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Keywords:energy prices OR Energy Prices 

Speech
The 2015 economic outlook and the implications for monetary policy

Remarks at Bernard M. Baruch College, New York City.
Speech , Paper 153

Working Paper
Asymmetric Responses of Consumer Spending to Energy Prices: A Threshold VAR Approach

We document asymmetric responses of consumer spending to energy price shocks: Using a multiple-regime threshold vector autoregressive model estimated with Bayesian methods on US data, we find that positive energy price shocks have a larger negative effect on consumption compared with the increase in consumption in response to negative energy price shocks. For large shocks, the cumulative consumption responses are three to five times larger for positive than for negative shocks. Digging into disaggregated spending, we find that the estimated asymmetric responses are strongest for durable ...
Working Papers , Paper 20-17

Journal Article
Energy Investment Variability Within the Macroeconomy

Over the past 10 years, the U.S. energy sector has exerted substantial influence?both positive and negative?on overall U.S. business fixed investment. From 2010 to 2014, a time when energy production in the United States was expanding, investment in the energy sector was a boon to aggregate investment. However, following the sharp oil price decline in 2014, the energy sector was a drag on aggregate investment. {{p}} Assessing the energy sector?s contribution to aggregate investment requires an understanding of both the size of the sector as well as its individual segments. David Rodziewicz ...
Economic Review , Issue Q III , Pages 53-75

Working Paper
Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach

This paper focuses on forecasting quarterly energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information in this large database, we apply a dynamic factor model based on a penalized maximum likelihood approach that allows us to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show ...
Globalization Institute Working Papers , Paper 376

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