Multivariate functional-coefficient regression models for nonlinear vector time series data

Biometrika ◽  
2014 ◽  
Vol 101 (3) ◽  
pp. 689-702 ◽  
Author(s):  
J. Jiang
2019 ◽  
Vol 36 (3) ◽  
pp. 443-472 ◽  
Author(s):  
Chunyan Li ◽  
Wei Huang ◽  
Brian Milan

AbstractAtmospheric cold fronts provide recurring forcing for circulations and long-term transport in estuaries with microtides. Multiple horizontal ADCPs were used to obtain time series data from three inlets in Barataria Bay. The data cover a period of 51 atmospheric cold fronts between 2013 and 2015. The weather and subtidal ocean response are highly correlated in the “weather band” (3–7 days). The cold front–associated winds produce alternating flows into, out of, and then back into the bay, forming an asymmetric “M” for low-pass filtered flows. Results show that cold front–induced flows are the most important component in this region, and the flows can be predicted based on wind vector time series. Numerical simulations using a validated Finite-Volume Coastal Ocean Model (FVCOM) demonstrate that the wind-driven oscillations within the bay are consistent with the quasi-steady state with little influence of the Coriolis effect for cold front–related wind-driven flows. The four major inlets (from the southwest to the northeast) consistently carry 10%, 57%, 21%, and 12% of the tidal exchange of the bay, respectively. The subtidal exchange rates through them however fluctuate greatly with averages of 18% ± 13%, 35% ± 18%, 31% ± 16%, and 16% ± 9%, respectively. Several modes of exchange flows through the multiple inlets are found, consisting of the all-in and all-out mode (45% occurrence) under strong winds perpendicular to the coastline; the shallow-downwind, deep-upwind mode (41%), particularly during wind-relaxation periods; and the upwind-in and downwind-out mode (13%) under northerly or southerly winds. These modes are discussed with the low-pass filtered model results and verified by a forcing–response joint EOF analysis.


Agriekonomika ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 205-214
Author(s):  
Oni Ringgu Lero ◽  
Agnes Quartina Pudjiastuti ◽  
Sumarno Sumarno

Cashews contribute significantly to the Indonesian economy because it is one of the exporting countries. However, volume of exports tends to fluctuate, so it is necessary to identify the influencing factors. This study aims to analyze volume of Indonesian cashew exports and its determinants. Time series data for 8 variables during 1985–2016 were analyzed descriptively by multiple regression models. The results again show fluctuations in export volume and value over 1985–2016 period. Lowest export volume occurred in 1989, but its value was in 1985. Highest export volume and value occurred in 2015. National cashew export volume depends on the domestic cashew price, exchange rate and income per capita. Peanuts and coffee have a complementary relationship with cashews, while sugar has a substitution relationship with this commodity. Cashews are an inferior goods.


2019 ◽  
Vol 14 (1) ◽  
pp. 37-44
Author(s):  
Rizki Kenraraswati ◽  
M. Syurya Hidayat ◽  
Yohanes Vyn Amzar

The study aims to analyze the effect of domestic investment (PMDN), minimum wage (UMP) and capital expenditure (BM) on employment absorption in Jambi Province. The data used is time series data of Jambi Province during the period 2000-2016. Data were analyzed descriptively as well as multiple regression models. The results of the study found that: 1) the average growth of employment is 3.11percent per year, domestic investment is 11.67 percent per year, UMP is 16.44 percent per year and capital expenditure is 20.00 percent per year; 2) Simultaneously PMDN, UMP and BM have a significant effect on employment in Jambi Province. Partially the BM variable does not have a significant effect while the PMDN and UMP variables have a significant effect on employment in Jambi Province.


2019 ◽  
Vol 22 (2) ◽  
pp. 97-116
Author(s):  
Tucker S McElroy ◽  
Agnieszka Jach

Summary We investigate the collinearity of vector time series in the frequency domain, by examining the rank of the spectral density matrix at a given frequency of interest. Rank reduction corresponds to collinearity at the given frequency. When the time series is nonstationary and has been differenced to stationarity, collinearity corresponds to co-integration at a particular frequency. We examine rank through the Schur complements of the spectral density matrix, testing for rank reduction via assessing the positivity of these Schur complements, which are obtained from a nonparametric estimator of the spectral density. New asymptotic results for the test statistics are derived under the fixed bandwidth ratio paradigm; they diverge under the alternative, but under the null hypothesis of collinearity the test statistics converge to a non-standard limiting distribution. Subsampling is used to obtain the limiting null quantiles. A simulation study and an empirical illustration for 6-variate time series data are provided.


2005 ◽  
Vol 57 (3-4) ◽  
pp. 195-208
Author(s):  
Amitava Dey ◽  
V. K. Sharma ◽  
Himadri Ghosh

In regression models using time series data, the errors are generally correlated. The sample residuals contain useful information for predicting post­sample observations. This information, which is generally ignored, has been exploited here in deriving the best linear unbiased predictors in a 2­equation linear regression model. The gain in efficiency of the proposed predictors over the usual generalized least ­ squares predictors has been obtained and the particular case when error terms in the two equations follow AR(l) process has also been disscussed.


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