An overview of the Almon Distributed Lag model, its historical context, key features, mathematical formulation, importance, and application in econometrics.
The ARCH Effect describes how past shocks influence current volatility in time series data, especially in financial markets.
The ARCH model is a statistical approach used to forecast future volatility in time series data based on past squared disturbances. This model is instrumental in fields like finance and econometrics.
A comprehensive exploration of the Augmented Dickey-Fuller (ADF) test, used for detecting unit roots in time series data, its historical context, types, applications, mathematical formulas, examples, and related terms.
Autocorrelation, also known as serial correlation, measures the linear relation between values in a time series. It indicates how current values relate to past values.
An approach in empirical econometrics where model evaluation and selection are performed by a computerized algorithm, streamlining the process to produce robust and statistically significant models.
Autoregression (AR) is a statistical modeling technique that uses the dependent relationship between an observation and a specified number of lagged observations to make predictions.
The Autoregressive (AR) Model is a type of statistical model used for analyzing and forecasting time series data by regressing the variable of interest on its own lagged values.
Explore the Autoregressive Conditional Heteroscedasticity (ARCH) model, its historical context, applications in financial data, mathematical formulations, examples, related terms, and its significance in econometrics.
The Autoregressive Integrated Moving Average (ARIMA) is a sophisticated statistical analysis model utilized for forecasting time series data by incorporating elements of autoregression, differencing, and moving averages.
A projection of how the economy will develop if existing trends and policies continue unchanged. Models of the economy may be based on theory, econometrics, or some combination of these.
Bayesian Econometrics is an approach in econometrics that uses Bayesian inference to estimate the uncertainty about parameters in economic models, contrasting with the classical approach of fixed parameter values.
An in-depth exploration of the Between-Groups Estimator used in panel data analysis, focusing on its calculation, applications, and implications in linear regression models.
An in-depth look at binary choice models used to analyze decision-making processes involving two distinct alternatives.
An examination of the Breitung Test, used for testing unit roots or stationarity in panel data sets. The Breitung Test assumes a balanced panel with the null hypothesis of a unit root.
An in-depth look at the Breusch-Pagan Test, its importance in linear regression models, and how to perform and interpret it.
The Cochrane-Orcutt procedure is a two-step estimation technique designed to handle first-order serial correlation in the errors of a linear regression model. This method uses the ordinary least squares residuals to estimate the first-order autocorrelation coefficient and then rescale the variables to eliminate serial correlation in the errors.
Cointegration refers to a statistical property indicating a stable, long-run relationship between two or more time series variables, despite short-term deviations.
A comprehensive overview of cointegration, its historical context, types, key events, mathematical models, and importance in various fields such as economics and finance.
An in-depth exploration of counterfactual analysis in econometrics, including its historical context, methodologies, applications in macroeconomics and microeconomics, key events, and more.
The Cowles Foundation, originally founded as the Cowles Commission for Research in Economics, is renowned for its contributions to econometrics and general equilibrium theory. Established in 1932, it has significantly shaped economic thought and research.
An in-depth exploration of the dependent variable, its role in econometric models, mathematical representations, significance in predictive analysis, and key considerations.
Difference in Differences (DiD) is a statistical technique used to estimate the causal effect of a treatment or policy intervention using panel data. It compares the average changes over time between treated and untreated groups.
A comprehensive guide on dummy variables in statistical and econometric models, their importance, applications, and methods of creation.
An in-depth look at Durbin's Test for identifying first-order serial correlation in errors with a lagged dependent variable.
Dynamic Analysis involves the study of economic variables and how they evolve over time, offering insights into the temporal behavior and interdependencies of various economic factors.
Learn about econometric models, their historical context, types, key events, detailed explanations, mathematical formulas, diagrams, importance, examples, considerations, related terms, comparisons, interesting facts, quotes, and more.
A comprehensive overview of econometrics, including its historical context, types, key events, methodologies, and real-world applications.
Economic Research involves the systematic study of how societies produce, distribute, and consume goods and services, analyzing economic activities and relationships to inform policy, business, and personal decisions.
Endogeneity is the condition where an explanatory variable in a regression model correlates with the error term, leading to biased and inconsistent estimates.
Endogeneity problem occurs due to simultaneous causality between the dependent and endogenous variables in a model, leading to biased and inconsistent estimations. This article explores the origins, implications, and methods to address endogeneity in econometric models.
An in-depth exploration of endogenous variables, including their definitions, applications in econometrics, and related concepts such as endogeneity problems.
An estimate in econometrics refers to the value of an unknown model parameter obtained by applying an estimator to the data sample. This article explores its definition, historical context, key concepts, and much more.
Exogeneity refers to the condition where explanatory variables are uncorrelated with the error term, ensuring unbiased and consistent estimators in econometric models.
A comprehensive examination of exogenous variables, their significance in econometrics, examples, types, applications, and the importance in economic modeling.
An in-depth look at the Feasible Generalized Least Squares Estimator (FGLS) in econometrics, its historical context, key concepts, mathematical formulations, and practical applications.
An in-depth look at Frequency Domain Analysis, a method in time series econometrics utilizing spectral density to analyze and estimate the characteristics of stochastic processes.
A comprehensive guide to understanding and applying the GARCH Model for estimating volatility in financial time series data.
The General Linear Hypothesis involves a set of linear equality restrictions on the coefficients of a linear regression model. This concept is crucial in various fields, including econometrics, where it helps validate or refine models based on existing information or empirical evidence.
A generalization of the method of moments estimator applicable when the number of moment conditions exceeds the number of parameters to be estimated, computed by minimizing the sum of squared differences between the population moments and the sample moments.
An in-depth exploration of the Geometric Lag Model, its historical context, applications in economics, key formulas, and related concepts.
The Goldfeld–Quandt Test is a statistical method used to detect heteroscedasticity in regression models by dividing the data into two subgroups and comparing the variances of the residuals.
An exploration of Goodhart's Law, an observation by economist C. Goodhart, which states that when an empirical regularity is exploited for economic policy, it tends to lose its predictive reliability.
The Hausman Test is a model specification test commonly used in econometrics to evaluate the consistency and efficiency of two proposed estimators under different hypotheses.
Heteroscedasticity occurs when the variance of the random error is different for different observations, often impacting the efficiency and validity of statistical models. Learn about its types, tests, implications, and solutions.
Heteroskedasticity refers to a condition in regression analysis where the variance of the error terms varies across observations, complicating the analysis and necessitating adjustments.
Understanding the intricacies of the identification problem in economics, focusing on the challenge of estimating the parameters of structural equations when only equilibrium positions can be observed.
An Instrumental Variable (IV) is a key concept in econometrics used to account for endogeneity, ensuring the reliability of causal inference in regression analysis.
An in-depth look into Instrumental Variables (IV) and their role in addressing endogeneity in statistical models.
A device used to transform an infinite geometric lag model into a finite model with lagged dependent variable, making estimation feasible but introducing serial correlation in errors.
A symbol used to denote lags of a variable in time series analysis, where L is the lag operator such that Ly_t ≡ y_{t−1}, L^2y_t ≡ L(Ly_t) = y_{t−2}, etc. Standard rules of summation and multiplication can be applied.
A method for estimating unknown parameters by minimizing the sum of squared differences between observed and predicted values in a model.
An in-depth exploration of the Linear Probability Model, its history, mathematical framework, key features, limitations, applications, and comparisons with other models.
An in-depth examination of Linear Regression, its historical context, methodologies, key events, mathematical models, applications, and much more.
Macroeconometrics is the branch of econometrics that has developed tools specifically designed to analyze macroeconomic data. These include structural vector autoregressions, regressions with persistent time series, the generalized method of moments, and forecasting models.
The concept of mean reversion, its applications in various fields, historical context, models, and significance.
Microeconometrics focuses on the development and application of econometric methods for analyzing individual-level data, such as those of households, firms, and individuals. It encompasses a variety of tools including non-linear models, instrumental variables, and treatment evaluation techniques.
Discrete choice models used for the analysis of individual choice of mode of transportation.
The Monte Carlo Method is a powerful computational technique for investigating complex systems and economic models through random sampling and numerical simulations.
A statistical method used in time series analysis, the Moving Average (MA) Model uses past forecast errors in a regression-like model to predict future values.
Multicollinearity refers to strong correlations among the explanatory variables in a multiple regression model. It results in large estimated standard errors and often insignificant estimated coefficients. This article delves into the causes, detection, and solutions for multicollinearity.
Nested models in econometrics are models where one can be derived from another by imposing restrictions on the parameters. This article explains nested models, providing historical context, key concepts, mathematical formulation, and more.
An in-depth exploration of noise, its definitions in different contexts, historical evolution, types, key events, mathematical models, and its importance across various fields.
Panel data combines cross-sectional and time series data, providing a comprehensive dataset that tracks multiple entities over time for enhanced statistical analysis.
Panel data refers to data that is collected over several time periods on a number of individual units. It's used extensively in econometrics, statistics, and various social sciences to understand dynamics within data.
An overview of Pooled Least Squares, its application in panel data regression, key events, types, formulas, examples, and significance.
In Bayesian econometrics, the posterior refers to the revised belief or the distribution of a parameter obtained through Bayesian updating of the prior, given the sample data.
Understanding Predetermined Variables in Dynamic Econometric Models, their Importance, and Applications
An in-depth exploration of the concept of 'Prior' in Bayesian econometrics, including historical context, types, key events, mathematical models, applications, and related terms.
An in-depth look at qualitative choice models (also known as discrete choice models), their historical context, categories, key events, detailed explanations, mathematical formulations, applications, and more.
The Ramsey Regression Equation Specification Error Test (RESET) is a diagnostic tool used in econometrics to detect misspecifications in a linear regression model by incorporating non-linear combinations of explanatory variables.
A deep dive into Recursive Models, a specific version of simultaneous equations models characterized by a triangular coefficient matrix and no contemporaneous correlation of random errors across equations.
A comprehensive exploration of Regression Kink Design, a method of estimation designed to find causal effects when policy variables have discontinuities in their first derivative. Explore historical context, key events, formulas, diagrams, applications, and more.
A comprehensive overview of the Ramsey Regression Equation Specification Error Test (RESET), including historical context, methodology, examples, and applications in econometrics.
Explore the concept of residuals in regression analysis, their importance, key events, detailed explanations, and practical applications.
Understanding the Right-Hand-Side Variable in Regression Analysis and its Role as an Explanatory Variable.
Seasonal ARIMA (SARIMA) is a sophisticated time series forecasting method that incorporates both non-seasonal and seasonal elements to enhance the accuracy of predictions.
A comprehensive method for evaluating the robustness and responsiveness of models and investment projects to variations in assumptions and input factors.
A comprehensive look at the Simultaneous Equations Model (SEM), an econometric model that describes relationships among multiple endogenous variables and exogenous variables through a system of equations.
A comprehensive exploration of specification error in econometric models, including historical context, types, key events, explanations, formulas, charts, importance, examples, related terms, comparisons, interesting facts, inspirational stories, famous quotes, proverbs and clichés, expressions, jargon, FAQs, references, and summary.
Understanding Stationarity: Its Importance in Time Series Analysis and How It Applies to Various Fields
Understanding the concept, types, mathematical models, and applications of Structural Equations in econometrics.
Time-Series Data refers to data for the same variable recorded at different times, usually at regular frequencies, such as annually, quarterly, weekly, daily, or even minute-by-minute for stock prices. This entry discusses historical context, types, key events, techniques, importance, examples, considerations, and related terms.
An in-depth look at the Tobit Model, a regression model designed to handle censored sample data by estimating unknown parameters. Explore its historical context, applications, mathematical formulation, examples, and more.
A comprehensive examination of trends in time-series data, including types, key events, mathematical models, importance, examples, related terms, FAQs, and more.
Trend-Cycle Decomposition is an approach in time-series analysis that separates long-term movements or trends from short-term variations and seasonal components to better understand the forces driving economic variables.
A comprehensive guide to the Vector Autoregressive (VAR) model, including its history, types, key concepts, mathematical formulation, and practical applications in economics and finance.
Vector Autoregression (VAR) is a statistical model used to capture the linear interdependencies among multiple time series, generalizing single-variable AR models. It is widely applied in economics, finance, and various other fields to analyze dynamic behavior.
A comprehensive overview of the Vector Autoregressive (VAR) Model, including its historical context, mathematical formulation, applications, importance, related terms, FAQs, and more.
A comprehensive guide to the Vector Error Correction Model (VECM), its historical context, types, key events, mathematical formulations, importance, examples, related terms, and much more.
An in-depth exploration of volatility clustering, a fundamental concept in financial market dynamics where periods of high volatility are followed by periods of low volatility, and vice versa.
The Wald Test is a statistical method used to test restrictions on unknown parameters in a model using the maximum likelihood estimation.
White's Test is used to test the null hypothesis of homoscedasticity against the alternative of heteroscedasticity in a regression model.
A comprehensive overview of the within-groups estimator, a crucial technique for estimating parameters in models with panel data, using deviations from group means.
Comprehensive article detailing the concept of Allowance for Depreciation, also known as Accumulated Depreciation, its calculation methods, implications, and examples.
Econometrics is the application of statistical and mathematical methods to the study of economic data and problems.
Detailed exploration of forecasting techniques in economics and stock markets, covering methods, applications, and related concepts.
The Lorenz Curve visually represents income distribution across a population, highlighting economic inequality by comparing cumulative percentages of income against the population.
A comprehensive definition and exploration of normal goods, which are items for which demand rises as consumer income increases, under ceteris paribus conditions.
In economics, finance, and corporate planning, 'projection' refers to the estimate of future performance typically formulated by experts such as economists, corporate planners, and credit and securities analysts. This includes projecting metrics like GDP, inflation, unemployment, and company cash flow.
