1,368 research outputs found
The Intellectual Impact of Agricultural Economists
agricultural economists, Agribusiness, Agricultural Finance, Labor and Human Capital, Production Economics,
Mixed Unit Roots and Deterministic Trends in Noncausality Tests
Using Japanese economic data and a Monte Carlo simulation, this study analyzes the consequences of ignoring deterministic trends in mixed unit-root data for Granger noncausality tests. Results from an augmented VAR suggest over-rejection in certain empirically relevant cases at various sample sizes.Research Methods/ Statistical Methods,
A DYNAMIC RESPONSE ANALYSIS FOR THE U.S. ROUGH RICE MARKET
This study provides a comparative evaluation of VARs versus structural VARs for policy analysis and simulation via impulse response analysis (IRA). The IRA is valuable information for rice market participants as these results provide an economically intuitive explanation of adjustments that occur as a result of shocks to the market.Marketing,
A Semiparametric Approach to Estimate Engel curves using the US Micro Data
The study estimates Engel curves using cross-section data from the 2003 US consumer expenditure survey (CES). We focus on finding adequate specification for modeling the demographic characteristics using parametric, nonparametric, and semiparametric techniques. The empirical results indicate parametric Working-Leser or Piglog specification was sufficient for most budget shares except for transportation where semiparametric specification had support.Consumer/Household Economics,
MONTE CARLO EVIDENCE ON COINTEGRATION AND CAUSATION
The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples.Causality tests, Cointegration, Likelihood ratio, Wald statistic, Monte Carlo Experiments, Research Methods/ Statistical Methods,
Developed Speculation and Under Developed Markets - The Role of Futures Trading on Export Prices in Less Developed Countries
This paper examines the relationship between New York coffee futures and cash export prices in Guatemala and Honduras . Cointegration tests suggest that the futures market is serving its price discovery function, and provides a vehicle by which to manage the domestic price risk in export countries. However, further analysis finds that as the percent of speculative open interest increases in the coffee futures market, price volatility increases. This suggests that cash market price risk in exporting countries may actually increase as a result of futures trading activity in developed country futures exchanges.
String amplitudes in the Hpp-wave limit of AdS3xS3
We compute string amplitudes on pp-waves supported by NS-NS 3-form fluxes and
arising in the Penrose limit of AdS3xS3xM. We clarify the role of the
non-chiral accidental SU(2) symmetry of the background. We comment on the
extension of our results to the superstring and propose a holographic formula
in the BMN limit of the AdS3/CFT2 correspondence valid for any correlator.Comment: Latex,no figures, 47 p
BOOTSTRAPPING IN VECTOR AUTOREGRESSIONS: AN APPLICATION TO THE PORK SECTOR
Standard bootstrap method is used to generate confidence intervals (CIs) of impulse response functions of VAR and SVAR models in the pork sector. In the VAR model, the bootstrap method does not produce significant different results from Monte Carlo simulations. In the SVAR analysis, on the other hand, the bootstrap CIs are significantly different from Monte Carlo CIs after a six period forecast intervals. This suggests that the choice of method used to measure reliability of IRFs is not trivial. Furthermore, bootstrap CIs in SVAR model seem to be more stable than MC CIs, which tend to be wider in the longer horizons.Research Methods/ Statistical Methods,
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