Category: Learning modules
Instrumental variable is a third variable that estimates causal relationships in the regression analysis when an endogenous variable is present. Instrumental variables are useful when the independent variable in the regression model correlates with the error term in the model.
detection in supervised learning, Supervised learningAfter performing Autoregressive Integrated Moving Average (ARIMA) modelling in the previous article: ARIMA modeling for time series analysis in STATA, the time series GDP can be modelled through ARIMA (9, 2, 1) .
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometrics, trend analysisAn effect size is the magnitude or size of an effect resulting from a clinical treatment. Thus, in Comprehensive Meta Analysis (CMA), it assumes the reference of “treatment effect”.
CMA, CMA analysisIn statistics, to increase the prediction accuracy and interpret-ability of the model, LASSO (Least Absolute Shrinkage and Selection Operator) is extremely popular. It is a regression procedure that involves selection and regularisation and was developed in 1989. Lasso regression is an extension of linear regression that uses shrinkage. The lasso imposes a constraint on the sum of the absolute values of the model parameters. Here the sum has a specific constant as an upper bound.
exploratory model analysis, regressions in supervised learning, Supervised learningStructural equation model is a statistical modeling technique. Structural equation model (SEM) tests estimate or establish relationships between variables. It is a multivariate statistical data analysis technique. SEM analyzes the structural relationships or to establish causal relationships between variables.
SEM moduleThis article explains how to test ARIMA models and identifies the appropriate one for the process of forecasting time series GDP.
empirical analysis with econometrics, STATA for data analysis, time series analysis, time series for econometrics, trend analysisMissing data is one of the most common problems in almost all statistical analyses. If the data is not available for all the observations of variables in the model, then it is a case of ‘missing data’.
estimation in supervised learning, Supervised learning, trend analysisMarkov chain is one of the most important tests in order to deal with independent trials processes. There are two major principal theorems for these processes. The first one is the ‘Law of Large Numbers’ and the second one is the ‘Central Limit Theorem’.
estimation in supervised learning, Supervised learning