financial econometrics

Evaluating Regional Capital Mobility in China In the matlab .mat file, we have annual data on real per-capita consumption and real per-capital net income for nine regions in China. Between 1978 and 2007 (30 annual observations across 9 regions, so 260 pooled observations): Grouping of 21 provinces into 9 regions Region number Provinces included 1 Beijing, Tianjin, Hebei 2 Shanxi, Shandong 3 Liaoning, Jilin, Heilongjiang 4 Shanghai, Jiangsu, Zhejiang 5 Hunan 6 Henan, Anhui 7 Guangdong, Fujian 8 Guangxi, Yunan 9 Shaanxi, Xinjiang, Inner Mongolia The goal of the exercise is to evaluate the relationship between consumption and net income in these regions. Has it changed over time? If a country is becoming for integrated financially, it can borrow or lend within the broader national region, so that the link between consumption and net income in different regions should be less and less. Rich regions can lend to poorer regions and poorer regions can borrow from more prosperous ones. The measure of financial integration comes from the Feldstein-Horioka paradox. If there is free mobility of capital across nations or regions, then the link between national saving and national investment should be small. After all, under financial autarky, all investment has to be financed by domestic saving. Low and behold Professors Feldstein and Horioka discovered in the world economy, that there was a high correlation between national saving and national investment. This study challenged the prevailing rule that the capital markets are wide open and well integrated. Criticism of the Feldstein-Horioka result came from the fact that saving is measured differently across countries. So another approach was used: to test the relation between saving and investment across states, provinces or regions of particular countries. This is what we are going to do for China. You have a spreadsheet giving the real C and net income levels for the nine regions, in mcnelis_panel_finecon2015.xls. You are to estimate the model with a pooled sample and well as with fixed and random effects. You can also break the panel in half to see if there is a change in the more recent periods between consumption and net income. Word of caution: the data are in levels; we will have to put them in log first differences, to clear up auto correlation. There is also a likely relation between net income and the disturbance terms so you will likely want to use instrumental variables. Write up a three page essay telling me what you find in the data set: is there evidence of increasing financial integration in China? Attached is the word document and the xls sheet with data on real consumption and real net income (per capita) for nine regions of China. Your assignment is to examine these data to see if there is evidence of financial integration within China. That would imply a decreasing link within each region between Consumptions and net income, since borrowing and lending opportunities would be available through national capital markets. Attached is the spreadsheet with the panel data for the 9 regions of China, as well as a sample code for doing estimation with the data, for Panel data.. Panel data is really elementary. When we have data scross 9 regions and across time, we can either treat each region as different, with 9 different regressions, or we can pool all data into one sample, treat them as if they were all the same. Panel data is more realisitic: we are not completely different but we are also not completely identical. This is why we used fixed effects: across the regions some coefficients are the same, but some differ. Anyway for your extra credit, simply make use of this data set and tell me something interesting. You need the LeSage toolbox to do the regression. Note: for Mac users, use the matlab program Mypanel_China1.m and download the CHINA_DATA.mat file. The xlsread command does not work the same way on Mac versions of matlab. The m file should be run in the same directory as the speadhseet.