Three Essays on Divisia Monetary Aggregates and GDP Nowcasting

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

Monetary Policy in China: A Factor Augmented VAR Approach

We use a Factor-Augmented VAR (FAVAR) model to investigate the effectiveness of monetary policy in China. As the Chinese economy is an open economy, our FAVAR model divides the macroeconomic variables into three groups. The first group contains measures of external shock to Chinese economy while others groups contain the measure of output and price. This approach allows factors to have an economic interpretation. We estimate our model with the maximum likelihood technique and that an increase in the change of money supply Divisia M2 has substantial and direct impact on Chinese economic activity and inflation. Our results also find that the change in change in interest rate has substantial impact on Chinese economy and on inflation with a delay of eight and twelve months respectively. Moreover, the impact of an increase in money supply Simple Sum M2 on economic activity is economically and statistically significant with a delay of four months. However, its impact on inflation is weak with delay of twelve months. In addition to the previous instruments, we use the Dollar-Yuan nominal exchange rate as monetary policy instrument and find that an increase in nominal exchange rate (depreciation of Yuan) has a substantial impact on both output and inflation with a delay of eight and eleven months. The latter is the main contribution of our paper. In contrast with the current literature on Chinese economy such as Fernald et al. (2013), our paper found that the monetary aggregates are still strong instrument of monetary in China if they are measured correctly by the central bank. The measure proposed by Barnett (1978 and 1980), Divisia monetary Aggregates, is statistically and economically significant which confirms the literature on developed economies (Belongia and Ireland (2012), Barnett et al. (2016))

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, our 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. We conclude that different nowcasting models should be used during the two regimes

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, our 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. We conclude that different nowcasting models should be used during the two regimes

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

Inhibition of NOX4 with GLX351322 alleviates acute ocular hypertension-induced retinal inflammation and injury by suppressing ROS mediated redox-sensitive factors activation

Reactive oxygen species (ROS) overproduction plays an essential role in the etiology of ischemic/hypoxic retinopathy caused by acute glaucoma. NADPH oxidase (NOX) 4 was discovered as one of the main sources of ROS in glaucoma. However, the role and potential mechanisms of NOX4 in acute glaucoma have not been fully elucidated. Therefore, the current study aims to investigate the NOX4 inhibitor GLX351322 that targets NOX4 inhibition in acute ocular hypertension (AOH)-induced retinal ischemia/hypoxia injury in mice. Herein, NOX4 was highly expressed in AOH retinas, particularly the retinal ganglion cell layer (GCL). Importantly, the NOX4 inhibitor GLX351322 reduced ROS overproduction, inhibited inflammatory factor release, suppressed glial cell activation and hyperplasia, inhibited leukocyte infiltration, reduced retinal cell senescence and apoptosis in damaged areas, reduced retinal degeneration and improved retinal function. This neuroprotective effect is at least partially associated with mediated redox-sensitive factor (HIF-1α, NF-κB, and MAPKs) pathways by NOX4-derived ROS overproduction. These results suggest that inhibition of NOX4 with GLX351322 attenuated AOH-induced retinal inflammation, cellular senescence, and apoptosis by inhibiting the activation of the redox-sensitive factor pathway mediated by ROS overproduction, thereby protecting retinal structure and function. Targeted inhibition of NOX4 is expected to be a new idea in the treatment of acute glaucoma