Spillovers of uncertainty shocks: Evidence from GVAR model

• Thesis title in Czech: Přelévání šoků nejistoty: Důkazy z modelu GVAR
• Thesis title in English: Spillovers of uncertainty shocks: Evidence from GVAR model
• Academic year of topic announcement: 2021/2022
• Thesis type: diploma thesis
• Thesis language: angličtina
• Department: Institute of Economic Studies (23-IES)
• Supervisor: PhDr. Jaromír Baxa, Ph.D.
• Author: hidden - assigned by the advisor
• Date of registration: 30.06.2022
• Date of assignment: 30.06.2022
• Date and time of defence: 21.09.2023 09:00
• Venue of defence: Opletalova, O314, místnost. č. 314
• Date of electronic submission: 01.08.2023
• Date of proceeded defence: 21.09.2023
• Opponents: prof. Ing. et Ing. Luboš Komárek, M.Sc., MBA, Ph.D.

Guidelines

Methodology:

GVAR is an adaptation of vector autoregression model, which allows modelling of highly dimensional data. It is one of a few global models currently used in macroeconomic modelling and it’s main asset lies in „theoretical coherence and statistical consistence“ (Di Mauro, 2013). Other models do not present a closed system and are incomplete, which makes it more complex to use them for simulations. (Granger, 2007).
It is based on Pesaran et al. (2004). The concept was developed in the aftermath of the Asian financial crisis as a model to incorporate regional and global shocks, which were relevant for the crisis and caused economic damage.
It is a two-step process in which „small VARs“ are computed for each country using regional variables and weighted average of „world“ variables to account for foreign influence.This is being done using augmented VAR with „world“ bariables treated as weakly exogenous (which is a reasonable assumption for a regional model). In the second step the „small VARs“ are put together and estimated simultaneously as one global (G)VAR.
Apparently, the methodology is well suited for multi-country datasets with a number of local or regional data, which is precisely the case in this thesis.
Data for this project will include standard macroeconomic data from renowned resources such as Eurostat, IMF, etc, and Economic Policy Uncertainty Index, which is an index based on text analysis, where frequency of certain keywords is translated into a numerical value of the index. It is availabe for major European countries and for the most important world economies.

Main research questions:

1. What is the real influence of uncertainty on the economy?
2. How international spillovers of uncertainty affect international?
3. Does uncertainty affect monetary policy transmission?

Expected Contribution:

The thesis should provide insight into how uncertainty affects other economic variables in the European countries. Due to the methodology used, the conclusion both about the effects of uncertainty shocks themselves but also about transmission and interaction of those shocks in Europe. Especially implications for monetary policies of different central banks interactions could constitute an interesting contribution of this thesis.

References

Castelnuovo, E., Lim, G., & Pellegrino, G. (2017). A short review of the recent literature on uncertainty. Australian Economic Review, 50(1), 68-78

Bloom, N. (2014). Fluctuations in uncertainty. Journal of Economic Perspectives, 28(2), 153-76.

Di Mauro, F., & Pesaran, M. H. (Eds.). (2013). The GVAR handbook: Structure and applications of a macro model of the global economy for policy analysis. OUP Oxford

Granger, C. W., & Jeon, Y. (2007). Evaluation of global models. Economic Modelling, 24(6), 980-989.

Caballero, R. J. (1990). Consumption puzzles and precautionary savings. Journal of monetary economics, 25(1), 113-136

Bloom, N. (2017). Observations on uncertainty. Australian Economic Review, 50(1), 79-84

Bernanke, B. S. (1983). Irreversibility, uncertainty, and cyclical investment. The quarterly journal of economics, 98(1), 85-106

Bloom, N. (2009). The impact of uncertainty shocks. econometrica, 77(3), 623-685

Bloom, N., Floetotto, M., Jaimovich, N., Saporta‐Eksten, I., & Terry, S. J. (2018). Really uncertain business cycles. Econometrica, 86(3), 1031-1065

Cesa-Bianchi, A., Pesaran, M. H., & Rebucci, A. (2014). Uncertainty and economic activity: A global perspective. CAFE Research Paper, (14.03)

Burriel, P., & Galesi, A. (2018). Uncovering the heterogeneous effects of ECB unconventional monetary policies across euro area countries. European Economic Review, 101, 210-229

Pesaran, M. H., Schuermann, T., & Weiner, S. M. (2004). Modeling regional interdependencies using a global error-correcting macroeconometric model. Journal of Business & Economic Statistics, 22(2), 129-162

Aldasoro, I., & Unger, R. (2017). External financing and economic activity in the euro area-why are bank loans special?

Preliminary scope of work

Motivation

After The Great Financial Crisis in 2007 gave rise to significant economic shocks worldwide, their implications became a topic of many research efforts. An important part of this research focused on uncertainty shocks as a part of larger economic shock and their implications. The reasoning behind this is quite straightforward. A number of variables capturing economic uncertainty share certain characteristics with other economic variables. In a highly cited paper, Bloom (2014), mentions 3 distinctive features of economic uncertainty: co-movement with real indicators, countercyclicality, and a spike in the wake of any crisis. Hence, it could be an exogenous cause of many effects, that are attributed to a crisis in general.
Numerous attempts were made to explain this relationship. Uncertainty affects both consumption and investment in a similar manner. Consumption is reduced due to an increase in precautionary savings due to uncertainty in labor income (Caballero, 1990). Investment experiences a similar reduction due to the “wait-to-see” behavior of firms. (Bernanke, 1983, Bloom, 2009). Both of these effects are consistent with the three attributes of uncertainty mentioned above. Importantly, the highly non-linear behavior of uncertainty in crises implicates potentially huge effects.
Papers on this issue use various methodologies on various datasets, e.g. Bloom (2009) uses recursively identified VAR on US economic data, and also different proxies for the economic uncertainty e.g. VIX index, responses from business surveys, cross-sectional dispersion of firm total factor productivity (Bloom, 2018).
This work should contribute to this stream of research by using another source of uncertainty data and combining it with a very handy GVAR methodology. None of those ideas is revolutionary alone, Cesa-Bianchi, et al. (2014) used GVAR for a similar reason but used the VIX index for uncertainty, and Burriel and Galesi (2018) used Economic Policy Uncertainty Index, which shall be used in this thesis. Still, the connection of these two tools may offer new perspectives on the researched issues and, since data from different countries are analyzed, can be used to compare results for different geographies.
Literature also suggests (Aldasoro, and Unger, 2018), that the effects on the banking sector may be different from the effects on the general economy, hence author will try to analyze this issue too, if possible.


1. Motivation: Description of current research streams, specification of the research question, specifying risk and uncertainty
2. Literature review: Review of available research on the topic, review of potential methodologies and their impact on results
3. Data: Description of global economic data and data on uncertainty, special focus on the generation of uncertainty data, since it matters for their use and potential conclusions
4. Methods: Description of GVAR framework and why is it convenient in this research.
5. Results: Discussion and critical analysis of impulse response functions, robustness check using another type of regression (a different type of VAR)
6. Conclusion: Summary of results and implications for policymaking