Bayesian statistics has become an increasingly influential framework in economics, offering an alternative to the frequentist methods that dominated much of the twentieth century. At its core, the Bayesian approach is built around the idea of probability as a degree of belief rather than as a long-run frequency. Rooted in the eighteenth-century work of Thomas Bayes and formalised by Pierre-Simon Laplace, the Bayesian method uses Bayes’ theorem to update prior beliefs in light of new evidence. This simple but powerful principle—posterior belief equals prior belief updated by data—has profound implications for how economists model uncertainty, interpret evidence, and make decisions.
Read MoreOne of the most fundamental concepts in time series econometrics is stationarity. A stationary time series is one whose statistical properties—such as mean, variance, and autocorrelation—remain constant over time. This concept may appear technical, but it is central to the validity of econometric inference. Much of modern applied econometrics, from forecasting inflation to modelling asset prices, rests on the assumption of stationarity. When this condition is violated, standard results collapse, leading to spurious regressions, misleading inferences, and flawed policy conclusions.
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