The relationship between stock prices and macroeconomy has been studied for many years, which provides a great deal of views and insights for government officers and managers. In previous literature, the research of relationship between stock prices and macroeconomy can be divided into two types: “stable correlation theory” and “divergence theory”. “Stable correlation theory” holds that the real economic activity is the basis of stock prices, so the stock prices should lead the real economy activity. In financial theory, Gorden’s formula holds that the stock prices are determined by the discounted value of future dividends. In securities investment, the well-known Graham’s method also stands on the values of securities. The “stable correlation theory” has a wide and profound impact on the researchers in finance society and securities investment society. The conclusion “stock prices are leading indicators for macroeconomy” was derived from the “stable correlation theory”. Many empirical analysis results support the “stable correlation theory”, among which the most representative ones are the empirical results of Fama [1] and Schwert [2]. Fama’s statistical analysis of stock returns in the United States between 1953 and 1987 shows that stock returns have significant explanations for future real economic activity, and there is a higher correlation between monthly, quarterly, annual returns and future production growth rates. Schwert also achieved a similar result. Following them, many scholars have drawn similar conclusions by using different econometric models at different frequencies, different time spans, or different macroeconomic indexes of different countries or regions. From the perspective of time frequency, there are daily data (see e.g. [3,4]), weekly data (see e.g. [5]), monthly data (see e.g. [6,7]), quarterly data (see e.g. [8-11]) and annual data (see e.g. [12-14]). In addition, some researches take into account different time frequencies simultaneously (see e.g. [15-17]). Time span varies from several years(see e.g. [3,17,18]) to several decades(see e.g. [5,7,12,19,20]). Focusing on a single country, many articles consider the relationship between US stocks and macroeconomic metrics (see e.g. [7-9,21-23]). On contrary, there are also articles study the relationship between the stock prices and mocroeconomy in G7 (see e.g. [26]), G20 (see e.g. [12]), India (see e.g. [6,19]), China (see e.g. [3,13]), Canada (see e.g. [27]), Austria (see e.g. [15]), Belgium (see e.g. [28]), South Africa (see e.g. [14]) and Malaysia (see e.g. [18,29]). As to the selected macroeconomic indicators, the studied metrics include GDP (see e.g. [14,17,27]), CPI (see e.g. [8]), M1 (see e.g. [8]), and also some other macroeconomic indexes (see e.g. [9]). Among these researches, the most popularly used testing model is linear Granger causality. Signal decomposition techniques (see e.g. [21]), dynamic factor model (see e.g. [22]), MRW method and GMM method (see e.g. [11]) model have been adopted in few researches, but not widely used. It is noteworthy that only a few of researches take both linear and nonlinear Granger causality test models into account. Specifically, Choudhry, Papadimitriou and Shabi [23] used monthly data from January 1990 to December 2011 to investigate the relationship between stock price volatility and the business cycle in the US, Canada, Japan and the UK via both linear and nonlinear bivariate causality tests. Results suggest that there is a bidirectional causal relationship between stock price volatility and the business cycle within each country. Literature [3,29] adopt the linear and nonlinear Granger causality model simultaneously to test the correlation of stock prices with the futures market, and that with the bond market.