Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

McKenzie, Andrew M.; Goodwin, Harold L.; Carreira, Rita I.

doi:10.1017/s1074070800002650

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…According to the sequence of occurrence time, the quantitative prediction methods can be divided into econometric method, time series analysis method, and intelligent prediction method. The econometric method finds the supporting economic theory according to the research problem, then puts forward the hypothesis, and establishes the econometric model to verify the hypothesis [4][5][6][7][8][9]. However, most empirical studies do not prove that the prediction effect of the classic econometric model is better than that of the time series analysis method [10][11][12].…”

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confidence: 99%

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

Zhang

2018

Mathematical Problems in Engineering

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Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up ofLow,R, andUp, which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameterscandgfor SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.

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Section: Introductionmentioning

confidence: 99%

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

Zhang

2018

Mathematical Problems in Engineering

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What Drives the Change in Employment in the US Logging Industry? A Directed Acyclic Graph Approach

He¹,

Li²,

Smidt³

et al. 2022

Forest Products Journal

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Employment in the US logging industry has been declining over the past few decades and fell to a 20-year low following the 2008 economic recession. This study investigates the drivers of employment in the US logging industry from 2007 to 2017, using a directed acyclic graph (DAG). This approach is applied for the first time to disclose the contemporaneous causal relations among employment, wages, mechanization, production level, and product prices in the logging industry. Forecast error variance decomposition (VD) is further used to examine the long-run dynamic relationships between these variables. The results show that the product price directly affects employment and indirectly promotes employment through wages. The results of VD show that mechanization has an increasing long-term effect on employment.

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External Debt and Economic Growth in Niger: a Vector Autoregression and Variance Decomposition Analysis

Oumarou¹

2021

Economics and Business

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In the quest for quick economic development, many Sub Saharan African (SSA) countries borrow money to finance their budget deficits and vital infrastructure. Niger has seen its external debt increase year after year without really reaching economic development. This study uses a vector autoregressive (VAR) model to investigate the relation linking external debt and economic growth in Niger and variance decomposition forecast to verify if there is any significant impact from shocks for a period of 5 years in the future. The study utilises time series yearly data provided by the World Bank for the period covering 1970–2019. The empirical results reveal no long-run relationship between economic growth, external debt and government spending in Niger. The results also indicated that, on average ceteris paribus, the past realisation of economic growth is related to an increase of 97.75 % in economic growth, while the past realisation of external debt and government spending is associated with an increase of 83.77 % and 79.70 % in external debt and government spending, respectively. The results furthermore show that economic growth has a statistically significant causal effect on government spending in the short term. One percentage increase in economic growth accounts for an increase of 35.28 % in government spending on average ceteris paribus. The variance decomposition forecast reveals that economic growth has a significant influence on predicting government spending in the future.

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Alternative Model Selection Using Forecast Error Variance Decompositions in Wholesale Chicken Markets

Cited by 4 publications

References 11 publications

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

What Drives the Change in Employment in the US Logging Industry? A Directed Acyclic Graph Approach

External Debt and Economic Growth in Niger: a Vector Autoregression and Variance Decomposition Analysis

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