doi.org/10.1007/s11079-019-09523-z