Introduction: An overview of the GVAR approach and the handbook

Mauro, Filippo di; Pesaran, M. Hashem

doi:10.1093/acprof:oso/9780199670086.003.0001

Cited by 7 publications

(9 citation statements)

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“…Supporting information section includes details and information on the dataset. The interested reader can refer to [ 24 , 25 ], or [ 26 ] recent book on GVAR modelling, and [ 13 ] for a review of this research field of analysis.…”

Section: A Gvar-climate Modelmentioning

confidence: 99%

Impacts of El Niño-Southern Oscillation on the wheat market: A global dynamic analysis

Gutierrez

2017

PLoS ONE

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Although the widespread influence of the El Niño-Southern Oscillation (ENSO) occurrences on crop yields of the main agricultural commodities is well known, the global socio-economic consequences of ENSO still remain uncertain. Given the global importance of wheat for global consumption by providing 20% of global calories and nourishment, the monitoring and prediction of ENSO-induced variations in the worldwide wheat market are essential for allowing national governments to manage the associated risks and to ensure the supplies of wheat for consumers, including the underprivileged. To this end, we propose a global dynamic model for the analysis of ENSO impacts on wheat yield anomalies, export prices, exports and stock-to-use ratios. Our framework focuses on seven countries/regions: the six main wheat-exporting countries—the United States, Argentina, Australia, Canada, the EU, and the group of the main Black Sea export countries, i.e. Russia, Ukraine, and Kazakhstan—plus the rest of the world. The study shows that La Niña exerts, on average, a stronger and negative impact on wheat yield anomalies, exports and stock-to-use ratios than El Niño. In contrast, wheat export prices are positively related to La Niña occurrences evidencing, once again, its steady impact in both the short and long run. Our findings emphasize the importance of the two ENSO extreme phases for the worldwide wheat market.

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Section: A Gvar-climate Modelmentioning

confidence: 99%

Impacts of El Niño-Southern Oscillation on the wheat market: A global dynamic analysis

Gutierrez

2017

PLoS ONE

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“…We thus have tried di¤erent sample periods such as staring the sample in 1980Q1 and 1995Q1, or ending the sample in 1999Q4. 6 In the U.S., and particularly in Japan, recent monetary policy has been characterized by quantitative easing, which is to allow reserves held by depository institutions far above the required level, while keeping the policy rate close to zero. In principal, at interest rates near zero, the relationship between monetary policy and other variables such as the exchange rate become non-linear.…”

Section: -2013mentioning

confidence: 99%

“…Here we adopt the GVAR model of di Mauro and Pesaran (2013) to examine how shocks to Japanese monetary policies are transmitted, not only to the U.S., but also to other Japanese trading partners. The GVAR model is essentially a multicountry vector autoregression model with enough exogeneity assumptions to make the model estimable and to conduct impulse responses.…”

Section: Multicounty Vector Autoregressionsmentioning

confidence: 99%

Japanese monetary policy and international spillovers

Dekle¹,

Hamada

2015

Journal of International Money and Finance

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The Japanese currency has recently weakened past 100 yen to the dollar. The reason for the recent depreciation of the yen is the expectation of higher in ‡ation in Japan, owing to the rapid projected growth in Japanese base money, the sum of currency and commercial banking reserves at the Bank of Japan.We show through our empirical analysis that recent expansionary Japanese monetary policies have generally helped raise U.S. GDP, despite the appreciation of the dollar.

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“…A smaller model could be simpler to specify and to update. One of the main findings of the GVAR studies highlighted in Di Mauro and Pesaran (2013) is that for small, open economies one could model only a few countries explicitly in the GVAR to get reliable forecasts. Assenmacher (2013) models only three trading partners together with Switzerland in a GVAR to forecast the Swiss economy.…”

Section: Introductionmentioning

confidence: 99%

“…We also make two contributions to the international literature. First, we compare the forecasting power of the standard 33-country GVAR and a customised small GVAR for South Africa to determine whether the key finding in Di Mauro and Pesaran (2013), as discussed in the previous paragraph, holds for a developing 3 country like South Africa. Second, we use the time-varying trade weighted approach, rather than the fixed trade-weighted approach used in previous studies of GVAR forecasting, to account for the large changes in the trade weights of South Africa"s trading partners over time.…”

Section: Introductionmentioning

confidence: 99%

Do we need a global VAR model to forecast inflation and output in South Africa?

2015

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This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The paper considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-ofsample forecasts for South African GDP and inflation from six types of models: a general 33-country (large) GVAR, a customised small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customised small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1-2013:Q4, to highlight their ability to track turning points in output and inflation respectively. JEL Classification Codes: C51, C53

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Introduction: An overview of the GVAR approach and the handbook

Cited by 7 publications

References 0 publications

Impacts of El Niño-Southern Oscillation on the wheat market: A global dynamic analysis

Impacts of El Niño-Southern Oscillation on the wheat market: A global dynamic analysis

Japanese monetary policy and international spillovers

Do we need a global VAR model to forecast inflation and output in South Africa?

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