GDP data are published quarterly with a substantial lag, while many other monetary and financial decisions are made at higher frequencies. GDP nowcasting can evaluate the current quarter's GDP growth rate given the available economic data up to the point at which the nowcasting is conducted. Therefore, nowcasting GDP has become an increasingly important task for central banks. My dissertation explores nowcasting GDP growth rates, incorporating the Divisia monetary aggregate indexes as indicators, along with a large panel of economic data. This research contributes to the nowcasting literature by clarifying and summarizing existing work, and goes further, by introducing Divisia monetary aggregates into GDP nowcasting using a dynamic factor model. This new model produces better nowcasting results in the U.S. case than the Survey of Professional Forecasters at the Federal Reserve Bank of Philadelphia. Finally, the third chapter of my dissertation Chinese Divisia Monetary Index and GDP Nowcasting contributes to the literature by constructing Chinese Divisia monetary indexes, including M1, M2, and for the first time, M3 and M4. The two broader aggregates M3 and M4 were never published by the People's Bank of China. The third paper sheds lights on the increasing borrowing cost in China. The nowcasting results also show that the Chinese economy experienced a structural break in early 2012. Overall, the results demonstrate that Divisia indexes contain more information than simple sum aggregates, and thereby help to produce better results. My dissertation contain three chapters:;Literature Review on GDP Nowcasting and US Quarterly GDP Nowcasting. First I survey the literature on GDP nowcasting from the 1970s through to current research. This ranges from simple time series models to the current advanced econometric models, including dynamic factor models (DFM) with regime switching and structural changes. Then it moves on to nowcasting US quarterly GDP growth with dynamic factor model and exploring information from a large and unbalanced panel of time series. It compares the nowcasting results from DFM to the results from other nowcasting models. DFM extracts a few common factors from a large number of monthly variables, regresses the GDP data on common factors which explain the bulk of the co-movement of the economy. The comparison demonstrates that DFM functions better nowcasting results than Survey of Professional Forecasters (SPF).;Nowcasting US quarterly GDP with Divisia Monetary Index. In this chapter, I investigate the nowcasting power of Divisia Monetary Index in U.S. economy. I briefly survey the development of the Divisia Monetary Index, the theory behind it, and the employment of the Divisia Index in related forecasting research literature. Using the Divisia index available from the Advances in Monetary and Financial Measurement (AMFM) program directed by Professor William A. Barnett with the Center for Financial Stability, I investigate the forecasting and nowcasting power of Divisia Monetary Aggregates Indexes, Divisia M1, M2, and M3 and evaluate the contributions of these monetary indexes to the accuracy of nowcasting. I also compare the nowcasting results from DFM with the traditional simple sum monetary aggregates M1, M2, and M3 to the model with weighted Divisia Index M1, M2, and M3. The comparison shows that Divisia monetary aggregates are superior to simple sum monetary aggregates by 9.1% in accurately nowcasting GDP.;Chinese Divisia Monetary Index and GDP Nowcasting. Since China's enactment of the Reform and Opening-Up policy in 1978, China has become one of the world's fastest growing economies, with an annual GDP growth rate exceeding 10% between 1978 and 2008. But in 2015, Chinese GDP grew at 7 %, the lowest rate in five years. Many corporations complain that the borrowing cost of capital is too high. This paper constructs Chinese Divisia monetary aggregates M1 and M2, and, for the first time, constructs the broader Chinese monetary aggregates, M3 and M4. Those broader aggregates have never before been constructed for China, either as simple-sum or Divisia. The results shed light on the current Chinese monetary situation and the increased borrowing cost of money.;GDP data are published only quarterly and with a substantial lag, while many monetary and financial decisions are made at a higher frequency. GDP nowcasting can evaluate the current month's GDP growth rate, given the available economic data up to the point at which the nowcasting is conducted. Therefore, nowcasting GDP has become an increasingly important task for central banks. This paper nowcasts Chinese monthly GDP growth rate using a dynamic factor model, incorporating as indicators the Divisia monetary aggregate indexes, Divisia M1 and M2 along with additional information from a large panel of other relevant time series data. The results show that Divisia monetary aggregates contain more indicator information than the simple sum aggregates, and thereby help the factor model produce the best available nowcasting results.;In addition, results demonstrate that China's economy experienced a regime switch or structure break in 2012, which a Chow test confirmed the regime switch. Before and after the regime switch, the factor models performed differently. I conclude that different nowcasting models should be used during the two regimes.
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