2022
DOI: 10.3390/agronomy12112680
|View full text |Cite
|
Sign up to set email alerts
|

The Dynamic Impacts of Weather Changes on Vegetable Price Fluctuations in Shandong Province, China: An Analysis Based on VAR and TVP-VAR Models

Abstract: In order to enrich the research on the influence of weather factors on agricultural economy and provide practical decision-making references for the relevant market entities, this study took pointed pepper, loofah, Chinese chives and tomato as examples, using weekly wholesale prices and corresponding weather factors data from one of the main production areas in China based on the vector autoregressive (VAR) and the time-varying parameter vector autoregressive (TVP-VAR) models to explore the dynamic impacts of … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 11 publications
(4 citation statements)
references
References 50 publications
0
1
0
Order By: Relevance
“…Weather shocks disrupt the supply‐demand balance of vegetables and intensify vegetable price fluctuations. The main reason is that commercial vegetable production is spatially concentrated, while the consumer market is dispersed over the country making the vegetable supply chain susceptible to weather shocks (Ji et al, 2016; Li et al, 2015; Yang et al, 2022). When vegetable prices strongly fluctuate, households cultivating homestead gardening can shift from buying vegetables to consuming more homestead vegetables while others have to adjust their food consumption patterns.…”
Section: Methodsmentioning
confidence: 99%
“…Weather shocks disrupt the supply‐demand balance of vegetables and intensify vegetable price fluctuations. The main reason is that commercial vegetable production is spatially concentrated, while the consumer market is dispersed over the country making the vegetable supply chain susceptible to weather shocks (Ji et al, 2016; Li et al, 2015; Yang et al, 2022). When vegetable prices strongly fluctuate, households cultivating homestead gardening can shift from buying vegetables to consuming more homestead vegetables while others have to adjust their food consumption patterns.…”
Section: Methodsmentioning
confidence: 99%
“…The historical selling price and wholesale price of vegetables are a set of regular time-series data [13]. The RNN recurrent neural network has strong model fitting ability as well as good prediction performance for time series data [2].…”
Section: Future Pricing Prediction For Vegetable Category Based On Rn...mentioning
confidence: 99%
“…If the p-value is less than the specific significance level, say, 0.05, then the model being tested is preferred over the saturated model. In this way, the optimal order of VAR Model is determined by choosing the model with the lowest p-value [8]. In Table 2, it is evident that lag 7 has the lowest p-value 0.015, which is smaller than 0.05, indicating that the best model according to the LR criterion would be the VAR model with 7 lags.…”
Section: Order Of Var Modelmentioning
confidence: 99%