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| 1 |
ID:
106024
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| Publication |
2011.
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| Summary/Abstract |
This article devises a method to separate the Global Terrorism Database (GTD) into transnational and domestic terrorist incidents. This decomposition is essential for the understanding of some terrorism phenomena when the two types of terrorism are hypothesized to have different impacts. For example, transnational terrorism may have a greater adverse effect than domestic terrorism on economic growth. Moreover, the causes of the two types of terrorism may differ. Once the data are separated, we apply a calibration method to address some issues with GTD data - namely, the missing data for 1993 and different coding procedures used before 1998. In particular, we calibrate the GTD transnational terrorist incidents to ITERATE transnational terrorist incidents to address GTD's undercounting of incidents in much of the 1970s and its overcounting of incidents in much of the 1990s. Given our assumption that analogous errors characterize domestic terrorist events in GTD, we apply the same calibrations to adjust GTD domestic incidents. The second part of the article investigates the dynamic aspects of GTD domestic and transnational terrorist incidents, based on the calibrated data. Contemporaneous and lagged cross-correlations for the two types of terrorist incidents are computed for component time series involving casualties, deaths, assassinations, bombings, and armed attacks. We find a large cross-correlation between domestic and transnational terrorist incidents that persists over a number of periods. A key finding is that shocks to domestic terrorism result in persistent effects on transnational terrorism; however, the reverse is not true. This finding suggests that domestic terrorism can spill over to transnational terrorism, so that prime-target countries cannot ignore domestic terrorism abroad and may need to assist in curbing this homegrown terrorism.
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| 2 |
ID:
136728
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| Publication |
New Delhi, Defence Research and Development Organisation, 2014.
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| Description |
204p.Pbk
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| Series |
DRDO Monograph
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| Standard Number |
9788186514535
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Copies: C:1/I:0,R:0,Q:0
Circulation
| Accession# | Call# | Current Location | Status | Policy | Location |
| 058106 | 623.7464/SIN 058106 | Main | On Shelf | General | |
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| 3 |
ID:
166450
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| Summary/Abstract |
Distributed energy resources, such as rooftop solar photovoltaics (PV), are likely to comprise a substantial fraction of new generation capacity in the United States. However, forecasting technology adoption based on people's willingness to pay (WTP) faces two major challenges: the stated-intention and omitted-variable biases. Previous solar adoption literature has neglected to address these two biases altogether. Here, we adopt a “parameterization + calibration” approach to address both biases and estimate customers’ WTP for PV. After collecting survey data on respondents’ WTP for adopting PV, we characterize its empirical cumulative density function using a gamma distribution. We further calibrate the gamma distribution parameters using a national distributed PV adoption simulation model, finding the parameters that produce the best fit between simulated and historic solar adoption. We then show that the calibrated gamma distribution improves the raw WTP data after correcting for the two biases. Finally, we use our optimally-calibrated WTP to forecast market demand for residential PV at the county-level of the United States in 2020. Improving estimates of customer willingness to pay has significant implications for policy directly, e.g. estimating the effect of a proposed policy on technology adoption, and other regulatory processes that use forecasting, e.g. integrated resource planning.
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