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Ms. Era Dabla-Norris, Mr. Alun H. Thomas, Mr. Rodrigo Garcia-Verdu, and Ms. Yingyuan Chen
This paper documents stylized facts on the process of structural transformation around the world and empirically analyzes its determinants using data on real value added by sector of economic activity (agriculture, manufacturing and services) for a panel of 168 countries over the period 1970-2010. The analysis points to large differences in sector shares both across and within regions as well as for countries at similar levels of economic development. Using both linear and quantile regression methods, it finds that a large proportion of the cross-country variation in sector shares can be accounted for by country characteristics, such as real GDP per capita, demographic structure, and population size. It also finds that policy and insitutional variables, such as product market reforms, openness to trade, human and physical capital, and finance improve the baseline model’s ability to account for the variation in sectoral shares across countries.
Marcella Lucchetta and Mr. Gianni De Nicolo
This paper formulates a novel modeling framework that delivers: (a) forecasts of indicators of systemic real risk and systemic financial risk based on density forecasts of indicators of real activity and financial health; (b) stress-tests as measures of the dynamics of responses of systemic risk indicators to structural shocks identified by standard macroeconomic and banking theory. Using a large number of quarterly time series of the G-7 economies in 1980Q1-2010Q2, we show that the model exhibits significant out-of sample forecasting power for tail real and financial risk realizations, and that stress testing provides useful early warnings on the build-up of real and financial vulnerabilities.
Emmanouil Kitsios and Manasa Patnam

Front Matter Page Research Department Contents I. Introduction II. Empirical identification with heterogeneity A. Identification using panel data B. Identification using panel data and instrumental variables C. Identifying instrument: Fuel subsidies and oil price shocks D. Identifying conditions and estimation III. Data sources IV. Results V. Robustness checks A. Inference and outliers B. Instrument validity VI. Quantile estimates of the fiscal multiplier VII. Conclusions References VIII. Appendix A

Herman Kamil

D. Additional Statistical Checks VI. Alternative Explanations A. Changes in Regulations to Banks’ Foreign Currency Lending B. Differential Access to Credit and Ability to Expand Production During Crisis VII. A Closer Look at the Data: Exploiting Changes in Entire Distribution of Firms’ Dollar Debt Ratios A. Conditional Quantile Estimates: Basic Framework B. Results VIII. Conclusions References Tables 1. Number of Observations Used in Empirical Analyses 2. Descriptive Statutes for Full Sample 3. Exchange Rate Regimes and Measures

Mr. Tobias Adrian, Federico Grinberg, Nellie Liang, and Sheheryar Malik

of GaR by Initial FCI Groups E. Term Structures of Expected Median and GaR by Initial FCI Groups F. Interpreting the Intertemporal Risk-Return Tradeoff V. Robustness A. Growth at Risk in a Heteroskedastic Variance Model—Two-Step OLS Regressions B. Quantile Estimates for the AEs, Excluding the Global Financial Crisis C. Comparison of Quantile Regression Panel Estimates to U.S. Estimates VI. Conclusion Tables 1. Independent Variables Figures 1. Estimated Coefficients on FCI for GaR and Median Growth—AEs and EMEs 2. Coefficient Estimates

Gianni De Nicolò, Marcella Lucchetta, and Mr. Stijn Claessens

) of the form (5) and (6), with estimates of the static factors F ^ t as conditioning variables. Denote with τ ∊ (0,1) a particular quantile, and with a —hat” estimated quantile coefficients. Quantile estimates of (5) and (6) for each τ ∈ {1,2,…..,99}are: GDPGQ t ( τ ) = α ^ 1 ( τ ) + Λ ^ R ′ ( τ ) F ^ t + γ ^ R ( τ ) ( L ) GDPG t − 1 ( 7

International Monetary Fund. Asia and Pacific Dept

Quantile Estimates (standardized) Standard Error 95% Confidence Limits P-Value (intercept) 0.1 -1.07 0.26 -1.33 -0.81 0.00 Price 0.1 -0.74 0.25 -0.98 -0.49 0.00 Property 0.1 0.80 0.36 0.44 1.16 0.00 Equity 0.1 -0.58 0.37 -0.95 -0.21 0.01 Leverage 0.1 -0.20 0.43 -0.63 0.23 0.44 REER 0.1 0.20 0.34 -0.14 0.53 0.33 Real GDP CHN (yoy growth) 0.1 0.06 0.30 -0.24 0.36 0.73 (intercept) 0.25 -0.56 0.17 -0.73 -0.39 0

Andreas Jobst

endpoint of the simulated loss distribution is rescaled to zero (in order to avoid negative quantile estimates), before the mean is calibrated to the annual expected loss in each simulation year over the five-year sample period (2000-04) covered by the LDCE sample statistics. 3 The Jarque-Bera (JB) test diagnostic indicates whether the null hypothesis of normally distributed residuals can be rejected. 4 AMA quantitative criteria for percentile level of reported unexpected operational risk losses ( Basel Committee, 2004 , 2005 , and 2006b ). Table 5

Emmanouil Kitsios and Manasa Patnam
We estimate the average fiscal multiplier, allowing multipliers to be heterogeneous across countries or over time and correlated with the size of government spending. We demonstrate that this form of nonseparable unobserved heterogeneity is empirically relevant and address it by estimating a correlated random coefficient model. Using a panel dataset of 127 countries over the period 1994-2011, we show that not accounting for omitted heterogeneity produces a significant downward bias in conventional multiplier estimates. We rely on both crosssectional and time-series variation in spending shocks, exploiting the differential effects of oil price shocks on fuel subsidies, to identify the average government spending multiplier. Our estimates of the average multiplier range between 1.4 and 1.6.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 IV. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 V. Robustness checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 A. Inference and outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 B. Instrument validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 VI. Quantile estimates of the fiscal multiplier . . . . . . . . . . . . . . . . . . . . . . 26 VII. Conclusions