時(shí)間序列分析及var模型-文庫吧
2025-06-11 18:30 本頁面
【正文】 highperforming panies are more likely to enter foreign markets due to their ownership specific advantages. This argument is based on the Resourcebased View and the OLS framework developed by Dunning and Rugman (Reading School of International Business). The VAR model allows you to incorporate both effects: in fact you can test whether performance drives internationalisation or internationalisation drives performance.Before you start using a VAR model, you have to make sure that the time series are stationary. So the first step is to check whether the time series is stationary using DickeyFuller tests and KPSS tests. The second step is to specify the optimal lag length (p) of the model. This is done by paring different model specifications using information criteria. Apart from using Akaike (AIC) and Bayesian Schwarz (BIC), the HannanQuinn (HQIC) is monly used. Most applied econometricians favour the HannanQuinn (HQIC) criterion. STATA will help you to make a good choice. After specifying your model, you need to check stability conditions. The coefficient matrix of the reduced form VAR has to ensure that the iteration sequence converges to a longterm value. STATA will help you in checking stability.To be precise, you need to show that the eigenvalues of the coefficient matrix lie within the unit circle. The reason behind it can be only understood when you understand the method of diagonalizing a matrix. VAR models offer another nice feature: impulse response functions. VAR models capture the dynamics of two (or more) stationary time series。 hence, we can assess the dynamic impact of a marginal change of one variable on another. The standard OLS regression provides coefficients, and coefficients refer to the partial impact of an explanatory variable on the dependent variable. In the case of VAR models, the relationship bees dynamic, as a change of one variable (say x) in t can affect x and y in t+1. The impact on x and y in t+1 in turn affects x and y in t+2 and so on until the impact dies out. Impulse response functions are very useful in illustrating the shortterm dynamics in a model.Let’s look at an example to see how VAR modelling works. In Lecture 5, we tried very hard to understand gold prices. We extend our univariate model by exploring the relationships between gold and silver prices. Linking two (similar) assets or securities is a very mon trading strategy, which is called pairstrading.Before we do any sophisticated modelling, it is always beneficial to look at some line charts. Figure 1 shows the indexed time series of nominal gold and silver prices from 1900 to 2010.Figure 1: Nominal gold and silver prices, indexed, 19002010We can see that there is a certain degree of comovement, which we might be able to exploit for our trading strategy. Before we can use VAR, we need to ensure that both time series are stationary. It is obvious from Figure 1 that gold and silver prices are not stationary. However, after taking a firstdifference we can show that price changes are stationary. So both ti
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