Gross State Product

Gross state product is the total market value of all newly produced final goods and services in a state over a given period of time. These estimate are usually provided at a quarterly frequency (four times a year). The most recent estimates are often revised as more accurate information becomes available. While real GSP is but one measure of output and income, it is the benchmark that is most closely watched and associated with real economic activity1.

Historical Data

Long run annual estimates of nominal and real2 gross state product (GSP) show us how an economy grows over time. The U.S. economy exhibits a continual long run increase in standards of living post World War II. The same is not true of the Louisiana economy. Prior to hurricane Katrina Louisiana exhibited a similar growth pattern as the U.S., but after Katrina state economic growth has flatlined. Since the Great Recession real income growth has been negative.

Usually nominal and real GDP or GSP intersect at the base (chain) year only. For Louisiana real (income) intersects nominal income twice in the past ten years. Real incomes are trending down while prices are still rising (albeit at a lower growth rate than what is seen in the early 2000s).

Quarterly GSP

Quarterly estimates of GSP show a more rich dynamic process, but not generally different than the annual data shown above. The downward trend in quarterly real GSP (the blue line) is more pronounced when not averaged at an annual rate.

The quarterly estimates also provide us a more rich understanding of the state economy compared with the overall U.S. economy. The inflationary pressure from monetary policy can be seen during the later 2000s until about 2011. Louisiana real GDP is more closely aligned with nominal GDP from 2011 to 2016 (and in the quarterly estimates these lines intersect three times). After 2016 inflationary pressure picks back up again, but incomes have not kept up.

GSP Forecasts

Forecasts3 for Louisiana GSP are made from an aggregation of multiple statistical models designed to approximate the underlying data generating process of the available data. A Bayesian model averaging approach is used here to capture the joint uncertainty that any given model may be misspecified as well as to capturing the probabilistic uncertainty inherent in each individual estimate. Observed data is given in blue while forecasts are presented in orange. The weighted average of all models used is represented by the solid orange line. The upper bound and lower of the cone of uncertainty surrounding these estimates is represented by the dashed upper and lower lines respectively.


  1. Please note that the graphs below are interactive HTML widgets. Please hover over each to examine the underlying data that comprises them.

  2. Nominal GSP is constructed using current prices and output data. Real GSP is chained using the prices of a base year in order to hold the effect of rising prices (inflation) constant.

  3. Forecasts are provided as a convenience and for informational purposes only without any explicit or implied warranty. The authors and publishers of this post and site bear no responsibility for the information provided here and cannot be held liable for any negative consequences that may arise due its publication. Forecasting the future is inherently a tricky proposition, and all forecasts have an error term attached to them. Please exercise caution when making financial and business decisions based on the information provided. Use this information as a single input into your decisions making process.

Patrick is an assistant professor of Economics at Louisiana Tech University. He researches interest rate determination and the inflationary consequences of suboptimal monetary policy. He teaches monetary economics, research methods & macroeconomic theory.