時間序列分析及var模型-免費(fèi)閱讀
【正文】 thus, in the long run it tends to go back to its longterm equilibrium (mean). Based on the ratio, we could argue that gold seems to be overvalued pared to silver at the moment. Of course, taking the ratio suggests a very simple cointegrating vector – in fact we assume a onetoone relationship. Before we can use the Johansen procedure, we have to make sure that the time series have the same order of integration I(p). We already know that gold and silver prices are both I(1) time series. Table 5 shows the results of the Johansen test for cointegration. In line with the VAR model, we use two lags.Table 5: Johansen testThe null hypothesis that there is no cointegration (r=0) can be rejected if we use the trace statistic. However, the null hypothesis that we have one cointegrating vector (r=1) cannot be rejected. The problem is that the maxlambda statistic does not support cointegration. I also tried logprices instead, which is mon in analysing goldsilver ratios。 hence, there is no serial correlation between error terms and any realisation of any independent variable (lead or lag). As we discovered, serial correlation (or autocorrelation) is very mon in financial time series and panel data. Furthermore, we assumed a predefined relation of causality: explanatory variable affect the dependent variable. 傳統(tǒng)的線性回歸模型假設(shè)嚴(yán)格的外生性，誤差項(xiàng)與可實(shí)現(xiàn)的獨(dú)立變量之間沒有序列相關(guān)性。 CointegrationA). There is evidence that multinationals outperform local peers due to the benefits of operating in many countries. At the same time, we know that 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
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