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This paper uses the strategy and data of Blanchard and Perotti (BP) to identify fiscal shocks and estimate fiscal multipliers for the United States. With these results, it computes the cumulative multiplier of Ramey and Zubairy (2018), now common in the literature. It finds that, contrary to the peak and through multipliers reported by BP, the cumulative tax multiplier is much larger than the cumulative spending one. Hence, the conclusions depend on the definition of multiplier. This methodology is also used to estimate the effects of fiscal shocks on economic activity in eight Latin American countries. The results suggest that the fiscal multipliers vary significantly across countries, and in some cases multipliers are larger than previously estimated.

Econometrics --- Macroeconomics --- Public Finance --- Fiscal Policy --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Diffusion Processes --- State Space Models --- National Government Expenditures and Related Policies: General --- Taxation, Subsidies, and Revenue: General --- Econometrics & economic statistics --- Public finance & taxation --- Structural vector autoregression --- Expenditure --- Vector autoregression --- Revenue administration --- Fiscal multipliers --- Econometric analysis --- Fiscal policy --- Expenditures, Public --- Revenue --- United States

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Global merchandise trade expanded rapidly over the last 6½ decades and its relationship with global income has seen ebbs and flows. This paper examines the shifts in this relationship using time series data over 1950-2014 and situates it in the current and longer term context. The conjunctural context comes from, among other things, the “great trade collapse” (GTC) and the global financial crisis (GFC) in 2009, and developments since then. The longer term context comes from the relative role of “globalization” and “technology” shocks in accounting for the short and long run variance of global exports and income. The paper estimates trade and income elasticities using ADL models taking account of structural breaks, and impulse response functions from structural VARs. The estimated SVAR model provides a lens to ask whether global trade and income are in a “new normal’ or only “back to (an old) normal” after the GTC and GFC.

International trade. --- Exports. --- Global Financial Crisis, 2008-2009. --- Diffusion Processes --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Econometric analysis --- Econometrics & economic statistics --- Econometrics --- Empirical Studies of Trade --- Export performance --- Exports and Imports --- Exports --- Income --- International economics --- International trade --- Macroeconomics --- National accounts --- Personal income --- Personal Income, Wealth, and Their Distributions --- State Space Models --- Structural vector autoregression --- Time-Series Models --- Trade: General --- Vector autoregression --- Russian Federation

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Conventional assessments of debt sustainability in low income countries are hampered by poor data and weaknesses in methodology. In particular, the standard International Monetary Fund-World bank debt sustainability framework relies on questionable empirical assumptions: its baseline projections ignore statistical uncertainty, and its stress tests, which are performed as robustness checks, lack a clear economic interpretation and ignore the interdependence between the relevant macroeconomic variables. This paper proposes to alleviate these problems by pooling data from many countries and estimating the shocks and macroeconomic interdependence faced by a generic, low income country. The paper estimates a panel vector autoregression to trace the evolution of the determinants of debt, and performs simulations to calculate statistics on external debt for individual countries. The methodology allows for the value of the determinants of debt to differ across countries in the long run, and for additional heterogeneity through country-specific exogenous variables. Results in this paper suggest that ignoring the uncertainty and interdependence of macroeconomic variables leads to biases in projected debt trajectories, and consequently, the assessment of debt sustainability.

Bankruptcy and Resolution of Financial Distress --- Bootstrap --- Debt Markets --- Debt sustainability --- Economic Theory & Research --- Emerging Markets --- External Debt --- Finance and Financial Sector Development --- Low income countries --- Macroeconomics and Economic Growth --- Panel vector autoregression

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The expansionary fiscal contraction (EFC) hypothesis states that fiscal austerity can increase output or consumption when a country is under heavy debt burdens because it sends positive signal about the country's solvency situation and long-term economic wellbeing. Empirical tests of this hypothesis have suffered from identification concerns due to data sources and empirical methodology. Using a sample of OECD countries between 1978 and 2014, this paper combines new IMF narrative data and the proxy structural Vector Auto-regression (SVAR) method to examine whether fiscal austerities can be expansionary when debt levels are high. Fiscal austerities are measured as 1) narrative fiscal shocks and 2) structural shocks from a proxy SVAR. Additionally, this paper uses a model-based approach to determine the cutoff debt level beyond which EFC is expected to be observed. This paper finds empirical evidence in support of the EFC hypothesis for OECD countries: results for output are driven by changes in tax rates and are robust to how one defines a high-debt regime and how one measures austerity.

Austerity --- Debt Burden --- Debt Sustainability --- Economic Crisis --- Economic Shock --- External Debt --- Fiscal Adjustment --- Fiscal Consolidation --- Fiscal Policy --- Fiscal Shock --- International Economics and Trade --- Macroeconomic Management --- Macroeconomics and Economic Growth --- Structural Vector Autoregression

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This paper addresses the identification of low-frequency macroeconomic shocks, such as technology, in Structural Vector Autoregressions. Whilst identification issues with long-run restricted VARs are well documented, the recent attempt to overcome said issues using the Max-Share approach of Francis and others (2014) and Barsky and Sims (2011) has its own shortcomings, primarily that they are vulnerable to bias from confounding non-technology shocks. A modification to the Max-Share approach and two further spectral methods are proposed to improve empirical identification. Performance directly hinges on whether these confounding shocks are of high or low frequency. Applied to US and emerging market data, spectral identifications are most robust across specifications, and non-technology shocks appear to be biasing traditional methods of identifying technology shocks. These findings also extend to the SVAR identification of dominant business-cycle shocks, which are shown will be a variance-weighted combination of shocks rather than a single structural driver.

Business Cycle --- Business Cycles and Stabilization Policies --- Economic Conditions and Volatility --- Economic Growth --- Economic Shocks --- Economic Theory and Research --- Macroeconomics and Economic Growth --- Productivity --- Science and Technology Development --- Structural Vector Autoregression --- Technology Innovation --- Technology Shock