What Is Statistical Methods In Economics
Florencia Abila
These methods involve a set of techniques that allow economists to draw conclusions from empirical data, test hypotheses, and make predictions. This article provides an overview of the most common econometric and statistical methods used in economics. Regression Analysis: Regression analysis is a statistical method that aims to establish a relationship between a dependent variable and one or more independent variables. It allows economists to examine the impact of one or more factors on a particular outcome. In regression analysis, the dependent variable is the outcome of interest, while the independent variables are the factors that are hypothesized to influence the outcome. Regression analysis can be linear or non-linear, and it can be used to model both continuous and categorical dependent variables.
Regression analysis is a statistical technique used to model and analyze the relationship between a dependent variable and one or more independent variables. The goal of regression analysis is to find a mathematical equation that can be used to predict the values of the dependent variable based on the values of the independent variables (Raikov & Abrosimov, 2018).
There are two main types of regression analysis: simple regression and multiple regression. In simple regression, there is only one independent variable, while in multiple regression, there are two or more independent variables. The most commonly used regression method is linear regression, which assumes a linear relationship between the independent and dependent variables. Other types of regression methods include polynomial regression, logistic regression, and time-series regression (Wong, 2019).
Regression analysis can be used for various purposes, including prediction, modeling, and hypothesis testing. It is widely used in fields such as economics, finance, marketing, and social sciences, among others (Arbia et al., 2008).
To perform regression analysis, statistical software such as R, SAS, or SPSS is often used. The output of a regression analysis typically includes information about the coefficients of the independent variables, the statistical significance of the model, and measures of how well the model fits the data, such as the R-squared value.
Time Series Analysis: Time series analysis is a statistical method that is used to analyze data that is collected over time. It involves analyzing the patterns and trends in the data to identify any underlying patterns. Time series analysis is particularly useful in economics, where many economic variables are time-dependent. Examples of time series data include stock prices, inflation rates, and GDP growth rates.
Panel data analysis: Panel data analysis is a statistical method that is used to analyze data that has both a cross-sectional and a time-series component. In panel data analysis, the same set of individuals or entities are followed over time, and data is collected on both the individual or entity and the time period. Panel data analysis can be used to examine the impact of various factors on an outcome while controlling for other variables (Kmenta, 2010).
Experimental methods: Experimental methods involve conducting experiments to test hypotheses and establish causal relationships between variables. In economics, experiments are often used to study the impact of policy interventions or to test economic theories. Experimental methods involve creating a controlled environment where one or more variables can be manipulated while holding all other factors constant.
Survey methods: Survey methods involve collecting data through questionnaires or interviews. Surveys are often used to gather information about consumer behavior, attitudes, and preferences. Survey methods can be used to collect data from a large sample of individuals, which can be used to make inferences about a larger population.
Simulation methods: Simulation methods involve creating models of economic systems and using them to simulate the impact of different policy interventions or changes in economic variables. Simulation methods can be used to predict the likely outcomes of policy changes and to evaluate the effectiveness of different policy options (Mitchell, 1971).
Econometric and statistical methods are used in a wide range of applications in economics, including macroeconomic forecasting, financial analysis, labor market analysis, and environmental economics. These methods are essential tools for economists and policymakers who need to make informed decisions based on empirical data.
In conclusion, econometric and statistical methods are critical tools for analyzing economic data. These methods allow economists to draw conclusions from empirical data, test hypotheses, and make predictions. The most common econometric and statistical methods used in economics include regression analysis, time series analysis, panel data analysis, experimental methods, survey methods, and simulation methods. By using these methods, economists can make informed decisions and provide valuable insights into economic behavior and policy outcomes.
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The statistical quality of trial-based economic evaluations is often suboptimal, while a comprehensive overview of available statistical methods is lacking. Therefore, this review summarized and critically appraised available statistical methods for trial-based economic evaluations. A literature search was performed to identify studies on statistical methods for dealing with baseline imbalances, skewed costs and/or effects, correlated costs and effects, clustered data, longitudinal data, missing data and censoring in trial-based economic evaluations. Data was extracted on the statistical methods described, their advantages, disadvantages, relative performance and recommendations of the study. Sixty-eight studies were included. Of them, 27 (40%) assessed methods for baseline imbalances, 39 (57%) assessed methods for skewed costs and/or effects, 27 (40%) assessed methods for correlated costs and effects, 18 (26%) assessed methods for clustered data, 7 (10%) assessed methods for longitudinal data, 26 (38%) assessed methods for missing data and 10 (15%) assessed methods for censoring. All identified methods were narratively described. This review provides a comprehensive overview of available statistical methods for dealing with the most common statistical complexities in trial-based economic evaluations. Herewith, it can provide valuable input for researchers when deciding which statistical methods to use in a trial-based economic evaluation.
It is envisaged that participants interested in attending these courses are people currently undertaking, reviewing or commissioning analyses of health economics and outcomes research (HEOR) data, within the pharmaceutical and medical device industries, consultancy, academia or the health service.
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Practical exercises will be conducted in Stata to help participants appreciate how the methods described during the lectures can be used in real life. Some prior knowledge of Stata is recommended to be able to maximise the learning opportunity offered by the practical exercises. Each participant will be given access to the latest version of the Stata software. Stata codes (do-files) required to complete the exercises will be provided and all exercises will be supported by Faculty and a group of tutors.
This course uses a simulated, but realistic, patient-level dataset to illustrate the key concepts, which are like building blocks introduced with increasing sophistication. Ultimately the course aims to show students how to analyse these kinds of data to estimate within-study quantities (e.g. differential mean costs) or to derive key input parameters to populate a cost-effectiveness model to inform HTA decisions.
Before you register on these workshops please ensure you have secured the appropriate funding from your organisation, and (if applicable) that you allow yourself plenty of time to apply for any visas you may require to enter the UK, as you may experience some delay in getting these processed.
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For bookings of between 1 and 5 participants from the same organisation.
- Cancellations made 30 days or more before the programme start date: programme registration fees refunded less a 10% administrative charge.
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For larger bookings of 6 or more participants from the same organisation.
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Andrea Manca (course leader)
Andrea is Professor of Health Economics based in the Team for Economic Evaluation and Health Technology Assessment. His research interests include the application of statistical methods for the analysis of cost-effectiveness and health outcomes data, as well as the use of evidence synthesis techniques in economic evaluation to support health care decision making. Andrea has worked in economic evaluations of health technologies in several clinical areas.