Empirical Estimation of Natural Geoelectric Hazards

AbstractThe COVID-19 crisis created large economic losses for corn, ethanol, gasoline, and oil producers and refineries both in the United States and worldwide. We extend the theory used by Schmitz, A., C. B. Moss, and T. G. Schmitz. 2007. “Ethanol: No Free Lunch.” Journal of Agricultural & Food Industrial Organization 5 (2): 1–28 as a basis for empirical estimation of the effect of COVID-19. We estimate, within a welfare economic cost-benefit framework that, at a minimum, the producer cost in the United States for these four sectors totals $176.8 billion for 2020. For U.S. oil producers alone, the cost was $151 billion. When world oil is added, the costs are much higher, at $1055.8 billion. The total oil producer cost is $1.03 trillion, which is roughly 40 times the effect on U.S. corn, ethanol, and gasoline producers, and refineries. If the assumed unemployment effects from COVID-19 are taken into account, the total effect, including both producers and unemployed workers, is $212.2 billion, bringing the world total to $1266.9 billion.

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Empirical Estimation of Shortest Route Length along U.S. Interstate Highways Based on Great Circle Distance

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211015251 ◽

2021 ◽

pp. 036119812110152

Author(s):

Nawei Liu ◽

Fei Xie ◽

Zhenhong Lin ◽

Mingzhou Jin

Keyword(s):

Linear Regression ◽

State Level ◽

Distance Matrix ◽

Great Circle ◽

The State ◽

Empirical Estimation ◽

Interstate Highways ◽

Shortest Route ◽

Route Length ◽

Great Circle Distance

In this study, 98 regression models were specified for easily estimating shortest distances based on great circle distances along the U.S. interstate highways nationwide and for each of the continental 48 states. This allows transportation professionals to quickly generate distance, or even distance matrix, without expending significant efforts on complicated shortest path calculations. For simple usage by all professionals, all models are present in the simple linear regression form. Only one explanatory variable, the great circle distance, is considered to calculate the route distance. For each geographic scope (i.e., the national or one of the states), two different models were considered, with and without the intercept. Based on the adjusted R-squared, it was observed that models without intercepts generally have better fitness. All these models generally have good fitness with the linear regression relationship between the great circle distance and route distance. At the state level, significant variations in the slope coefficients between the state-level models were also observed. Furthermore, a preliminary analysis of the effect of highway density on this variation was conducted.

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Accuracy of Households’ Dwelling Valuations, Housing Demand and Mortgage Decisions: Israeli Case

The Journal of Real Estate Finance and Economics ◽

10.1007/s11146-021-09823-7 ◽

2021 ◽

Author(s):

Alla Koblyakova ◽

Larisa Fleishman ◽

Orly Furman

Keyword(s):

Academic Research ◽

House Price ◽

Housing Demand ◽

Empirical Estimation ◽

Cross Sectional ◽

Price Expectations ◽

Housing Services ◽

House Price Expectations ◽

Supply Side Factors

AbstractHousing policy, as well as academic research, are increasingly concerned with the role of bias in subjective dwelling valuations as a proximate measure of households’ house price expectations and their relationship with housing demand. This paper contributes to this area of study by exploring the possibility of simultaneous relationships between households’ price expectations and incentive to maximise the size of housing services demanded also accounting for the supply side factors and regional perspective. The empirical estimation takes the form of a system of a two simultaneous equations model applying two stage least squares estimation technique. Cross sectional estimations utilise data extracted from the Israeli Longitudinal Panel Survey (LPS) data. Applying the best available proxy for households’ price expectations, calculated as the ratio between subjective dwelling valuations (LPS) and the estimated market value of the same properties, research has identified the interrelated factors that simultaneously influence householders’ price expectations and housing demand. Results offer conceptual and empirical advantages, highlighting the imperfect nature of the housing market, as reflected by the inseparability of bias in subjective valuations and housing decisions.

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Pricing spread options by generalized bivariate edgeworth expansion

International Journal of Financial Engineering ◽

10.1142/s2424786317500177 ◽

2017 ◽

Vol 04 (02n03) ◽

pp. 1750017

Author(s):

Edward P. C. Kao ◽

Weiwei Xie

Keyword(s):

Edgeworth Expansion ◽

Price Difference ◽

Empirical Estimation ◽

Strike Price ◽

Closed Form Solutions ◽

Spread Options ◽

Valuation Process ◽

The Difference ◽

Spread Option ◽

Underlying Processes

A spread option is a contingent claim whose underlying is the price difference between two assets. For a call, the holder of the option receives the difference, if positive, between the price difference and the strike price. Otherwise, the holder receives nothing. Spread options trade in large volume in financial, fixed-income, commodity, and energy industries. It is well known that pricing of spread options does not admit closed-form solutions even under a geometric Brownian motion paradigm. When price dynamics experience stochastic volatilities and/or jumps, the valuation process becomes more challenging. Following the seminal work of Jarrow and Judd, we propose the use of Edgeworth expansion to approximate the call price. In the spirit of Pearson, we reduce the cumbersome computation inherent in Edgeworth expansion to single numerical integrations. For an arbitrary bivariate price process, we show that once its product cumulants are available, either by virtue of the structural properties of the underlying processes or by empirical estimation using market data, the approach enables analysts to approximate the call price easily. Specifically, the call prices so estimated capture the correlation, skewness, and kurtosis of the two underlying price processes. As such, the approach is useful for approximate valuations based on Lévy-based models.

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Financial correlations at ultra-high frequency: theoretical models and empirical estimation

The European Physical Journal B ◽

10.1140/epjb/e2011-10865-y ◽

2011 ◽

Vol 80 (2) ◽

pp. 243-253 ◽

Cited By ~ 5

Author(s):

I. Mastromatteo ◽

M. Marsili ◽

P. Zoi

Keyword(s):

High Frequency ◽

Theoretical Models ◽

Empirical Estimation ◽

Ultra High Frequency

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The empirical Gaia G-band extinction coefficient

Astronomy and Astrophysics ◽

10.1051/0004-6361/201732327 ◽

2018 ◽

Vol 614 ◽

pp. A19 ◽

Cited By ~ 24

Author(s):

C. Danielski ◽

C. Babusiaux ◽

L. Ruiz-Dern ◽

P. Sartoretti ◽

F. Arenou

Keyword(s):

Extinction Coefficient ◽

Main Sequence ◽

Spectral Energy Distribution ◽

Empirical Method ◽

Spectral Energy ◽

Red Giants ◽

Main Sequence Stars ◽

Empirical Estimation ◽

Two Samples ◽

Data Release

Context. The first Gaia data release unlocked the access to photometric information for 1.1 billion sources in the G-band. Yet, given the high level of degeneracy between extinction and spectral energy distribution for large passbands such as the Gaia G-band, a correction for the interstellar reddening is needed in order to exploit Gaia data. Aims. The purpose of this manuscript is to provide the empirical estimation of the Gaia G-band extinction coefficient kG for both the red giants and main sequence stars in order to be able to exploit the first data release DR1. Methods. We selected two samples of single stars: one for the red giants and one for the main sequence. Both samples are the result of a cross-match between Gaia DR1 and 2MASS catalogues; they consist of high-quality photometry in the G-, J- and KS-bands. These samples were complemented by temperature and metallicity information retrieved from APOGEE DR13 and LAMOST DR2 surveys, respectively. We implemented a Markov chain Monte Carlo method where we used (G – KS)0 versus Teff and (J – KS)0 versus (G – KS)0, calibration relations to estimate the extinction coefficient kG and we quantify its corresponding confidence interval via bootstrap resampling. We tested our method on samples of red giants and main sequence stars, finding consistent solutions. Results. We present here the determination of the Gaia extinction coefficient through a completely empirical method. Furthermore we provide the scientific community with a formula for measuring the extinction coefficient as a function of stellar effective temperature, the intrinsic colour (G – KS)0, and absorption.

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Is Russia successful in attracting foreign direct investment? Evidence based on gravity model estimation

Review of Economic Perspectives ◽

10.1515/revecp-2016-0015 ◽

2016 ◽

Vol 16 (3) ◽

pp. 245-267 ◽

Cited By ~ 3

Author(s):

Oleg Mariev ◽

Igor Drapkin ◽

Kristina Chukavina

Keyword(s):

Foreign Direct Investment ◽

Gravity Model ◽

Host Country ◽

Direct Investment ◽

Recipient Country ◽

Developed Countries ◽

Likelihood Method ◽

Empirical Estimation ◽

Fdi Inflows ◽

Russian Economy

Abstract The aim of this paper is twofold. First, it is to answer the question of whether Russia is successful in attracting foreign direct investment (FDI). Second, it is to identify partner countries that “overinvest” and “underinvest” in the Russian economy. We do this by calculating potential FDI inflows to Russia and comparing them with actual values. This research is associated with the empirical estimation of factors explaining FDI flows between countries. The methodological foundation used for the research is the gravity model of foreign direct investment. In discussing the pros and cons of different econometric methods of the estimation gravity equation, we conclude that the Poisson pseudo maximum likelihood method with instrumental variables (IV PPML) is one of the best options in our case. Using a database covering about 70% of FDI flows for the period of 2001-2011, we discover the following factors that explain the variance of bilateral FDI flows in the world economy: GDP value of investing country, GDP value of recipient country, distance between countries, remoteness of investor country, remoteness of recipient country, level of institutions development in host country, wage level in host country, membership of two countries in a regional economic union, common official language, common border and colonial relationships between countries in the past. The potential values of FDI inflows are calculated using coefficients of regressors from the econometric model. We discover that the Russian economy performs very well in attracting FDI: the actual FDI inflows exceed potential values by 1.72 times. Large developed countries (France, Germany, UK, Italy) overinvest in the Russian economy, while smaller and less developed countries (Czech Republic, Belarus, Denmark, Ukraine) underinvest in Russia. Countries of Southeast Asia (China, South Korea, Japan) also underinvest in the Russian economy.

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