# EViews Reading - Unit 6 - Stepwise regression (Brooks 3ed Ch4 p145-150)

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4.6 Multiple regression in EViews 145 • • • • • • • • • • • • 4.6 Multiple regression in EViews using an APT-style model In the spirit of arbitrage pricing theory (APT), the following example will examine regressions that seek to determine whether the monthly returns on Microsoft stock can be explained by reference to unexpected changes in a set of macroeconomic and financial variables. Open a new EViews workfile to store the data. There are 254 monthly observations in the file ‘macro.xls’, starting in March 1986 and ending in April 2013. There are thirteen series in total plus a column of dates. The series in the Excel file are the Microsoft stock price, the S&P500 index value, the consumer price index, an industrial production index, Treasury bill yields for the following maturities: three months, six months, one year, three years, five years and ten years, a measure of ‘narrow’ money supply, a consumer credit series, and a ‘credit spread’ series. The latter is defined as the difference in annualised average yields between a portfolio of bonds rated AAA and a portfolio of bonds rated BAA. Import the data from the Excel file and save the resulting workfile as ‘macro.wf1’. The first stage is to generate a set of changes or differences for each of the variables, since the APT posits that the stock returns can be explained by reference to the unexpected changes in the macroeconomic variables rather than their levels. The unexpected value of a variable can be defined as the difference between the actual (realised) value of the variable and its expected value. The question then arises about how we believe that investors might have formed their expectations, and while there are many ways to construct measures of expectations, the easiest is to assume that investors have naive expectations that the next period value of the variable is equal to the current value. This being the case, the entire change in the variable from one period to the next is the unexpected change (because investors are assumed to expect no change). 1 Transforming the variables can be done as described above. Press Genr and then enter the following in the ‘Enter equation’ box: dspread = baa aaa spread – baa aaa spread(-1) Repeat these steps to conduct all of the following transformations: dcredit = consumer credit – consumer credit(-1) dprod = industrial production – industrial production(-1) rmsoft = 100 dlog(microsoft) rsandp = 100 dlog(sandp) dmoney = m1money supply – m1money supply(-1) 1 It is an interesting question as to whether the differences should be taken on the levels of the variables or their logarithms. If the former, we have absolute changes in the variables, whereas the latter would lead to proportionate changes. The choice between the two is essentially an empirical one, and this example assumes that the former is chosen, apart from for the stock price series themselves and the consumer price series.
146 Further development of classical linear regression inflation = 100 dlog(cpi) term = ustb10y – ustb3m and then click OK