墨西哥:实际汇率的决定因素,2001.01-2022.12
Eduardo Loría1, Lorenzo Nalin1
1School of Economics, National Autonomous University of Mexico (UNAM), Mexico City, Mexico.
PloS one
|December 6, 2023
概括
墨西哥的实际汇率受到贸易和金融因素的影响,在2009年之后出现了共同整合的证据. 巴拉萨-萨尔森效应和交易条款是关键驱动因素,与金融变量和长期折旧趋势一起.
科学领域:
- 经济学 经济学 经济学
- 计量经济学 计量经济学
- 国际金融 国际金融
背景情况:
- 墨西哥实际汇率 (q) 受各种经济和金融决定因素的影响.
- 了解这些驱动因素对于经济政策和金融市场分析至关重要.
- 在2008-2009年金融危机之后的时期,国际金融化增加了.
研究的目的:
- 估计墨西哥双边实际汇率的短期和长期决定因素.
- 通过商业和金融变量,调查墨西哥实际汇率是否存在共同整合.
- 分析巴拉萨-萨尔森效应,交易条款和金融变量对实际汇率的影响.
主要方法:
- 自主回归分布式滞后 (ARDL) 模型 (Pesaran和Shin等人,2001) 适用于2001年01月至2022年12月墨西哥数据.
- 对整个样本和子期 (2001.01-2008.12和 2009.01-2022.12年) 的共同整合关系的分析.
- 使用双日志模型来量化已识别的决定因素的影响.
主要成果:
- 对于整个样本或2009年之前的时期,没有发现同整合关系,但对2009年01月至2022年12月出现了证据.
- 实际汇率的强烈短期自动回归效应 (高达4次滞后) 被观察到.
- 巴拉萨-萨尔森效应显示出最重要的影响,其次是贸易条款. 金融变量 (期货汇率,风险溢价) 也产生了显著的影响.
- 实际折旧的长期趋势 (0.0020) 表明购买力平价 (PPP) 假设随着时间的推移而持有.
结论:
- 调查结果强调了墨西哥实际汇率决定的不断变化的性质,特别是2009年后金融化的影响.
- 巴拉萨-萨尔森效应和贸易条款是关键的长期驱动因素,而金融变量则起着重要作用.
- 这项研究为墨西哥实际汇率的长期PPP假设提供了实证支持.
相关概念视频
Microsoft Excel: Regression Analysis
621
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
To perform regression...
621
Econometric Views (EViews)
145
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
145
Interpreting X̄ Charts
67
Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
67
Central Tendency: Analysis
156
Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
156
Microsoft Excel: Pearson's Correlation
503
Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
503
Variation
6.8K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
6.8K


