Keynesian model
Chapter 2
Regression Analysis and Forecasting Models
A forecast is merely a prediction about the future values of data. However, most extrapolative model forecasts assume that the past is a proxy for the future. That is, the economic data for the 2012–2020 period will be driven by the same variables as was the case for the 2000–2011 period, or the 2007–2011 period. There are many traditional models for forecasting: exponential smoothing, regression, time series, and composite model forecasts, often involving expert forecasts. Regression analysis is a statistical technique to analyze quantitative data to estimate model parameters and make forecasts. We introduce the reader to regression analysis in this chapter.
The horizontal line is called the X-axis and the vertical line the Y-axis. Regression analysis looks for a relationship between the X variable (sometimes called the
“independent” or “explanatory” variable) and the Y variable (the “dependent” variable). For example, X might be the aggregate level of personal disposable income in the United States and Y would represent personal consumption expenditures in the
United States, an example used in Guerard and Schwartz (2007). By looking up these numbers for a number of years in the past, we can plot points on the graph.
More specifically, regression analysis seeks to find the “line of best fit” through the points. Basically, the regression line is drawn to best approximate the relationship
J.B. Guerard, Jr., Introduction to Financial Forecasting in Investment Analysis,
DOI 10.1007/978-1-4614-5239-3_2, # Springer Science+Business Media New York 2013
19
20
2 Regression Analysis and Forecasting Models
between the two variables. Techniques for estimating the regression line (i.e., its intercept on the Y-axis and its slope) are the subject of this chapter. Forecasts using the regression line assume that the relationship which existed in the past between the two variables will
Regression Analysis and Forecasting Models
A forecast is merely a prediction about the future values of data. However, most extrapolative model forecasts assume that the past is a proxy for the future. That is, the economic data for the 2012–2020 period will be driven by the same variables as was the case for the 2000–2011 period, or the 2007–2011 period. There are many traditional models for forecasting: exponential smoothing, regression, time series, and composite model forecasts, often involving expert forecasts. Regression analysis is a statistical technique to analyze quantitative data to estimate model parameters and make forecasts. We introduce the reader to regression analysis in this chapter.
The horizontal line is called the X-axis and the vertical line the Y-axis. Regression analysis looks for a relationship between the X variable (sometimes called the
“independent” or “explanatory” variable) and the Y variable (the “dependent” variable). For example, X might be the aggregate level of personal disposable income in the United States and Y would represent personal consumption expenditures in the
United States, an example used in Guerard and Schwartz (2007). By looking up these numbers for a number of years in the past, we can plot points on the graph.
More specifically, regression analysis seeks to find the “line of best fit” through the points. Basically, the regression line is drawn to best approximate the relationship
J.B. Guerard, Jr., Introduction to Financial Forecasting in Investment Analysis,
DOI 10.1007/978-1-4614-5239-3_2, # Springer Science+Business Media New York 2013
19
20
2 Regression Analysis and Forecasting Models
between the two variables. Techniques for estimating the regression line (i.e., its intercept on the Y-axis and its slope) are the subject of this chapter. Forecasts using the regression line assume that the relationship which existed in the past between the two variables will