Sources of Macroeconomic Data

Proud Economy 0011

Bureau of Economic Analysis, U.S. Department of Commerce (BEA)

Concepts and Methods of the U.S. National Income and Product Accounts:

The national income and product accounts (NIPAs) are one of the three major elements of the U.S. national economic accounts. The NIPAs display the value and composition of national output and the distribution of incomes generated in its production.

The other major elements of the U.S. national economic accounts are the industry accounts, which are also prepared by the Bureau of Economic Analysis (BEA), and the flow of funds accounts, which are prepared by the Federal Reserve Board. The industry accounts consist of the input-output (I-O) accounts, which trace the flow of goods and services among industries in the production process and which show the value added by each industry and the detailed commodity composition of national output, and the gross domestic product (GDP) by industry accounts, which measure the contribution of each private industry and of government to GDP. The flow of funds accounts record the acquisition of nonfinancial and financial assets (and the incurrence of liabilities) throughout the U.S. economy, the sources of the funds used to acquire those assets, and the value of assets held and of liabilities owed.

In addition, BEA prepares two other sets of U.S. economic accounts: the international accounts, which consist of the international transactions (balance of payments) accounts and the international investment position accounts; and the regional accounts, which consist of the estimates of GDP by state and by metropolitan area, of state personal income, and of local area personal income. Finally, the U.S. Bureau of Labor Statistics prepares estimates of productivity for the U.S. economy (which are partly based on the estimates of GDP). Altogether, the system of U.S. economic accounts presents a coherent, comprehensive, and consistent picture of U.S. economic activity Labor Statistics prepares estimates of productivity for the U.S. economy (which are partly based on the estimates of GDP). Altogether, the system of U.S. economic accounts presents a coherent, comprehensive, and consistent picture of U.S. economic activity.

(Download NIPA Tables)
(Gross-Domestic-Product-(GDP)-by-Industry Data)
(Regional Economic Accounts: Download CSV)
(Direct Investment and Multinational Companies: Comprehensive data)

A Primer on BEA’s Industry Accounts By Mary L. Streitwieser:

BEA’s industry accounts—which include the annual and benchmark input-output (I-O) accounts, the GDP by industry accounts, the KLEMS statistics, and satel­ lite accounts—provide answers to such questions. Broadly speaking, the accounts facilitate the study of the internal workings of the U.S. economy.

Concepts and Methods of the Input-Output Accounts by Karen J. Horowitz and Mark A. Planting:

The input-output (I-O) accounts are an integral and essential element of the U.S. economic accounts. First, they are the building blocks for other economic accounts. Prominent among these are BEA’s national income and product accounts (NIPAs), which feature the estimates of gross domestic product (GDP). Second, the I-O accounts provide detailed statistics on economic processes and relationships. They incorporate a complete, balanced set of economic statistics, and they present a full accounting of industry and final-use transactions.

Board of Governors of the Federal Reserve System

(Data Download Program)
(Complete table listing)

Financial Accounts Guide

This interactive documentation serves as a guide to the data compiled and published as part of the “Financial Accounts of the United States” (Z.1) data release, previously called the “Flow of Funds Accounts of the United States”. The Z.1 includes flow of funds, balance sheet, and integrated macroeconomic account data. This comprehensive documentation system is designed to help a user understand the links between series and underlying source data by providing the capability to search or browse the vast amount of information underlying the Z.1.

The U.S. Flow of Funds Accounts and Their Uses by Albert M. Teplin

In simple terms, the flow of funds accounts measure financial flows across sectors of the economy, tracking funds as they move from those sectors that serve as sources of capital, through intermediaries (such as banks, mutual funds, and pension funds), to sectors that use the capital to acquire physical and financial assets. With data extending back more than half a century, the accounts provide a broadly consistent set of time-series data for measuring financial flows in the economy.

Gary Young (proudindiv)

Examining "Macroeconometric Modeling by Ray C. Fair"

Proud Economy 0010

This looks like something that I wanted to learn back in the 1970's, but was unable to get into. I started programming in 1967 and worked at the Federal Reserve Bank of San Francisco for a while. I became aware of their economists and econometric modeling, but I was in the wrong world to break into that field.

Professor Fair's information looks like he has been there and he has made his information available on the internet at "fairmodel.econ.yale.edu". Now I wonder if one could use his work to reconstruct something like his model from scratch and use that to learn about macroeconomics?

Reading Fair's "Reflections on Macroeconometric Modeling", July 2013, it sounds like he is talking about the right stuff, but I don't understand it enough to evaluate it at all. I would start with a naive approach, see how much of Fair's model I can understand and extract, and attempt to formulate an infrastructure to build my versions.

Starting with "The US Model Appendix A" sectors

• Households
• Firms
• Financial Institutions
• Foreign Entities
• Government

we can then sort out the variables (about 350) and equations (188 of them). The "The Fair-Parke Program" appears to contain the Fortran code and all of data that is used to run the models at "The US Model" and "The MCI Model".

More as I get to it

Gary Young (proudindiv)

Federal Reserve Economic Data - FRED - St. Louis Fed

Proud Economy 0009

Modeling

Proud Economy 0008

“The model is generally referred to as an ARIMA(p,d,q) model where parameters p, d, and q are non-negative integers that refer to the order of the autoregressive, integrated, and moving average parts of the model respectively. ARIMA models form an important part of the Box-Jenkins approach to time-series modelling.” wikipedia

I first learned about ARIMA models by taking an econometrics course at the University of Washington in 1976 (I think) from Charles R. Nelson. I took the class as a hobby and never followed up on it, even though I've thought about the material much. Along with the original “Time series analysis: Forecasting and control” by George Box and Gwilym Jenkins (1970, Holden-Day), we used Nelson's “Applied Time Series Analysis for Managerial Forecasting” and in the cover of my copy, which I still have, are a couple of hand written programs for an HP55 programable calculator. 1976 was pre-personal computers, so computer access was by submitting batch jobs on punch cards at the university computer center and picking up the output listing the next day. I had a new HP55 programmable calculator. Wow, this dates me, but it explains the direction that I've been taking my posts in the blog. There are now lots of standard econometrics textbooks with the ARIMA models.

Professor Nelson, now apparently retired, still has a presence on the UW Econ site, with his Student Help Page. That links to related information at Professor Fair's site at Yale and his thoughts on Macroeconometric Modeling. These can be used as references for macroeconomics and modeling, along with the wikipedia. I'm rambling in this post and have strayed from the ARIMA models, but this is the type of information that I had hoped to find for the blog.

Gary Young

Vector autoregressions

Proud Economy 0007

Looking at “Forecasting: principles and practice: An online textbook by Rob J Hyndman and George Athanasopoulos, they have an example similiar to what I was thinking about in a couple of the prior posts using the vars R package. These use multiple time series in a forecast.

Using these libraries:

library(fpp)
library(vars)

Their section 9/2 Vector autoregressions contains R code like this:

VARselect(usconsumption, lag.max = 8, type = "const")$selection

## AIC(n)  HQ(n)  SC(n) FPE(n) 
##      5      1      1      5

var <- VAR(usconsumption, p = 3, type = "const")
serial.test(var, lags.pt = 10, type = "PT.asymptotic")

## 
##  Portmanteau Test (asymptotic)
## 
## data:  Residuals of VAR object var
## Chi-squared = 33.38, df = 28, p-value = 0.2219

## $serial
## 
##  Portmanteau Test (asymptotic)
## 
## data:  Residuals of VAR object var
## Chi-squared = 33.38, df = 28, p-value = 0.2219

summary(var)

## 
## VAR Estimation Results:
## ========================= 
## Endogenous variables: consumption, income 
## Deterministic variables: const 
## Sample size: 161 
## Log Likelihood: -338.797 
## Roots of the characteristic polynomial:
## 0.767 0.553 0.524 0.524 0.318 0.318
## Call:
## VAR(y = usconsumption, p = 3, type = "const")
## 
## 
## Estimation results for equation consumption: 
## ============================================ 
## consumption = consumption.l1 + income.l1 + consumption.l2 + income.l2 + consumption.l3 + income.l3 + const 
## 
##                Estimate Std. Error t value Pr(>|t|)    
## consumption.l1   0.2228     0.0858    2.60   0.0103 *  
## income.l1        0.0404     0.0623    0.65   0.5180    
## consumption.l2   0.2014     0.0900    2.24   0.0267 *  
## income.l2       -0.0983     0.0641   -1.53   0.1273    
## consumption.l3   0.2351     0.0882    2.66   0.0085 ** 
## income.l3       -0.0242     0.0614   -0.39   0.6944    
## const            0.3197     0.0912    3.51   0.0006 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 0.63 on 154 degrees of freedom
## Multiple R-Squared: 0.218,   Adjusted R-squared: 0.188 
## F-statistic: 7.17 on 6 and 154 DF,  p-value: 9.38e-07 
## 
## 
## Estimation results for equation income: 
## ======================================= 
## income = consumption.l1 + income.l1 + consumption.l2 + income.l2 + consumption.l3 + income.l3 + const 
## 
##                Estimate Std. Error t value Pr(>|t|)    
## consumption.l1   0.4871     0.1164    4.19  4.8e-05 ***
## income.l1       -0.2488     0.0845   -2.94  0.00374 ** 
## consumption.l2   0.0322     0.1221    0.26  0.79213    
## income.l2       -0.1111     0.0870   -1.28  0.20317    
## consumption.l3   0.4030     0.1197    3.37  0.00096 ***
## income.l3       -0.0915     0.0833   -1.10  0.27348    
## const            0.3628     0.1237    2.93  0.00386 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 0.855 on 154 degrees of freedom
## Multiple R-Squared: 0.211,   Adjusted R-squared: 0.18 
## F-statistic: 6.87 on 6 and 154 DF,  p-value: 1.76e-06 
## 
## 
## 
## Covariance matrix of residuals:
##             consumption income
## consumption       0.397  0.196
## income            0.196  0.731
## 
## Correlation matrix of residuals:
##             consumption income
## consumption       1.000  0.364
## income            0.364  1.000

fcst <- forecast(var)
plot(fcst, xlab = "Year")

Conference Boards referenced datasets as in my prior post:

Now to format our data to do the same.

library(xts)
library(Quandl)
# Quandl.auth('yourauthenticationtoken')

getts <- function(series, name) {
    y <- Quandl(series, collapse = "quarterly", type = "xts", start_date = "1992-01-01", 
        end_date = "2013-01-31")
    names(y)[1] <- name
    return(y)
}

• Real GDP
• Real Consumer Spending
• Housing Starts
• Real Capital Spending
• Net Exports

gdp <- getts("FRED/GDPC1", "gdp")
pce <- getts("FRED/PCE", "pce")
houst <- getts("FRED/HOUST", "houst")
atcgno <- getts("FRED/ATCGNO", "atcgno")
bopgstb <- getts("FRED/BOPGSTB", "bopgstb")

USeconomy <- cbind(gdp, pce, houst, atcgno, bopgstb)
summary(USeconomy)

##      Index                 gdp             pce            houst     
##  Min.   :1992-03-31   Min.   : 8151   Min.   : 4156   Min.   : 505  
##  1st Qu.:1997-06-30   1st Qu.: 9801   1st Qu.: 5527   1st Qu.:1046  
##  Median :2002-09-30   Median :11587   Median : 7482   Median :1475  
##  Mean   :2002-09-29   Mean   :11357   Mean   : 7637   Mean   :1368  
##  3rd Qu.:2007-12-31   3rd Qu.:12949   3rd Qu.: 9745   3rd Qu.:1649  
##  Max.   :2013-01-01   Max.   :13726   Max.   :11290   Max.   :2151  
##      atcgno          bopgstb      
##  Min.   : 44972   Min.   :-64214  
##  1st Qu.: 59285   1st Qu.:-46860  
##  Median : 67165   Median :-34269  
##  Mean   : 68753   Mean   :-32265  
##  3rd Qu.: 80431   3rd Qu.:-10475  
##  Max.   :102161   Max.   : -2641

VARselect(USeconomy, lag.max = 8, type = "const")$selection

## AIC(n)  HQ(n)  SC(n) FPE(n) 
##      2      2      2      2

var <- VAR(USeconomy, p = 2, type = "const")
serial.test(var, lags.pt = 10, type = "PT.asymptotic")

## 
##  Portmanteau Test (asymptotic)
## 
## data:  Residuals of VAR object var
## Chi-squared = 226, df = 200, p-value = 0.1002

## $serial
## 
##  Portmanteau Test (asymptotic)
## 
## data:  Residuals of VAR object var
## Chi-squared = 226, df = 200, p-value = 0.1002

summary(var)

## 
## VAR Estimation Results:
## ========================= 
## Endogenous variables: gdp, pce, houst, atcgno, bopgstb 
## Deterministic variables: const 
## Sample size: 83 
## Log Likelihood: -2971.243 
## Roots of the characteristic polynomial:
##    1 0.935 0.935 0.926 0.717 0.717 0.412 0.358 0.358 0.0955
## Call:
## VAR(y = USeconomy, p = 2, type = "const")
## 
## 
## Estimation results for equation gdp: 
## ==================================== 
## gdp = gdp.l1 + pce.l1 + houst.l1 + atcgno.l1 + bopgstb.l1 + gdp.l2 + pce.l2 + houst.l2 + atcgno.l2 + bopgstb.l2 + const 
## 
##             Estimate Std. Error t value Pr(>|t|)    
## gdp.l1      9.33e-01   1.47e-01    6.35  1.7e-08 ***
## pce.l1      4.45e-01   1.39e-01    3.19   0.0021 ** 
## houst.l1    5.39e-02   7.25e-02    0.74   0.4598    
## atcgno.l1   9.84e-04   1.02e-03    0.97   0.3359    
## bopgstb.l1  1.62e-03   2.02e-03    0.80   0.4273    
## gdp.l2      2.38e-02   1.40e-01    0.17   0.8656    
## pce.l2     -3.78e-01   1.39e-01   -2.71   0.0084 ** 
## houst.l2    7.13e-02   8.09e-02    0.88   0.3807    
## atcgno.l2  -1.41e-03   1.02e-03   -1.38   0.1716    
## bopgstb.l2  2.42e-03   2.00e-03    1.21   0.2306    
## const      -1.76e+00   1.80e+02   -0.01   0.9922    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 59.5 on 72 degrees of freedom
## Multiple R-Squared: 0.999,   Adjusted R-squared: 0.999 
## F-statistic: 6.61e+03 on 10 and 72 DF,  p-value: <2e-16 
## 
## 
## Estimation results for equation pce: 
## ==================================== 
## pce = gdp.l1 + pce.l1 + houst.l1 + atcgno.l1 + bopgstb.l1 + gdp.l2 + pce.l2 + houst.l2 + atcgno.l2 + bopgstb.l2 + const 
## 
##             Estimate Std. Error t value Pr(>|t|)    
## gdp.l1      3.92e-01   1.60e-01    2.46    0.016 *  
## pce.l1      9.83e-01   1.51e-01    6.49  9.6e-09 ***
## houst.l1   -3.33e-02   7.87e-02   -0.42    0.673    
## atcgno.l1   2.73e-05   1.10e-03    0.02    0.980    
## bopgstb.l1  3.81e-04   2.20e-03    0.17    0.863    
## gdp.l2     -3.58e-01   1.53e-01   -2.35    0.021 *  
## pce.l2      1.02e-02   1.51e-01    0.07    0.947    
## houst.l2    5.79e-02   8.78e-02    0.66    0.512    
## atcgno.l2  -1.08e-03   1.11e-03   -0.97    0.335    
## bopgstb.l2  6.47e-04   2.17e-03    0.30    0.767    
## const      -1.94e+02   1.96e+02   -0.99    0.325    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 64.6 on 72 degrees of freedom
## Multiple R-Squared: 0.999,   Adjusted R-squared: 0.999 
## F-statistic: 9.37e+03 on 10 and 72 DF,  p-value: <2e-16 
## 
## 
## Estimation results for equation houst: 
## ====================================== 
## houst = gdp.l1 + pce.l1 + houst.l1 + atcgno.l1 + bopgstb.l1 + gdp.l2 + pce.l2 + houst.l2 + atcgno.l2 + bopgstb.l2 + const 
## 
##             Estimate Std. Error t value Pr(>|t|)    
## gdp.l1      5.57e-03   2.57e-01    0.02     0.98    
## pce.l1      1.93e-01   2.44e-01    0.79     0.43    
## houst.l1    9.12e-01   1.27e-01    7.19  4.8e-10 ***
## atcgno.l1  -1.33e-03   1.78e-03   -0.75     0.46    
## bopgstb.l1  3.40e-03   3.54e-03    0.96     0.34    
## gdp.l2      4.37e-02   2.46e-01    0.18     0.86    
## pce.l2     -2.09e-01   2.44e-01   -0.86     0.39    
## houst.l2    6.99e-02   1.41e-01    0.49     0.62    
## atcgno.l2  -1.21e-03   1.78e-03   -0.68     0.50    
## bopgstb.l2 -6.53e-04   3.50e-03   -0.19     0.85    
## const      -1.65e+02   3.15e+02   -0.52     0.60    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 104 on 72 degrees of freedom
## Multiple R-Squared: 0.954,   Adjusted R-squared: 0.948 
## F-statistic:  150 on 10 and 72 DF,  p-value: <2e-16 
## 
## 
## Estimation results for equation atcgno: 
## ======================================= 
## atcgno = gdp.l1 + pce.l1 + houst.l1 + atcgno.l1 + bopgstb.l1 + gdp.l2 + pce.l2 + houst.l2 + atcgno.l2 + bopgstb.l2 + const 
## 
##             Estimate Std. Error t value Pr(>|t|)    
## gdp.l1      4.12e+00   1.43e+01    0.29  0.77404    
## pce.l1      5.84e+01   1.36e+01    4.29  5.4e-05 ***
## houst.l1   -2.00e+01   7.06e+00   -2.83  0.00596 ** 
## atcgno.l1   3.84e-01   9.90e-02    3.89  0.00023 ***
## bopgstb.l1 -8.74e-03   1.97e-01   -0.04  0.96478    
## gdp.l2     -9.36e+00   1.37e+01   -0.68  0.49622    
## pce.l2     -5.17e+01   1.36e+01   -3.81  0.00029 ***
## houst.l2    2.63e+01   7.88e+00    3.33  0.00135 ** 
## atcgno.l2   3.49e-01   9.94e-02    3.51  0.00077 ***
## bopgstb.l2  1.56e-01   1.95e-01    0.80  0.42522    
## const       1.89e+04   1.76e+04    1.08  0.28534    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 5800 on 72 degrees of freedom
## Multiple R-Squared: 0.848,   Adjusted R-squared: 0.827 
## F-statistic: 40.2 on 10 and 72 DF,  p-value: <2e-16 
## 
## 
## Estimation results for equation bopgstb: 
## ======================================== 
## bopgstb = gdp.l1 + pce.l1 + houst.l1 + atcgno.l1 + bopgstb.l1 + gdp.l2 + pce.l2 + houst.l2 + atcgno.l2 + bopgstb.l2 + const 
## 
##             Estimate Std. Error t value Pr(>|t|)    
## gdp.l1     -4.25e+00   7.09e+00   -0.60  0.55130    
## pce.l1     -3.00e+01   6.73e+00   -4.45  3.0e-05 ***
## houst.l1   -2.85e+00   3.50e+00   -0.81  0.41781    
## atcgno.l1  -7.33e-02   4.90e-02   -1.49  0.13942    
## bopgstb.l1  7.38e-01   9.78e-02    7.55  1.1e-10 ***
## gdp.l2      5.09e-01   6.78e+00    0.08  0.94033    
## pce.l2      3.10e+01   6.73e+00    4.61  1.7e-05 ***
## houst.l2    1.01e+00   3.91e+00    0.26  0.79646    
## atcgno.l2   1.83e-01   4.93e-02    3.71  0.00041 ***
## bopgstb.l2  7.18e-02   9.67e-02    0.74  0.45974    
## const       2.56e+04   8.70e+03    2.94  0.00439 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 2870 on 72 degrees of freedom
## Multiple R-Squared: 0.981,   Adjusted R-squared: 0.978 
## F-statistic:  369 on 10 and 72 DF,  p-value: <2e-16 
## 
## 
## 
## Covariance matrix of residuals:
##              gdp    pce houst   atcgno   bopgstb
## gdp      3535.64   2448  2171    93577 -6.73e+00
## pce      2448.09   4172  2569    47103 -4.54e+04
## houst    2171.30   2569 10821    21586  3.27e+04
## atcgno  93576.88  47103 21586 33588697  5.13e+04
## bopgstb    -6.73 -45358 32702    51264  8.25e+06
## 
## Correlation matrix of residuals:
##               gdp    pce  houst  atcgno   bopgstb
## gdp      1.00e+00  0.637 0.3510 0.27154 -3.94e-05
## pce      6.37e-01  1.000 0.3823 0.12582 -2.45e-01
## houst    3.51e-01  0.382 1.0000 0.03581  1.09e-01
## atcgno   2.72e-01  0.126 0.0358 1.00000  3.08e-03
## bopgstb -3.94e-05 -0.245 0.1095 0.00308  1.00e+00

# str(var)
plot(var)

fcstUSeconomy <- forecast(var)

## Error: non-numeric argument to mathematical function

# plot(fcstUSeconomy)

The results as far as they got look like what I was expecting. There isn't much hint as to why 'forecast' gets the error though.

Gary Young

Economic link to stock market?

Proud Economy 0006

library(forecast)
library(quantmod)

“U.S. stocks slip, still on pace for weekly gain”

Being the last day of the quarter, the financial news are reporting disappointment.

“NEW YORK (MarketWatch) – U.S. stocks lost ground Friday, but still aimed for their first weekly gain in three weeks as investors digested Federal Reserve comments and mixed consumer-sentiment and business-conditions data.” by Victor Reklaitis, MarketWatch (emphasis mine)

“The data came after a report showed that the Chicago purchasing managers' index fell to 51.6 this month, from 58.7 in May, exceeding expectations for a decline to 56.0 but still remaining in expansion territory.” investing.com

napm_upd <- xts(51.6, as.Date("2013-06-01"))

“The Thomson Reuters/University of Michigan said today that its final index of confidence eased to 84.1 this month from 84.5 at the end of May, which was the highest since July 2007. The median forecast in a Bloomberg survey of economists called for 83 in the gauge after a preliminary reading of 82.7.” Bloomberg

So I'll attempt to work with the things that they are talking about, specifically the consumer-sentiment and business-conditions data if I can find them.

Extend and analyse data from FRED:

I've combined several of my prior ideas into the analyzets function.

analyzets <- function(series) {
    op <- par(no.readonly = TRUE)
    layout(matrix(c(1, 1, 2, 3), 2, 2, byrow = TRUE))
    plot(series, main = "series")
    acf(series, main = "Autocorrelations", ylab = "", ylim = c(-1, 1), ci.col = "black")
    pacf(series, main = "Partial Autocorrelations", ylab = "", ylim = c(-1, 
        1), ci.col = "black")
    par(op)
    return(summary(forecast(auto.arima(series), h = 2)))
}

University of Michigan: Consumer Sentiment (UMCSENT):

“The University of Michigan Consumer Sentiment Index Thomson Reuters/University of Michigan Surveys of Consumers is a consumer confidence index published monthly by the University of Michigan and Thomson Reuters. The index is normalized to have a value of 100 in December 1964. At least 500 telephone interviews are conducted each month of a continental United States sample (Alaska and Hawaii are excluded). Five core questions are asked. The consumer confidence measures were devised in the late 1940s by Professor George Katona at the University of Michigan. They have now developed into an ongoing, nationally representative survey based on telephonic household interviews. The Index of Consumer Sentiment (ICS) is developed from these interviews. The Index of Consumer Expectations (a sub-index of ICS) is included in the Leading Indicator Composite Index published by the U.S. Department of Commerce, Bureau of Economic Analysis.” wikipedia

Bloomberg has a page on the data at University of Michigan Survey of Consumer Confidence Sentiment.

FRED has the data, but holds back 6 months to allow it to go out to the proprietary subscribers.

“At the request of the source, the data is delayed by 6 months. … Copyright, 2011, Survey Research Center, Thomson Reuters/University of Michigan. Reprinted with permission.” FRED

The numbers have been published and can be displayed from Bloomberg's chart (CONSSENT:IND).

conssent_upd <- xts(c(XX.X, XX.X, XX.X, XX.X, XX.X, XX.X, XX.X, XX.X), order.by=seq(as.Date("2012-11-01"), by="1 month", length=8))

We'll get the older data directly from FRED.

getSymbols("UMCSENT", src = "FRED", env = .GlobalEnv)

## [1] "UMCSENT"

umcsent <- rbind(UMCSENT, conssent_upd)
analyzets(umcsent)

## 
## Forecast method: ARIMA(1,1,2)                   
## 
## Model Information:
## Series: series 
## ARIMA(1,1,2)                    
## 
## Coefficients:
##         ar1     ma1     ma2
##       0.556  -0.608  -0.101
## s.e.  0.190   0.192   0.060
## 
## sigma^2 estimated as 15.6:  log likelihood=-1187
## AIC=2382   AICc=2382   BIC=2398
## 
## Error measures:
##                     ME  RMSE   MAE    MPE  MAPE   MASE       ACF1
## Training set -0.005677 3.945 3.011 -17.96 372.9 0.9772 -2.077e-06
## 
## Forecasts:
##     Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 427          83.10 78.03 88.16 75.36 90.84
## 428          82.55 75.57 89.52 71.88 93.22

This forecast is for the next two months, July and August. It's really just a smoothing and extrapolation of the data with an automatic ARIMA model. I'll have to return to see how it did. The point here is to see if it's possible to do one's own analysis in response to the information that we see. I have a lot of learning to do to be able to build valid models that help give insight into the economy as we see it.

ISM Manufacturing: PMI Composite Index (NAPM)

“The Institute for Supply Management (ISM) is responsible for maintaining the Purchasing Managers Index (PMI), which is the headline indicator in the monthly ISM Report on Business. The ISM is a non-profit group boasting more than 40,000 members engaged in the supply management and purchasing professions. … The magic number for the PMI is 50. A reading of 50 or higher generally indicates that the industry is expanding. If manufacturing is expanding, the general economy should be doing likewise. As such, it is considered a good indicator of future GDP levels. Many economists will adjust their GDP estimates after reading the PMI report.” investopedia

getSymbols("NAPM", src = "FRED", env = .GlobalEnv)

## [1] "NAPM"

napm <- rbind(NAPM, napm_upd)
analyzets(napm)

## 
## Forecast method: ARIMA(3,0,0) with non-zero mean
## 
## Model Information:
## Series: series 
## ARIMA(3,0,0) with non-zero mean 
## 
## Coefficients:
##         ar1     ar2     ar3  intercept
##       1.125  -0.088  -0.126     52.738
## s.e.  0.035   0.054   0.036      1.015
## 
## sigma^2 estimated as 6.5:  log likelihood=-1852
## AIC=3714   AICc=3714   BIC=3737
## 
## Error measures:
##                      ME RMSE   MAE    MPE  MAPE   MASE      ACF1
## Training set -0.0004003 2.55 1.921 -26.18 377.3 0.9568 0.0004835
## 
## Forecasts:
##     Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 787          52.04 48.78 55.31 47.05 57.04
## 788          52.53 47.61 57.45 45.01 60.05

A current value of 51.6, and my extrapolations for the next two months at 52.04 and 52.53 would suggest that we're in for some positive times, but look at the forecast spread and the plot of what the series has done in the past. It's all over the map. Apparently the past values aren't a very good predictor for the future.

Gary Young