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The 2008 crisis underscored the interconnectedness of the international business cycle, with U.S. shocks leading to the largest global slowdown since the 1930s. We estimate spillover effects across major advanced country regions in a structural VAR (SVAR) using pre-crisis data. Our new method freely estimates the contemporaneous correlation matrix for underlying shocks in the VAR and (uniquely, to our knowledge) the associated uncertainty. Our results suggest that the international business cycle is largely driven by U.S. financial shocks with a significant impact from global shocks, mainly reflecting commodity prices. Other advanced economic regions play a much smaller and regional role in growth spillovers. Our findings are consistent with the emerging evidence on the current crisis.
Business cycles--Econometric models. --- Business cycles--United States--Econometric models. --- United States. --- Econometrics --- Macroeconomics --- Externalities --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Diffusion Processes --- State Space Models --- Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data) --- Commodity Markets --- Econometrics & economic statistics --- Economic growth --- Spillovers --- Vector autoregression --- Structural vector autoregression --- Business cycles --- Commodity prices --- International finance --- Prices --- United States --- Econometric models.
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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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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.
United States --- 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
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Imposing cointegration on a forecasting system, if cointegration is present, is believed to improve long-horizon forecasts. Contrary to this belief, at long horizons nothing is lost by ignoring cointegration when the forecasts are evaluated using standard multivariate forecast accuracy measures. In fact, simple univariate Box-Jenkins forecasts are just as accurate. Our results highlight a potentially important deficiency of standard forecast accuracy measures—they fail to value the maintenance of cointegrating relationships among variables—and we suggest alternatives that explicitly do so.
Econometrics --- Forecasting --- Time-Series Models --- Dynamic Quantile Regressions --- Dynamic Treatment Effect Models --- Diffusion Processes --- State Space Models --- Forecasting and Other Model Applications --- Econometrics & economic statistics --- Economic Forecasting --- Vector autoregression --- Economic forecasting
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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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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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A vector autoregression model with time-varying coefficients is used to examine the evolution of wage cyclicality in four Latin American economies: Brazil, Chile, Colombia and Mexico, during the period 1980-2010. Wages are highly pro-cyclical in all countries up to the mid-1990s except in Chile. Wage cyclicality declines thereafter, especially in Brazil and Colombia. This decline in wage cyclicality is in accordance with declining real-wage flexibility in a low-inflation environment. Controlling for compositional effects caused by changes in labor force participation along the business cycle does not alter these results.
Bayesian Estimation --- Downward Wage Rigidity --- Economic Theory & Research --- Environment --- Environmental Economics & Policies --- Governance --- Indexation --- Labor Markets --- Labor Policies --- Macroeconomics and Economic Growth --- Real Wage Cyclicality --- Social Protections and Labor --- Time Varying Coefficients --- Vector Autoregression --- Youth & Governance
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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
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Recent advances in food insecurity classification have made analytical approaches to predict and inform response to food crises possible. This paper develops a predictive, statistical framework to identify drivers of food insecurity risk with simulation capabilities for scenario analyses, risk assessment and forecasting purposes. It utilizes a panel vector-autoregression to model food insecurity distributions of 15 Sub-Saharan African countries between October 2009 and February 2019. Statistical variable selection methods are employed to identify the most important agronomic, weather, conflict and economic variables. The paper finds that food insecurity dynamics are asymmetric and past-dependent, with low insecurity states more likely to transition to high insecurity states than vice versa. Conflict variables are more relevant for dynamics in highly critical stages, while agronomic and weather variables are more important for less critical states. Food prices are predictive for all cases. A Bayesian extension is introduced to incorporate expert opinions through the use of priors, which lead to significant improvements in model performance.
Bayesian Extension --- Climate and Meteorology --- Disaster Management --- Economic Forecasting --- Expert Opinion --- Famine Risk --- Food Crisis --- Food Insecurity --- Food Security --- Forecasting --- Natural Disasters --- Panel Vector Autoregression --- Stochastic Simulation --- Variable Selection --- Weather Forecasting --- World Food Programme
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