Quantitative Methods and Economics
Statistics for Analysts
Time value of money is the foundation: present and future value, annuities, perpetuities, and uneven cash flows, with correct treatment of beginning- and end-of-period timing. Descriptive statistics require distinguishing mean, median, and mode for skew, and interpreting standard deviation, variance, and coefficient of variation. Probability covers conditional probability, Bayes' formula, and expected value. Inferential statistics covers sampling distributions, standard error, confidence intervals, hypothesis testing (including Type I and Type II errors and the meaning of a p-value), correlation, and simple regression with its assumptions and interpretation of R-squared.
Economics
Microeconomics emphasizes elasticity and its link to revenue, market structures and their pricing implications, and the behavior of costs in the short versus long run. Macroeconomics covers aggregate supply and demand, business cycles, inflation and unemployment, monetary and fiscal policy transmission, and exchange rate regimes including interest rate parity and purchasing power parity. Traps include confusing nominal and real rates, misapplying the Fisher relation, and assuming a p-value is the probability that the null hypothesis is true.
Worked Example
A test of a mean return uses a sample of 36 observations with a sample mean of 9%, a known standard deviation of 6%, and a hypothesized mean of 8%. The standard error is 1%, so the test statistic is (9 − 8)/1 = 1.00, which is not significant at the 5% level for a two-tailed test (critical value about 1.96). Rule of thumb: always compute the standard error before the test statistic.