The Macroeconomic Effects of European Financial Development: A Heterogenous Panel Analysis

This paper investigates the macroeconomic benefits of international financial integration and domestic financial sector development for the European Union. The sample consists of 26 European countries with annual data during the period 1970.2004. We attempt to exploit more fully the temporal dimension in the data by making use of the common correlated effects (CCE) estimator. We also account for the nonstationarity of time series by employing the cross-section augmented panel unit root test of Pesaran (2007) and recently developed panel cointegration techniques. We check the robustness of these results by using the fully modified OLS method of Pedroni (2000). Our empirical results suggest a relationship between domestic financial sector development and labour productivity. We report evidence that real GDP per worker is positively linked to a measure of international financial integration (stock of international financial assets and liabilities expressed as a ratio to GDP). We also try to disentangle the effects on real GDP per worker of di¤erent types of capital flows (FDI, Portfolio equity, Debt) and are able to identify a significant positive effect on GDP per worker of debt inflows which we could attribute to the institutional environment that has been fostered by the European Union.

The U.S. Oil Supply Revolution and the Global Economy

This paper investigates the global macroeconomic consequences of falling oil prices due to the oil revolution in the United States, using a Global VAR model estimated for 38 countries/regions over the period 1979Q2 to 2011Q2. Set-identification of the U.S. oil supply shock is achieved through imposing dynamic sign restrictions on the impulse responses of the model. The results show that there are considerable heterogeneities in the responses of different countries to a U.S. supply-driven oil price shock, with real GDP increasing in both advanced and emerging market oil-importing economies, output declining in commodity exporters, inflation falling in most countries, and equity prices rising worldwide. Overall, our results suggest that following the U.S. oil revolution, with oil prices falling by 51 percent in the first year, global growth increases by 0.16 to 0.37 percentage points. This is mainly due to an increase in spending by oil importing countries, which exceeds the decline in expenditure by oil exporters

The Macroeconomic Effects of European Financial Development: A Heterogenous Panel Analysis

This paper investigates the macroeconomic benefits of international financial integration and domestic financial sector development for the European Union. The sample consists of 26 European countries with annual data during the period 1970.2004. We attempt to exploit more fully the temporal dimension in the data by making use of the common correlated effects (CCE) estimator. We also account for the nonstationarity of time series by employing the cross-section augmented panel unit root test of Pesaran (2007) and recently developed panel cointegration techniques. We check the robustness of these results by using the fully modified OLS method of Pedroni (2000). Our empirical results suggest a relationship between domestic financial sector development and labour productivity. We report evidence that real GDP per worker is positively linked to a measure of international financial integration (stock of international financial assets and liabilities expressed as a ratio to GDP). We also try to disentangle the effects on real GDP per worker of di\ua4erent types of capital flows (FDI, Portfolio equity, Debt) and are able to identify a significant positive effect on GDP per worker of debt inflows which we could attribute to the institutional environment that has been fostered by the European Union

Global VAR (GVAR) Database, 1979Q2-2016Q4

This is the latest version of the Global VAR (GVAR) dataset. The GVAR is a global modelling framework for analyzing the international macroeconomic transmission of shocks, taking into account drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq), as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), over the 1979Q2 to 2016Q4 period. These 33 countries cover more than 90% of world GDP. You can download the data, as well as a description of the compilation, revision and updating of the GVAR Database, from here: http://www.econ.cam.ac.uk/people-files/faculty/km418/research.html#gvar It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2018). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2016Q4. University of Cambridge: Faculty of Economics (mimeo)”

Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2019Q4

This is the latest version of the Global VAR (GVAR) dataset. The GVAR is a global modelling framework for analyzing the international macroeconomic transmission of shocks, taking into account drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq), as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), over the 1979Q2 to 2019Q4 period. These 33 countries cover more than 90% of world GDP. You can download the data, as well as a description of the compilation, revision and updating of the GVAR Database, from here: https://www.mohaddes.org/gvar $$\$$ It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2020). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2019Q4. University of Cambridge: Judge Business School (mimeo)”

Global VAR (GVAR) Database, 1979Q2-2019Q4

This is the latest version of the Global VAR (GVAR) dataset. The GVAR is a global modelling framework for analyzing the international macroeconomic transmission of shocks, taking into account drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq), as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), over the 1979Q2 to 2019Q4 period. These 33 countries cover more than 90% of world GDP. $$\$$ It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2020). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2019Q4. University of Cambridge: Judge Business School (mimeo)”

Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2016Q4

This is the latest version of the Global VAR (GVAR) dataset. The GVAR is a global modelling framework for analyzing the international macroeconomic transmission of shocks, taking into account drivers of economic activity, interlinkages and spillovers between different countries, and the effects of unobserved or observed common factors. This dataset includes quarterly macroeconomic variables for 33 economies (log real GDP, y, the rate of inflation, dp, short-term interest rate, r, long-term interest rate, lr, the log deflated exchange rate, ep, and log real equity prices, eq), as well as quarterly data on commodity prices (oil prices, poil, agricultural raw material, pmat, and metals prices, pmetal), over the 1979Q2 to 2016Q4 period. These 33 countries cover more than 90% of world GDP. It would be appreciated if use of the updated dataset could be acknowledged as: “Mohaddes, K. and M. Raissi (2018). Compilation, Revision and Updating of the Global VAR (GVAR) Database, 1979Q2-2016Q4. University of Cambridge: Faculty of Economics (mimeo)”

DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection

Automatic crash bucketing is a crucial phase in the software development process for efficiently triaging bug reports. It generally consists in grouping similar reports through clustering techniques. However, with real-time streaming bug collection, systems are needed to quickly answer the question: What are the most similar bugs to a new one?, that is, efficiently find near-duplicates. It is thus natural to consider nearest neighbors search to tackle this problem and especially the well-known locality-sensitive hashing (LSH) to deal with large datasets due to its sublinear performance and theoretical guarantees on the similarity search accuracy. Surprisingly, LSH has not been considered in the crash bucketing literature. It is indeed not trivial to derive hash functions that satisfy the so-called locality-sensitive property for the most advanced crash bucketing metrics. Consequently, we study in this paper how to leverage LSH for this task. To be able to consider the most relevant metrics used in the literature, we introduce DeepLSH, a Siamese DNN architecture with an original loss function, that perfectly approximates the locality-sensitivity property even for Jaccard and Cosine metrics for which exact LSH solutions exist. We support this claim with a series of experiments on an original dataset, which we make available

China's Slowdown and Global Financial Market Volatility: Is World Growth Losing Out?

China's GDP growth slowdown and a surge in global financial market volatility could both adversely affect an already weak global economic recovery. To quantify the global macroeconomic consequences of these shocks, we employ a GVAR model estimated for 26 countries/regions over the period 1981Q1 to 2013Q1. Our results indicate that (i) a one percent permanent negative GDP shock in China (equivalent to a one-off one percent growth shock) could have significant global macroeconomic repercussions, with world growth reducing by 0:23 percentage points in the short-run; and (ii) a surge in global financial market volatility could translate into a fall in world economic growth of around 0:29 percentage points, but it could also have negative short-run impacts on global equity markets, oil prices and long-term interest rates