時間序列分析及var模型-全文預覽
【正文】 our position. We need to avoid that we have to trade too frequently to reduce transaction costs. The following figure cumulates the deviations from the longterm equilibrium and shows periods of over an undervaluation.In practice, you would run different scenarios and determine losses and profits from following different trading rules. Moreover, you need to test your model outofsample to ensure that it holds. Currently, we assume that we update the model every day, which is not necessarily the case. Exercises Interpretation of VECMInterpret the following VECM result. In particular, discuss whether there is a longterm equilibrium between gold and silver prices and highlight the shortterm dynamics. ReferencesEnders, W. (2004) Applied econometric time series, 2nd edition, John Wiley amp。 however, I don’t obtain clear results.Given the extreme increase in volatility in prices, it might be likely that there are structural breaks in an alleged cointegration vector. Structural breaks are difficult to handle.Another way to look at this problem is to test whether price ratios or logprice ratios are stationary time series. If they are stationary, then the two underlying time series are cointegrated and the ratio indicates the cointegration vector. Again DickeyFuller tests cannot reject the null hypothesis。A). 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 of two (or more) stationary time seri
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