International Spillovers from U.S. Fiscal Policy Shocks

Abstract

I estimate the effect of U.S. government spending and tax shocks on Canada and the U.K. from 1975 to 2014, and on Japan from 1979 to 2014. Spending and tax shocks are identified using sign restrictions on the impulse responses from a vector autoregression (VAR). I find that spillover effects of expansionary fiscal shocks are not uniform across countries, though for all three countries they result in economically significant GDP increases in the short run. In addition, government spending shocks have larger effects than net tax shocks. Altogether, the results support the idea that some countries may benefit significantly from expansionary U.S. fiscal policy.

Appendices

Appendix : A: Generating Candidate Responses

To generate candidate sets of impulse responses it is useful to note that there is an arbitrarily large number of matrices for which $BB^{\prime } = \widehat {\Sigma }_{u}$. As Fry and Pagan (2007) emphasize, each of these matrices represents a separate structural model, all of which are observationally equivalent in the sense that they produce residuals with the same covariance structure. To generate each set of impulse responses, I start with the Cholesky factorization (P) and multiply by an orthonormal matrix Q which has the property that Q ^′ Q = Q Q ^′=I. Accordingly, $PQQ^{\prime }P^{\prime } = \widehat {\Sigma }_{u}$. The sign restrictions I impose are restrictions on the responses of the first three variables in the VAR (government spending, g _t , net taxes, t _t , and U.S. GDP, y _t , respectively). The matrix Q that I construct therefore makes use of a Givens rotation in three dimensions. Specifically, for each set of candidate responses I draw (θ ₁, θ ₂, θ ₃) from a uniform distribution on [0,π] and calculate

$$Q_{3} = \left[ \begin{array}{ccc} \cos(\theta_{1}) & -\sin(\theta_{1}) & 0 \\ \sin(\theta_{1}) & \cos(\theta_{1}) & 0 \\ 0 & 0 & 1 \end{array} \right] \times \left[ \begin{array}{ccc} \cos(\theta_{2}) & 0 & -\sin(\theta_{2}) \\ 0 & 1 & 0 \\ \sin(\theta_{2}) & 0 & \cos(\theta_{2}) \end{array} \right] \times \left[ \begin{array}{ccc} 1 & 0 & 0 \\ 0 & \cos(\theta_{3}) & -\sin(\theta_{3}) \\ 0 & \sin(\theta_{3}) & \cos(\theta_{3}) \end{array} \right], $$

where Q ₃ is the upper left 3 × 3 section of Q. The rest of Q consists of ones on the diagonal and zeros everywhere else.

Appendix : B: Data

Data for the U.S. variables is from the U.S. Bureau of Economic Analysis (BEA) National Income and Product Accounts (NIPA). Bilateral trade data are from the BEA’s International Transactions Accounts. Foreign data is from the International Monetary Fund’s International Financial Statistics Database. U.S. NIPA and IMF data are seasonally adjusted by the source. The international trade data were seasonally adjusted by me using the U.S. Census Bureau’s X12-ARIMA.⁷

U.S. real net taxes are from NIPA Table 3.1 and calculated as the sum of Current Tax Receipts (line 2), Contributions for Government Social Insurance (line 7) and Current Transfer Receipts (line 13) minus the sum of Current Transfer Payments (line 19), Interest Payments (line 24) and Subsidies (line 27). Interest rate differentials are calculated as the U.S. rate minus the foreign rate. Ex-post real interest rates are calculated by subtracting the quarterly CPI inflation rate. The real bilateral exchange rate is calculated as:

$$\text{Nominal Exchange Rate (units of foreign / \$ U.S.) }\times \frac{\text{US CPI}}{\text{Foreign CPI}}. $$

The trade balance is calculated as the log ratio of exports to imports. The Ramey defense news variable was downloaded from Valerie Ramey’s website and the Romer tax variable was downloaded from David Romer’s website.