“What Do We Know about Fiscal Multipliers?” in: Rethinking Fiscal Policy after the Crisis. Cambridge University Press, 2017. doi P. 443-482. doi
Authors: Favero C., Karamysheva M.
The Empirical evidence on fiscal multipliers is very heterogeneous. In this paper, we first survey available estimates of fiscal multipliers to try to understand their heterogeneity. We provide a general framework that allows to make the identification and specification choices made by the different authors explicit and leads hopefully to a better understanding of the heterogeneity of results.
"How do fiscal adjustments work? An empirical investigation"
Authors: Karamysheva M.
Recent empirical evidence suggests that fiscal consolidation based mainly on tax hikes has a more recessionary impact on economic growth than that based on expenditure cuts. This paper evaluates the effects of fiscal adjustment plans identified through the narrative approach on the U.S. macroeconomic activity. To do so, I incorporate fiscal plans into a vector autoregression model to investigate transmission channels of fiscal consolidation and accompanying policy. I check whether monetary policy, uncertainty, or financial markets can explain the heterogeneous effects of fiscal adjustment plans. I find that the financial market and macro uncertainty channels are the most important ones.
"The Network Effects of Fiscal Adjustments."
Authors: Briganti E., Favero C., Karamysheva M.
We study the effects of fiscal consolidations in the United States and their propagation in the production network. We use a narrative approach to identify fiscal adjustments which are exogenous to output fluctuations. Then we apply spatial econometric techniques to separate the total effect of fiscal adjustments into a direct and network component. We find that fiscal adjustments based on increased taxation are more recessionary than those based on spending cuts. Moreover, one quarter of the difference in their total output effect is explained by the stronger network propagation of taxes relative to government spending.
"Prudential policies and systemic risk: the role of interconnections."
Authors: Karamysheva M., Seregina E.
The impact of prudential policies in open economies depends on their intrinsic efficacy and the spillovers from the close financial partners. Using a sample of advanced economies, we find that prudential policy interventions significantly reduce systemic risk in the financial systems with the impact amplified through a network of financial investment links. Using the Spatial Autoregressive (SAR) model we show that the indirect (network) effect enforces the direct effect and accounts for up to 87% of total risk reduction assuming the uniform policy interventions. We are the first to perform a spillover analysis for prudential policies and uncover the importance of financial network and uniform interventions for the reduction of systemic risk.
"Fiscal Multiplier and the Size of Government Spending Shock. The case of the U.S."
Authors: German N., Karamysheva M.
This paper investigates whether the fiscal multiplier depends negatively on the size of the government spending shock. We build our hypothesis on behavioral arguments and check it empirically using U.S. data. In doing so, we adopt a non-linear Local Projection method. We address possible endogeneity issues by using government military spending and illustrate that our results are non-sensible to these concerns. Finally, we limit our analysis to the government consumption multiplier, as our hypothesis suggests strong non-constancy in this respect. We find a strong negative relationship between the government spending multiplier and the size of the shock. Results are robust to different subsamples, fiscal foresight, business cycle, and different identification schemes.
"Do We Reject Restrictions Identifying Fiscal Shocks? Identification Based on non-Gaussian Innovations."
Authors: Karamysheva M., Skrobotov A.
This paper is devoted to fiscal shock identification based on the assumption of non-Gaussianity of the errors, which can be easily tested. We use additional co-kurtosis conditions in GMM estimation of the AB-model to estimate the dynamic effects of fiscal shocks and find fiscal multipliers in the U.S. economy. Our approach results in higher tax multipliers on average relative to (Blanchard and Perotti 2002) and (Leeper, Walker, and Yang 2013). Testing the restrictions, we are not able to reject them in (Blanchard and Perotti 2002) model. Once we control for fiscal foresight, we can reject restrictions both individually and altogether. Finally, comparing elasticities of tax revenue to output to elasticities found in the literature, rejecting most of them, we are not able to reject the one of (Caldara and Kamps 2017).
"Design of Fiscal Adjustments: Plans versus Shocks"
Authors: Favero C., Karamysheva M.
This paper analyzes the measurement of the output effects of fiscal stabilization policies, by assessing the relevance of different methods in determining the heterogeneity of available results and by evaluating comparatively the different approaches proposed in the literature. We compare fiscal plans and fiscal shocks in the U.S. over the period 1978q1-2014q4 by estimating the truncated moving average (MA). We show that plans nest shocks. Our results suggest that the use of shocks instead of plans causes the size and the interpretation of fiscal multipliers to be affected.
"Do market-based networks reflect true exposures between banks?"
Authors: Craig B., Karamysheva M., Salakhova D.
Due to the lack of or poor access to the data on real exposures between banks, several methods have been proposed to reconstruct a network using market data. However, what does this market-based network represent? In this paper, we replicate several well-known methods to construct market-based networks. Next, we build networks based on true exposures through loans and securities holdings. Then we provide graphical analysis as well as a comparison of network characteristics across different types of networks and different time periods. Our regression analysis sheds light on which balance-sheet exposures better explain the links perceived by the market. Our findings suggest that while global network structure remains stable, networks evolve over time. Regression analysis shows that (i) market identifies two banks as connected when they have similar business models defined by overlapping portfolios of loans (IL); (ii) market identifies two banks as connected when they lend to each other using interbank lending (DL), only when market network is cleaned up the noise and co-movement; (iii) market on average does not capture common exposures to similar securities and direct securities on top of co-movement and controls.