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A statistical technique that deals with time series data, or trend analysis. Time Series Analysis is relevant in banking for forecasting economic indicators, stock prices, interest rates, and more.

A method used in economics to estimate the value of a good or service by breaking it down into its component attributes. In banking, Hedonic Regression might be used for mortgage valuation by considering the different features of a property.

Data collected from multiple subjects over multiple time periods. In banking, Panel Data is used to analyze and predict individual behavior, credit risk, and investment patterns over time.

A statistical test used to determine whether a time series variable is non-stationary and possesses a unit root. In banking, the Unit Root Test helps in deciding whether to use differences or levels of economic variables in regression analysis.

A statistical property of a collection of time series variables when they share a common stochastic drift. Banks use Cointegration to model the long-term equilibrium relationships between financial assets or economic series.

A statistical model used to capture the linear interdependencies among multiple time series. VAR models in banking help forecast the system of interrelated time series and the impact of random disturbances.

An econometric model that integrates short-term dynamics with long-term equilibrium in time series data. ECMs in banking are applicable for adjusting portfolios or strategies to get back to equilibrium after market shocks.

A regression model where the dependent variable is categorical, mainly used for predicting binary outcomes. In banking, the Logit Model is essential for credit scoring and predicting the probability of default.

A statistical phenomenon in which predictor variables in a multiple regression model are highly correlated. In banking, minimizing multicollinearity is crucial for the reliability of credit scoring models and risk assessments.

A method of estimating the unknown parameters in a linear regression model. OLS is used in banking to estimate relationships between variables, such as income and creditworthiness, for risk assessment and product pricing.

The correlation of a signal with a delayed copy of itself as a function of delay. In the context of banking, autocorrelation in financial returns can inform investment strategies and risk management practices.

A test statistic used to detect the presence of autocorrelation in the residuals from a regression analysis. In banking, ensuring no autocorrelation in the model residuals is important for accurate predictive models.

A statistical hypothesis test to determine if one time series can predict another. Granger Causality is used in banking to test the lead-lag relationship between various economic indicators and market indices.

The circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it. Heteroskedasticity in banking data can impact the reliability of risk modeling and pricing algorithms.

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, a tool for estimating the volatility of financial markets. Banks use GARCH models to measure, model, and predict the volatility of financial instruments for risk management.

A statistical technique that uses random sampling to obtain numerical results. The technique is used in banking for risk assessment and valuing instruments with probabilistic elements, like mortgage-backed securities.

A measure of the volatility or systematic risk of a security or portfolio in comparison to the market as a whole. Banks use the Beta Coefficient to assess risk and to calculate the cost of equity in the Capital Asset Pricing Model.

An analysis method that compares different entities at the same point in time. In banking, Cross-Sectional Analysis is used for portfolio construction, benchmarking and understanding sector-specific impacts on financial performance.

A quasi-experimental pretest-posttest design that estimates the effect of interventions by assigning a cutoff or threshold above or below which an intervention is assigned. Banks may use Regression Discontinuity Design for causal inference in assessing the impacts of new policies or strategies.