Simultaneous Equations Models and Distributed Lag Models

Subject. The article addresses projections of regional budget revenues, using distributed lag models. Objectives. The purpose is to review economic and statistical tools that are suitable for the analysis of relationship between the revenues of the regional budget system and regional macroeconomic predictors. Methods. The study draws on statistical, constructive, economic and mathematical methods of analysis. Results. In models with quantitative variables obtained under the Almon method, the significant predictors in the forecasting of regional budget revenues are determined mainly by the balanced financial result, the consumer price index, which characterizes inflation processes in the region, and the unemployment rate being the key indicator of the labor market. Models with quantitative variables obtained through the Koyck transformation are characterized by a wider range of predictors, the composition of which is determined by the peculiarities of economic situation in regions. The two-year forecast provides the average lag obtained during the evaluation of the models. The exception is the impact of unemployment rate, which is characterized as long-term. Conclusions. To generate forecasts of budget parameters, the results of both the Koyck method and the Almon method should be considered, though the former is more promising.

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Analysis and Forecasts of the Demand for Imported Wine in China

Cornell Hospitality Quarterly ◽

10.1177/1938965520981070 ◽

2021 ◽

pp. 193896552098107

Author(s):

Anyu Liu ◽

Haiyan Song

Keyword(s):

Single Equation ◽

Specific Model ◽

Specific Method ◽

Sparkling Wine ◽

Econometric Methods ◽

Distributed Lag ◽

Distributed Lag Models ◽

Wine Market ◽

Wine Demand

The aim of this study is to investigate the long-term determinants of China’s imported wine demand and to forecast wine imports from 2019 to 2023 using econometric methods. Auto-regressive distributed lag models are developed based on neoclassical economic demand theory to investigate the long-term determinants of China’s demand for imported bottled, bulk, and sparkling wine from the top five countries of origin. The empirical results demonstrate that income is the most important determinant of China’s imported wine demand, and that price only plays a significant role in a few markets. Substitute and complement effects are identified between wines from different countries of origin and between imported wines and other liquids. China’s imported wine demand is expected to maintain its rapid growth over the forecast period. Bottled wine will continue to dominate China’s imported wine market. France will have the largest market share in the bottled wine market, Spain will be the largest provider of bulk wine, and Italy will hold the same position for sparkling wine. This is the first study to use a single equation with the general to specific method rather than a system of equations to estimate and forecast China’s demand for imported bottled, bulk, and sparkling wines from different countries of origin. The more specific model setting for each country of origin improves forecasting accuracy.

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A New Computational Method for Estimating Simultaneous Equations Models Using Entropy as a Parameter Criteria

Mathematics ◽

10.3390/math9070700 ◽

2021 ◽

Vol 9 (7) ◽

pp. 700

Author(s):

Belén Pérez-Sánchez ◽

Martín González ◽

Carmen Perea ◽

Jose J. López-Espín

Keyword(s):

Experimental Study ◽

Computational Study ◽

Estimation Method ◽

Information Criteria ◽

Simultaneous Equations ◽

Estimation Methods ◽

Entropy Measure ◽

Economic Science ◽

Akaike Information Criteria ◽

Simultaneous Equations Models

Simultaneous Equations Models (SEM) is a statistical technique widely used in economic science to model the simultaneity relationship between variables. In the past years, this technique has also been used in other fields such as psychology or medicine. Thus, the development of new estimating methods is an important line of research. In fact, if we want to apply the SEM to medical problems with the main goal being to obtain the best approximation between the parameters of model and their estimations. This paper shows a computational study between different methods for estimating simultaneous equations models as well as a new method which allows the estimation of those parameters based on the optimization of the Bayesian Method of Moments and minimizing the Akaike Information Criteria. In addition, an entropy measure has been calculated as a parameter criteria to compare the estimation methods studied. The comparison between those methods is performed through an experimental study using randomly generated models. The experimental study compares the estimations obtained by the different methods as well as the efficiency when comparing solutions by Akaike Information Criteria and Entropy Measure. The study shows that the proposed estimation method offered better approximations and the entropy measured results more efficiently than the rest.

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