Short answers, pulled from the story.
Real business-cycle theory (RBC theory) is a class of new classical macroeconomic models that explains booms and recessions as the efficient response of economic agents to real, productivity-level shocks rather than to monetary or market failures. The theory holds that business cycle fluctuations represent optimal adjustments, and that national output at any point maximizes expected utility given the constraints agents face.
Finn E. Kydland and Edward C. Prescott introduced the core of real business-cycle theory in their 1982 paper Time to Build And Aggregate Fluctuations. A precursor was developed in the early 1970s by Milton Friedman and Robert Lucas, who focused on wage misperceptions rather than real productivity shocks.
RBC theory identifies technological shocks as the primary driver of business cycles. These shocks include innovations, bad weather, oil price spikes such as the one in the 1970s, and stricter environmental or safety regulations. A string of positive shocks produces a boom; a string of negative shocks leads to a recession.
RBC theory suggests that governments should concentrate on long-term structural change rather than discretionary fiscal or monetary policy. Because agents are already responding optimally to real shocks, intervention cannot improve on market outcomes and may only distort efficient adjustments.
Economists including Greg Mankiw and Larry Summers argued that RBC theory relies on three unrealistic assumptions: that technology can shift suddenly enough to drive observable cycles, that unemployment reflects voluntary decisions about how much to work, and that monetary policy is irrelevant to fluctuations. Kevin D. Hoover pointed out that the second assumption implies the 25% unemployment rate during the Great Depression in 1933 was a mass voluntary choice.
Calibration sets structural variables such as discount and capital depreciation rates using values drawn from independent econometric studies, typically within 95% confidence intervals, then checks whether simulated variable paths match observed economic data. Unlike standard estimation, calibration only revises the model when evidence against it is overwhelming, which critics argue makes RBC models difficult to falsify.