Quantitative Economic Analysis

Quantitative Economic Analysis involves the use of statistical methods and mathematical models to interpret economic data, understand economic behavior, and make predictions about future economic trends. Economists employ a variety of tools, such as descriptive and inferential statistics, to analyze data and test economic theories. Descriptive statistics help summarize large datasets, while inferential statistics allow researchers to draw conclusions about populations based on sample data. Techniques like correlation analysis and regression are used to determine relationships between variables, providing insights into consumer behavior and market dynamics.

Additionally, economists utilize forecasting methods to predict future economic conditions by analyzing historical data and identifying trends. Structural models and time series analysis are common approaches to modeling economic phenomena, helping to understand causal relationships within economies. Overall, quantitative analysis is essential for economists to validate theories, assess economic policies, and navigate the complexities of real-world economic behavior, ensuring that their conclusions reflect the realities of diverse economic environments.

Published in: 2021

By: Wienclaw, Ruth

Subject Terms

Quantitative Economic Analysis

Economists are interested in interpreting data in order to better understand and predict the behavior of economies and economic variables. To do this, they need to be able to quantitatively analyze data in order to determine the effect of changes on variables of interest, or to weigh the relative merits of econometric models. Quantitative analysis techniques used in economics include many of the tools of inferential statistics as well as various tools for building empirical, testable models. Through the use of quantitative analysis, economists can test and validate their theories and revise them so that they better reflect the realities of real world economies.

Economists can be found working in a wide spectrum of situations: From academicians who work in universities and develop and test theoretical models of various aspects of economies; to economists working in government and industry who apply those models to the real world in an attempt to forecast future trends based on past and current data. The application of economic theory is the stuff of headlines. In the government, economists may analyze the pros and cons of various alternative economic policies under consideration based on various facts and figures. Economists working in banks may evaluate whether or not interest rates should be change. In the private sector, economists may be called upon to forecast the change in various economic variables such as exchange rate movements and their effect on the exports of their organization. In all these situations, it is important that the economist is able to quantitatively analyze data in order to determine the effect of changes on variables of interest or to weigh the relative merits of econometric models. Having a theory based on one's observations of historical trends or current conditions is a good start in understanding the realities of an economy. However, such observations on their own are insufficient for reliably forecasting the future. Economists must be able to quantify the facts known about various economic conditions and analyze these in order to test the validity of an economic model and forecast trends (Koop, 2009).

Applications

Mathematical Statistics & Economic Study

Descriptive Statistics

Mathematical statistics give economists a number of quantitative tools that enable them to develop and test theories which help them better understand and predict economic behavior. These tools range from ways to organize and summarize data so that they are more easily understandable, to methods for predicting future trends using data and models. At the simpler end of this continuum, descriptive statistics help economists to clearly describe large amounts of data using pie charts, histograms, and frequency. Other methods of descriptive statistics include measures of central tendency (i.e., mean, median, and mode) that give the "average" for a particular variable of interest as well as measures of variability (i.e., range and standard deviation) that help one understand how widely dispersed the values of the variable are.

Inferential Statistics

In addition to such descriptive tools, mathematical statistics also offer economists tools which allow them to make inferences about data. These techniques, called inferential statistics, allow one to draw conclusions about a population from a sample and test hypotheses to determine if the results of a study occur at a rate that is unlikely to be due to chance (i.e., have statistical significance). For purposes of hypothesis testing, the hypothesis is stated in two ways. A null hypothesis (H₀) is the statement that there is no statistical difference between the status quo and the experimental condition. In other words, the treatment being studied made no difference on the end result. The alternative hypothesis (H₁) states that there is a relationship between the two variables and that the intervening variable did make a difference in the outcome. In order to determine whether one should accept or reject the null hypothesis, one must first determine how the data are to be statistically analyzed. This decision is made during the design of the experiment so that the appropriate statistical tool can be chosen and the necessary data collected. One frequently used class of statistical tests used in hypothesis testing is the family of tools known as t-tests. These tests are used to analyze the mean of a population or compare the means of two different populations. (In other situations where one wishes to compare the means of two populations, a z statistic may be used.)

Correlation Techniques

Correlation techniques are another classification of techniques that can be used by economists. These techniques help economists better understand the degree to which two variables are consistently related. For example, correlation can help one understand the relationship between various economic indicators and purchasing behavior. Correlation coefficients show the degree of relationship between the two variables, and vary between 0.0 and 1.0. A correlation of 1.0 shows that the variables are completely related: a change in the value of one variable will signify a corresponding change in the other variable. A correlation of 0.0, on the other hand, shows that there is no relationship between the two variables: knowing the value of one variable tells nothing about the value of the other variable. A correlation coefficient also signifies how the two variables are related. If the correlation coefficient is positive, then as the value of one variable increases so does the value of the other variable. A negative correlation, on the other hand, means that as the value of one variable increases the value of the other variable decreases.

Analysis of Variance

Another family of inferential techniques that is used for analyzing data in applied settings is analysis of variance (ANOVA). This family of techniques is used to analyze the joint and separate effects of multiple independent variables on a single dependent variable and to determine the statistical significance of the effect. Multivariate analysis of variance (MANOVA) is an extension of this set of techniques that allows economists to test hypotheses on more complex problems involving the simultaneous effects of multiple independent variables on multiple dependent variables. The work of Kureshi, Sood, and Koshy (2009) offers an example of how analysis of variance can be meaningfully used on economic data. The authors were interested in developing a profile of customers of a single brand sport store in an emerging market in order to assist retailers in identifying revenue-generating customers. The authors used a survey research technique to collect data. Based on their shopping patterns at the store, the customers were classified as purposive patrons, purposive nonpatrons, and browsers. The authors used analysis of variance to analyze the survey results and determine differences in demographic and psychographic characteristics, media habits, and ownership of personal lifestyle products or services. The results of that analysis indicated that the purposive patrons differed significantly from other store visitors on eighteen characteristics, information that can be useful in identifying and targeting potential customers.

Regression Analysis

Another frequently used quantitative technique for analyzing economic data is regression analysis. This is a family of statistical tools that can help economists better understand and predict economic behavior. Regression analysis allows researchers to build mathematical models that can be used to predict the value of one variable from knowledge of another. There are a number of specific regression techniques that can be used by sociologists to model real world behavior.

Linear Regression

Simple linear regression analysis allows the modeling of two variables. Although correlation techniques, as discussed above, can indicate the degree of relationship between two variables, this knowledge alone does not necessarily provide sufficient information to predict behavior. In situations where one needs to be able to predict the value of one variable from knowledge of another variable based on the data, simple linear regression is often used. Simple linear regression is a bivariate statistical tool that allows the value of one dependent variable to be predicted from the knowledge of one independent variable. For example, simple linear regression might be used to predict one's income based on the level of education that the person had attained. The pairs of data used in linear regression analysis are typically graphed on a scatter plot that shows the values of the points for two-variable numerical data. A line of best fit is superimposed on the scatter plot and used to predict the value of the dependent variable based on different values of the independent variable. A sample scatter plot with line of best fit is shown in Figure 1.

Standard regression analysis techniques make several assumptions including that the model is correct and that the data are good. Unfortunately, the real world situations of interest to economists frequently do not follow these assumptions. For example, the variables themselves may be correlated, thereby inflating the value of the correlation (e.g., intelligence often being related to both income and educational level), the sample data may contain outliers (i.e., observations in which the value is abnormally large or small) that pull the distribution in the direction of the skew, or multicollinearity among subsets of the input variables such that they exhibit nearly identical linear relations, can all influence the validity of the results. Unfortunately, there are few indications in standard statistics to indicate that these problems have been incurred.

Multiple Linear Regression

Simple linear regression can be very useful for building models and predicting the value of one variable from the knowledge of the value of another variable. However, the type of problems investigated by economists in the real world are often more complex and include multiple variables. For many such situations, multiple linear regression can be used to model the data. As opposed to simple linear regression, multiple linear regression analysis allows the modeling of two or more independent variables to be used in the prediction of a dependent variable. By using multiple linear regression analysis instead of simple linear regression, an economist can potentially take all the important independent variables into account to determine their effect on the dependent variable of interest.

Henderson, Willson, Dunn, and Kazmierczak (2009) used regression to investigate the impact of forestry-related ordinances on timber harvesting in St. Tammany Parish, Louisiana. The authors observed that local government regulations regarding forestry practices on private land in the parish were often made without a sufficient understanding of the potential economic consequences of the ordinances. Using regression analysis to determine the size of this effect, the authors found a significant negative relationship between certain classifications of forestry-related ordinances and the level of timber harvest. These results had economic implications for the regions in which the ordinances were instituted.

Forecasting

In addition to looking at relationships between variables in the present, economists are also interested in making predictions about future values of various economic variables based on past data. To this end, economists frequently take existing (i.e., secondary) data regarding current and past economic behavior to extrapolate trends and predict future behavior. This type of quantitative analysis allows organizations to better position themselves to leverage knowledge into profits. This process is known as forecasting. Forecasting is the science of estimating or predicting patterns and variations, and is a technique that is frequently used in economics. Forecasting is based on the observation that there are a number of causes of variation in economic activity: Trends, business cycles, and seasonal fluctuations as well as irregular and random fluctuations. Trends are persistent, underlying directions in which something is moving in either the short, intermediate, or long term. Many trends tend to be linear rather than cyclic, steadily growing (or shrinking) over a period of years. On the other hand, trends in new industries tend to be curvilinear as the demand for the new product or service grows after its introduction then declines after the product or service becomes integrated into the economy.

Structural Models

Economists frequently use quantitative methods for forecasting through the development of models, including structural, time series, and deterministic models. Structural models are sets of mathematical functions that are designed to represent the causal relationships between variables within an economy. Another approach to modeling uses time series data. In this approach, data are gathered on a specific characteristic over a period of time. To be useful, time series data must be collected at intervals of regular length. In time series analysis, the sequence of observations is assumed to be a set of jointly distributed random variables. Time series analysis allows one to study the structure of the correlation of variables over time to determine the appropriateness of the model. The model can then be adjusted as needed to make it more representative of the real world situation. Deterministic models assume that the variable of interest is a deterministic function of time and does not include the effects of any underlying data uncertainty or variability in the time series.

Conclusion

The use of quantitative analysis is essential to helping economists better understand the way that economies work and how economic variables are related as well as to predict changes in one variable based on changes in another variable or variables. Although like most social scientists, economists are often limited to secondary data, they still have a wide range of quantitative techniques that available for analyzing not only simple economic behaviors and relationships, but also for modeling complex systems. Quantitative analysis allows economists to test and validate their theories and to revise them so that they better reflect the realities of real world economies.

Terms & Concepts

Forecasting: In business, forecasting is the science of estimating or predicting future trends. Forecasts are used to support managers in making decisions about many aspects of the business including buying, selling, production, and hiring.

Hypothesis: An empirically testable declaration that certain variables and their corresponding measure are related in a specific way proposed by a theory.

Inferential Statistics: A subset of mathematical statistics used in the analysis and interpretation of data. Inferential statistics are used to make inferences such as drawing conclusions about a population from a sample and in decision making.

Linear Regression: A statistical technique used to develop a mathematical model for use in predicting one variable from the knowledge of another variable.

Mathematical Statistics: A branch of mathematics that deals with the analysis and interpretation of data. Mathematical statistics provides the theoretical underpinnings for various applied statistical disciplines, including business statistics, in which data are analyzed to find answers to quantifiable questions.

Measures of Central Tendency: Descriptive statistics that are used to estimate the midpoint of a distribution. Measures of central tendency include the median (the number in the middle of the distribution), the mode (the number occurring most often in the distribution), and the mean (a mathematically derived measure in which the sum of all data in the distribution divided by the number of data points in the distribution).

Measures of Variability: Descriptive statistics that summarize how widely dispersed the data are over the distribution. The range is this difference between the highest and lowest scores. The standard deviation is a mathematically derived index of the degree to which scores differ from the mean of the distribution.

Model: A representation of a situation, system, or subsystem. Conceptual models are mental images that describe the situation or system. Mathematical or computer models are mathematical representations of the system or situation being studied.

Population: The entire group of subjects belonging to a certain category (e.g., all women between the ages of 18 and 27; all dry cleaning businesses; all college students).

Sample: A subset of a population. A random sample is a sample that is chosen at random from the larger population with the assumption that such samples tend to reflect the characteristics of the larger population.

Statistical Significance: The degree to which an observed outcome is unlikely to have occurred due to chance.

Survey Research: A type of research in which data about the opinions, attitudes, or reactions of the members of a sample are gathered using a survey instrument. The phases of survey research are goal setting, planning, implementation, evaluation, and feedback. As opposed to experimental research, survey research does not allow for the manipulation of an independent variable.

Validity: The degree to which a survey or other data collection instrument measures what it purports to measure. A data collection instrument cannot be valid unless it is reliable. Content validity is a measure of how well an assessment instrument items reflect the concepts that the instrument developer is trying to assess. Content validation is often performed by experts. Construct validity is a measure of well an assessment instrument measures what it is intended to measure as defined by another assessment instrument. Face validity is merely the concept that an assessment instrument appears to measure what it is trying to measure. Cross validity is the validation of an assessment instrument with a new sample to determine if the instrument is valid across situations. Predictive validity refers to how well an assessment instrument predicts future events.

Variable: An object in a research study that can have more than one value. Independent variables are stimuli that are manipulated in order to determine their effect on the dependent variables (response). Extraneous variables are variables that affect the response but that are not related to the question under investigation in the study.

Bibliography

Black, K. (2006). Business statistics for contemporary decision making (4th ed.). New York: John Wiley & Sons.

Du, R., & Kamakura, W. (2012). Quantitative trendspotting. Journal of Marketing Research (JMR), 49(4), 514-536. Retrieved December 2, 2013 from EBSCO Online Database Business Source Premier. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=78191680

Henderson, J. E., Willson, T. M., Dunn, M. A., & Kazierczak, R. F. Jr. (2009). The impact of forestry-related ordinances on timber harvesting in St. Tammany Parish, Louisiana. Contemporary Economic Policy, 27(1), 67-75. Retrieved March 18, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=36432460&site=ehost-live

Koop, G. (2009). Analysis of economic data (2nd ed.). New York: Wiley.

Kureshi, S., Sood, V., & Koshy, A. (2008). An in-depth profile of the customers of single brand store in emerging market. South Asian Journal of Management, 15(2), 81-99. Retrieved March 18, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=35962733&site=ehost-live

Lee, G. & McGuiggan, R. (2008). Understanding small- and medium-sized firms' financial skill needs. Journal of International Finance and Economics, 8(3), 93-101. Retrieved March 18, 2009, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=35618429&site=ehost-live

Özkaynak, B., Adaman, F., & Devine, P. (2012). The identity of ecological economics: Retrospects and prospects. Cambridge Journal of Economics, 36(5), 1123-1142. Retrieved December 2, 2013 from EBSCO Online Database Business Source Premier. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=80237275

Wieland, V., Cwik, T., Müller, G. J., Schmidt, S., & Wolters, M. (2012). A new comparative approach to macroeconomic modeling and policy analysis. Journal of Economic Behavior & Organization, 83(3), 523-541. Retrieved December 2, 2013 from EBSCO Online Database Business Source Premier. http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=83448380

Witte, R. S. (1980). Statistics. New York: Holt, Rinehart and Winston.

Quantitative Economic Analysis

On this Page

Subject Terms

Quantitative Economic Analysis

Applications

Mathematical Statistics & Economic Study

Descriptive Statistics

Inferential Statistics

Correlation Techniques

Analysis of Variance

Regression Analysis

Linear Regression

Multiple Linear Regression

Forecasting

Structural Models

Conclusion

Terms & Concepts

Bibliography

Suggested Reading