Index number input dataframe

The index number functions such as priceIndex, quantityIndex and GEKSIndex all take a dataframe as their first argument. This dataframe should contain everything needed to compute the index. In general this includes columns for,

• prices
• quantities
• a time period variable (more on this below)
• a product identifier that uniquely identifies each product.

One exception to the above is when elementary indexes are estimated using the priceIndex function. A quantity variable is not required in this case because the index is unweighted, and in many cases quantities may not be available (for example, when statistical agencies collect sample prices on individual products).

The dataframe must have column names, since character strings are used in other arguments to the index number functions to specify which columns contain the data listed above. Column names can be set with the colnames function of base R. The sample dataset CES_sigma_2 is an example of the minimum dataframe required to compute an index.

head(CES_sigma_2)

##   time prices quantities prodID
## 1    1   2.00  0.3846154      1
## 2    2   1.75  0.5846626      1
## 3    3   1.60  0.7135502      1
## 4    4   1.50  0.9149417      1
## 5    5   1.45  1.0280574      1
## 6    6   1.40  1.2058234      1

In this case, the dataframe is sorted by the product identifier prodID, but it need not be sorted at all.

The time period variable

To be able to compute indices, the data need to be subset in order to extract all observations on products for given periods. The approach used in IndexNumR is to require a time period variable as an input into many of its functions that will be used for subsetting. This time period variable must satisfy the following,

• start at 1
• increase in integer increments of 1
• continuous (that is, no gaps).

The variable may, and in fact likely will, have many observations for a given time period, since there are generally multiple items with price and quantity information. For example, the CES_sigma_2 dataset has observations on 4 products for each time period. We can see this by observing the first few rows of the dataset sorted by the time period.

head(CES_sigma_2[order(CES_sigma_2$time),])

##    time prices quantities prodID
## 1     1   2.00  0.3846154      1
## 13    1   1.00  1.5384615      2
## 25    1   1.00  1.5384615      3
## 37    1   0.50 12.3076923      4
## 2     2   1.75  0.5846626      1
## 14    2   0.50  7.1621164      2

The user can provide their own time variable, or if a date variable is available, IndexNumR has four functions that can compute the required time variable: yearIndex, quarterIndex, monthIndex and weekIndex. Users should be aware that if there are a very large number of observations then these functions can take some time to compute, but once it has been computed it is easier and faster to work with than dates.

Time aggregation

A related issue is that of aggregating data collected at some higher frequency, to a lower frequency. When computing index numbers, this is often done by computing a unit value as follows, That is, sum up total expenditure on each item over the required period, and divide by the total quantity. Provided that a time period variable as described above is available, the unit values can be computed using the function unitValues. This function returns the unit values, along with the aggregate quantities for each time period and each product. The output will also include the product identifier and time period variable so the output dataframe from the unitvalues function contains everything needed to compute an index number.

Carry forward/backward prices

An alternative to using a matched sample of products is to impute the missing data. One technique for doing this is to replace missing values with the last actual price observation. If the data has both prices and quantities then the corresponding quantities are set to zero. If the missing observations occur at the beginning of the time series then the first actual observation is carried backward to the first time period. IndexNumR performs this carry price imputation with the imputeCarryPrices function; however, this is only needed if the imputed data themselves are of interest. Otherwise, the price index functions can use carry price imputation by setting the parameter imputePrices = "carry".

In the example below, the first two observations on product 1 are missing, so the price from the third period is carried backwards to fill the missing observations. Observations 3 and 4 are missing on product 2, so the price in period 2 is carried forward to fill them. The corresponding quantities are set to zero.

# create a dataset with some missing observations on product 1 and 2
df <- CES_sigma_2[-c(1,2,15,16),]
df <- df[df$prodID %in% 1:2 & df$time <= 6,]

dfMissing <- df[, c("time", "prices", "prodID")] %>%
  tidyr::pivot_wider(id_cols = time, names_from = prodID, values_from = prices)
dfMissing[order(dfMissing$time),]

## # A tibble: 6 × 3
##    time   `1`   `2`
##   <int> <dbl> <dbl>
## 1     1 NA     1   
## 2     2 NA     0.5 
## 3     3  1.6  NA   
## 4     4  1.5  NA   
## 5     5  1.45  1.12
## 6     6  1.4   1.15

# compute carry prices
carryPrices <- imputeCarryPrices(df, pvar = "prices", qvar = "quantities", 
                                 pervar = "time", prodID = "prodID")

# print the data with the product prices in columns to see the filled data
carryPrices[, c("time", "prices", "prodID")] %>%
  tidyr::pivot_wider(id_cols = time, names_from = prodID, values_from = prices)

## # A tibble: 6 × 3
##    time   `1`   `2`
##   <dbl> <dbl> <dbl>
## 1     1  1.6   1   
## 2     2  1.6   0.5 
## 3     3  1.6   0.5 
## 4     4  1.5   0.5 
## 5     5  1.45  1.12
## 6     6  1.4   1.15

# print the data with the product quantities in columns to see the corresponding zeros
carryPrices[, c("time", "quantities", "prodID")] %>%
  tidyr::pivot_wider(id_cols = time, names_from = prodID, values_from = quantities)

## # A tibble: 6 × 3
##    time   `1`   `2`
##   <dbl> <dbl> <dbl>
## 1     1 0      1.54
## 2     2 0      7.16
## 3     3 0.714  0   
## 4     4 0.915  0   
## 5     5 1.03   1.72
## 6     6 1.21   1.79

Time dummy methods

• Time-product-dummy

This is a regression model approach where log prices are modelled as a function of time and product dummies. The regression equation is given by,

where D^t is equal to 1 in period t and 0 in period t − 1, and D_n is equal to 1 if the product is product n and 0 otherwise.

The price index is then given by,

However, this is a biased estimate (Kennedy 1981), so IndexNumR optionally calculates the following adjusted estimate,

The time-product-dummy equation can be estimated using three methods in IndexNumR using the weights parameter: ordinary least squares; weighted least squares where the weights are the product expenditure shares; or weighted least squares where the weights are the average of the expenditure shares in the two periods. In the first case, the index produced is the same as the matched sample Jevons index, which does not use quantity information. The second option produces a matched sample harmonic share weights index, and the last option produces the matched sample Tornqvist index. See (Walter E. Diewert 2005b) for a discussion of these results.

Examples

To estimate a simple chained Laspeyres price index,

priceIndex(CES_sigma_2,
           pvar = "prices",
           qvar = "quantities",
           pervar = "time",
           prodID = "prodID", 
           indexMethod = "laspeyres", 
           output = "chained")

##            [,1]
##  [1,] 1.0000000
##  [2,] 0.9673077
##  [3,] 1.2905504
##  [4,] 1.3382002
##  [5,] 1.2482444
##  [6,] 1.7346552
##  [7,] 1.6530619
##  [8,] 1.4524186
##  [9,] 1.8386215
## [10,] 1.7126802
## [11,] 2.1810170
## [12,] 2.2000474

Estimating multiple different index numbers on the same data is straight-forward,

methods <- c("laspeyres","paasche","fisher","tornqvist")
prices <- lapply(methods, 
                 function(x) {priceIndex(CES_sigma_2,
                                         pvar = "prices", 
                                         qvar = "quantities", 
                                         pervar = "time", 
                                         prodID = "prodID", 
                                         indexMethod = x, 
                                         output = "chained")})

as.data.frame(prices, col.names = methods)

##    laspeyres   paasche    fisher tornqvist
## 1  1.0000000 1.0000000 1.0000000 1.0000000
## 2  0.9673077 0.8007632 0.8801048 0.8925715
## 3  1.2905504 0.8987146 1.0769571 1.0789612
## 4  1.3382002 0.9247902 1.1124543 1.1146080
## 5  1.2482444 0.6715974 0.9155969 0.9327861
## 6  1.7346552 0.7858912 1.1675831 1.1790710
## 7  1.6530619 0.7472454 1.1114148 1.1223220
## 8  1.4524186 0.5836022 0.9206708 0.9379711
## 9  1.8386215 0.6431381 1.0874224 1.0961295
## 10 1.7126802 0.5145138 0.9387213 0.9527309
## 11 2.1810170 0.5736947 1.1185875 1.1288419
## 12 2.2000474 0.5745408 1.1242851 1.1346166

This illustrates the Laspeyres index’s substantial positive bias, the Paasche index’s substantial negative bias, and the similar estimates produced by the Fisher and Tornqvist superlative index numbers.

Dissimilarity measures

To choose the linking period we need a measure of the similarity between two periods. For each period we have information on prices and quantities. The Hill (2001) method compares the two periods based on the Paasche-Laspeyres spread,

where P^L is a Laspeyres price index and P^P is a Paasche price index. Since the Laspeyres and Paasche indices are biased in opposite directions, this choice of similarity measure is designed to choose linking periods that minimise the influence of index number method choice.

Alternative measures exist that compute the dissimilarity of two vectors. Two such measures, recommended by Diewert (Walter E. Diewert 2002) are the weighted log-quadratic index of relative price dissimilarity and the weighted asymptotically linear index of relative price dissimilarity, given by the following, where P(p^t, p^T + 1, q^t, q^T + 1) is a superlative index number.

Another measure proposed by Fox, Hill and Diewert (Fox, Hill, and Diewert 2004) is a measure of absolute dissimilarity given by,

where M + N is the total number of items in the vector and x_j and x_k are the two vectors being compared. The authors use this in the context of detecting outliers, but it can be used to compare the price and quantity vectors of two time periods. One way to do this is to only use price information, or only use quantity information. There are two ways to use both price and quantity information: stack the price and quantity vectors for each time period into a single vector and compare the two `stacked’ vectors; or calculate separate measures of absolute dissimilarity for prices and quantities before combining these into a single measure. The former method is simple to implement, but augments the price vector with a quantity vector that may be of considerably different magnitude and variance. Another option is to compute the absolute dissimilarity using prices and quantities separately, then combine them by taking the geometric average.

The final measure is the predicted share measure of relative price dissimilarity employed by Diewert, Finkel, Sayag and White in the Seasonal Products chapter of the Update of the Consumer Price Index Manual, Consumer Price Index Theory (draft available here). To introduce this measure, first we define some notation.

The share of expenditure on product n in period t is given by $s_{t,n} = p_{t,n}q_{t,n}/ \sum_{i=1}^{K}p_{t,i}q_{t,i}$. The ‘predicted’ share of expenditure on product n in period t, using the quantities of period t and the prices of period r is given by $s_{r,t,n} = p_{r,n}q_{t,n}/ \sum_{i=1}^{K}p_{r,i}q_{t,i}$. We also define the predicted share error e_r, t, n as the actual share, minus the predicted share s_t, n − s_r, t, n.

The predicted share measure of relative price dissimilarity between the periods t and r is given by:

When the dataset being used does not have quantities, and an elementary index is being constructed, we cannot compute the shares in the above formulas. In this case, quantities are imputed in such a way that the expenditure shares on each product available in a time period are equal. The quantities are constructed by setting quantity equal to 1/P_n, t × N_t where N_t is the number of products available in period t. IndexNumR does this with the function imputeQuantities; however price indexes can be estimated without calling this function directly. Calling priceIndex and setting qvar = "" will trigger IndexNumR to impute the quantities used in the estimation of the predicted share relative price dissimilarity measure.

Estimating similarity-linked indexes

IndexNumR provides three functions, enabling the estimation of the dissimilarity measures above. The first function relativeDissimilarity calculates the Paasche-Laspeyres spread, log-quadratic, asymptotically linear and predicted share relative dissimilarity measures, and the second function mixScaleDissimilarity computes the mix, scale and absolute measures of dissimilarity. All three functions provide the same output - a data frame with three columns containing the indices of the pairs of periods being compared in the first two columns and the value of the dissimilarity measure in the third column.

Once these have been computed, the function maximiumSimilarityLinks can take the output data frame from these functions and compute the maximum similarity linking periods. The function priceIndex effectively computes a similarity-linked index as follows,

• Compute the measure of dissimilarity between all possible combinations of time periods.
• Set the price index to 1 in the first period.
• Compute the price index for the second period and chain it with the first period, where P(p¹, p², q¹, q²) is any bilateral index number formula.
• For each period t from 3, …, T, find the period t^min with the minimum dissimilarity, comparing period t to all periods 1, …, t − 1.
• Compute the similarity chain-linked index number,

Examples

Using the log-quadratic measure of relative dissimilarity, the dissimilarity between the periods in the CES_sigma_2 dataset is as follows,

lq <- relativeDissimilarity(CES_sigma_2, 
                            pvar="prices", 
                            qvar="quantities", 
                            pervar = "time", 
                            prodID = "prodID", 
                            indexMethod = "fisher", 
                            similarityMethod = "logquadratic")

head(lq)

##   period_i period_j dissimilarity
## 1        1        2    0.09726451
## 2        1        3    0.02037395
## 3        1        4    0.04164311
## 4        1        5    0.28078294
## 5        1        6    0.08880177
## 6        1        7    0.08531212

The output from estimating the dissimilarity between periods can than be used to estimate the maximum similarity links,

maximumSimilarityLinks(lq)

##    xt x0 dissimilarity
## 1   1  1   0.000000000
## 2   2  1   0.097264508
## 3   3  1   0.020373951
## 4   4  3   0.003832972
## 5   5  4   0.130990853
## 6   6  4   0.008684012
## 7   7  6   0.001122913
## 8   8  2   0.041022738
## 9   9  7   0.001367896
## 10 10  5   0.006962106
## 11 11  9   0.002946674
## 12 12 11   0.003612044

To estimate a chained Laspeyres index linking together the periods with maximum similarity as estimated above,

priceIndex(CES_sigma_2,
           pvar = "prices",
           qvar = "quantities",
           pervar = "time",
           prodID = "prodID", 
           indexMethod = "laspeyres", 
           output = "chained", 
           chainMethod = "logquadratic")

##            [,1]
##  [1,] 1.0000000
##  [2,] 0.9673077
##  [3,] 1.1000000
##  [4,] 1.1406143
##  [5,] 1.0639405
##  [6,] 1.2190887
##  [7,] 1.1617463
##  [8,] 1.0551558
##  [9,] 1.1357327
## [10,] 1.0928877
## [11,] 1.1732711
## [12,] 1.1835084

The GEKS method

The GEKS method is attributable to Gini (Gini 1931), Eltito and Koves (Eltetö and Köves 1964), and Szulc (Szulc 1964) in the cross-sectional context. The idea of adapting the method to the time series context is due to Balk (Balk 1981), and developed further by Ivancic, Diewert and Fox (Ivancic, Diewert, and Fox 2011).

The user must choose the size of the window over which to apply the GEKS method, typically one or two years of data plus one period to account for seasonality. Denote this as w.The basic method followed by the function GEKSIndex is as follows. Choose a period, denoted period k, within the window as the base period. Calculate a bilateral index number between period k and every other period in the window. Repeat this for all possible choices of k. This gives a matrix of size w × w of bilateral indexes between all possible pairs of periods within the window. Then compute the GEKS indexes for the first w periods as, where the term P^k, t is the bilateral index between period t and base period k. IndexNumR offers the Fisher, Tornqvist, Walsh, Jevons and time-product-dummy index number methods for the index P via the indexMethod option. The Tornqvist index method is the default. The w × w matrix of bilateral indexes is as follows, $$P = \begin{pmatrix} P^{1,1} & \cdots & P^{1,w} \\ \vdots & \ddots & \vdots \\ P^{w,1} & \cdots & P^{w,w} \end{pmatrix} $$ So that the first term of the GEKS index is the geometric mean of the elements in the first column of the above matrix, the second term is the geometric mean of the second column, and so on. Note that IndexNumR makes use of two facts about the matrix above to speed up computation: it is (inversely) symmetric so that P^j, k = 1/P^k, j; and the diagonal elements are 1.

The Geary-Khamis method

The Geary-Khamis, or GK method, was introduced by Geary (Geary 1958) and extended by Khamis (Khamis 1970, 1972). This method involves calculating a set of quality adjustment factors, b_n, simultaneously with the price levels, P_t. The two equations that determine both of these are:

These equations can be solved by an iterative method, where a set of b_n are arbitrarily chosen, which can then be used to calculate an initial vector of price levels. This vector of prices is then used to generate a new b vector, and so on until the changes become smaller than some threshold. IndexNumR can use the iterative method by specifying the parameter solveMethod = "iterative". However, there is an alternative method using matrix algebra that is significantly more efficient. To use the more efficient method discussed below, specify solveMethod = "inverse".

As discussed in (W. Erwin Diewert and Fox 2017) and following Diewert (Walter E. Diewert 1999), the problem of finding the b vector can be solved using the following system of equations:

where I_N is the N × N identity matrix, 0_N is an n-dimensional vector of zeros and the C matrix is given by,

where q̂⁻¹ is the inverse of an N × N diagonal matrix q, where the diagonal elements are the total quantities purchased for each good over all time periods, s^t is a vector of the expenditure shares for time period t, and q^tT is the transpose of the vector of quantities purchased in time period t. It can be shown that the matrix [I − C] is singular so a normalisation is required to solve for b. IndexNumR follows the method discussed by Irwin Collier Jr. in his comment on (Walter E. Diewert 1999) and assumes the following normalisation,

which is, in matrix form, where c is the N × 1 vector $\begin{bmatrix} 1 & 0 & \dots & 0 \end{bmatrix}^{\textbf{T}}$,

and R is the N × N matrix,

Adding the constraint to the original equation we now have the solution for b,

Once the b vector has been calculated, the price levels can be computed from the GK equations above.

The Weighted Time-Product-Dummy method

The weighed time-product-dummy method can be seen as the country-product-dummy method (Summers 1973) adapted to the time-series context and supposes the following model for prices:

where α_t can be interpreted as the price level in period t, b_n is the quality adjustment factor for product n and e_tn is a stochastic error term.

The problem is to solve for α and b using least squares minimisation. Following (Rao 1995), it is formulated as a weighted least squares minimisation, where the weights are based on economic importance. Diewert and Fox show that this can be written as the solution to the system of equations,

where I_N is the N × N identity matrix,

F is the following N × N matrix,

the elements of F are the following,

with the w_nj given by,

and the w_tnj given by,

f on the right-hand-side is the following,

where the f_n are given by,

and y_tn = log(p_tn).

The matrix [I_N − F] is singular so a normalisation must be used to solve the system of equations. IndexNumR uses the method discussed in (W. Erwin Diewert and Fox 2017); β_N is assumed to be zero and the last equation is dropped to solve for the remaining coefficients.

Extending multilateral indexes

The multilateral indexes are normalised by dividing by the first term, to give an index for the first w periods that starts at 1. If the index only covers w periods then no further calculation is required. However, if there are T > w periods in the dataset then the index must be extended.

Extending a multilateral index can be done in a multitude of ways. Statistical agencies generally do not revise price indices like the consumer price index, so the methods offered by IndexNumR to extend multilateral indexes are methods that do not lead to revisions. More specifically, these are called splicing methods and the options available are the movement, window, half, mean, fbew (Fixed Base Expanding Window), fbmw (Fixed Base Moving Window), wisp (window splice on published data), hasp (half-splice on published data) and mean splice on published data. The idea behind most of these methods is that we start by moving the window forward by one period and calculate the index for the new window. There will be w − 1 overlapping periods between the initial index and the index computed on the window that has been rolled forward one period. Any one of these overlapping periods can be used to extend the multilateral index. The variants of window, half and mean splice that are on published data use the same method as the classical counterparts, but splice onto the published series instead of the previously calculated window.

Let P_OLD be the index computed over periods 1 to w and let P_NEW be the index computed over the window rolled forward one period, from periods 2 to w + 1. Let the final index simply be P. For the first w periods P = P_OLD, then P^w + 1 is computed using the splicing methods as follows.

• Movement splice (Ivancic, Diewert, and Fox 2011) That is, the movement between the final two periods of the index computed over the new window is used to extend the original index from period w to w + 1.

• Window splice (Krsinich 2016) In this case, the ratio of the movement between the first and last periods computed using the new window, to the movement between the first and last periods using the old window is used to extend the original index.

• Half splice The half splice uses the period in the middle of the window as the overlapping period to calculate the splice.

• Mean splice (Ivancic, Diewert, and Fox 2011) The mean splice uses the geometric mean of the movements between the last period and every other period in the window to extend the original index.

• FBMW (Claude Lamboray 2017) This method uses a fixed base period that is updated periodically. For example, if the data are monthly then the base period could be each December month, which would be achieved by ensuring that December is the first period in the data and specifying a window length of 13. The splice is calculated by using the movement between the final data point and the base period in the new window to extend the index. If the new data point being calculated is the first period after the base period, then this method produces the same price growth the same as the movement splice. Using the same example, if each December is the base period, then this method will produce the same price growth for January on December as the movement splice.

• FBEW (Chessa 2016)

  This method uses the same calculation as FBMW, but uses a different set of data for the calculation. It expands the size of the window used to compute the new data point each period to include the latest period of data. If the data are monthly and the base period is each December, then the window used to compute the new data point in January includes only the December and January months. In February it includes the December, January and February months, and so on until the next December where it includes the full 13 months (assuming a window length of 13). This method produces the same result as the FBMW method when the new period being calculated is the base period. Using the same example, if each December is the base period, then each December this will produce the same result as the FBMW method.

The splicing methods are used in this fashion to extend the series up to the final period in the data.

# Assume that the data in CES_sigma_2 are quarterly data with time period
# 1 corresponding to the December quarter. 

splices <- c("window", "half", "movement", "mean", "fbew", "fbmw", "wisp", "hasp", "mean_pub")

# estimate a GEKS index using the different splicing methods. Under
# the above assumptions, the window must be 5 to ensure the base period is
# each December quarter.
result <- as.data.frame(lapply(splices, function(x){
  GEKSIndex(CES_sigma_2, 
          pvar = "prices", 
          qvar = "quantities", 
          pervar = "time", 
          prodID = "prodID", 
          indexMethod = "tornqvist", 
          window=5, 
          splice = x)
}))

colnames(result) <- splices
result

##       window      half  movement      mean      fbew      fbmw      wisp
## 1  1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 2  0.8927770 0.8927770 0.8927770 0.8927770 0.8927770 0.8927770 0.8927770
## 3  1.0781723 1.0781723 1.0781723 1.0781723 1.0781723 1.0781723 1.0781723
## 4  1.1132724 1.1132724 1.1132724 1.1132724 1.1132724 1.1132724 1.1132724
## 5  0.9292537 0.9292537 0.9292537 0.9292537 0.9292537 0.9292537 0.9292537
## 6  1.1784816 1.1789102 1.1772392 1.1783996 1.1746060 1.1772392 1.1784816
## 7  1.1221118 1.1207012 1.1205204 1.1214679 1.1179392 1.1191128 1.1225753
## 8  0.9383833 0.9371778 0.9368880 0.9376351 0.9348518 0.9352286 0.9386945
## 9  1.0951022 1.0942446 1.0941491 1.0947038 1.0914207 1.0914207 1.0914207
## 10 0.9515914 0.9510825 0.9507333 0.9512963 0.9486380 0.9483625 0.9517028
## 11 1.1280620 1.1274679 1.1268628 1.1277415 1.1241728 1.1242435 1.1288942
## 12 1.1336566 1.1327009 1.1323917 1.1332152 1.1297415 1.1297611 1.1354276
##         hasp  mean_pub
## 1  1.0000000 1.0000000
## 2  0.8927770 0.8927770
## 3  1.0781723 1.0781723
## 4  1.1132724 1.1132724
## 5  0.9292537 0.9292537
## 6  1.1789102 1.1783996
## 7  1.1191128 1.1214484
## 8  0.9383566 0.9373834
## 9  1.0925320 1.0940276
## 10 0.9524902 0.9512759
## 11 1.1253883 1.1276104
## 12 1.1341850 1.1330457

On the assumptions in the above example, periods 1, 5 and 9 are Decembers. Periods 1-5 are computed using full information and periods 6-12 are computed using the splicing methods. Notice that fbew = fbmw in period 9 (December) and fbmw = movement in period 6 (January).

Examples

Price indicators for the CES_sigma_2 dataset are as follows:

methods <- c("laspeyres", "paasche", "bennet", "montgomery")

p <- lapply(methods, function(x) {priceIndicator(CES_sigma_2, 
                                                 pvar = "prices", 
                                                 qvar = "quantities", 
                                                 pervar = "time", 
                                                 prodID = "prodID", 
                                                 method = x)})

as.data.frame(p, col.names = methods)

##     laspeyres     paasche      bennet  montgomery
## 1          NA          NA          NA          NA
## 2  -0.3269231 -3.23451167 -1.78071737 -1.27874802
## 3   4.3441768  1.19889566  2.77153621  2.23764163
## 4   0.4061429  0.33835480  0.37224887  0.37329461
## 5  -0.8066580 -5.65501233 -3.23083515 -2.35138599
## 6   5.8451382  1.89061744  3.86787782  3.23912451
## 7  -0.6114830 -0.72404798 -0.66776546 -0.66571059
## 8  -1.6992746 -4.76683536 -3.23305498 -2.74535253
## 9   4.5203554  1.38856453  2.95445995  2.45168559
## 10 -1.0274652 -4.49985294 -2.76365909 -2.28761791
## 11  4.9221471  1.65051935  3.28633320  2.85483403
## 12  0.1396069  0.02503502  0.08232098  0.08391295

Quantity indicators can also be produced using the same methods as outlined above via the quantityIndicator function. This allows for the value change from one period to the next to be decomposed into price and quantity movements. To facilitate this, IndexNumR contains the valueDecomposition function, which can be used as follows to produce a decomposition of the value change for CES_sigma_2 using a Bennet indicator:

valueDecomposition(CES_sigma_2, 
                   pvar = "prices",  
                   qvar = "quantities",  
                   pervar = "time",  
                   prodID = "prodID",  
                   priceMethod = "bennet")

##          price   quantity changes values
## 1           NA         NA      NA     NA
## 2  -1.78071737  4.7807174       3     13
## 3   2.77153621 -4.7715362      -2     11
## 4   0.37224887  0.6277511       1     12
## 5  -3.23083515  6.2308351       3     15
## 6   3.86787782 -5.8678778      -2     13
## 7  -0.66776546  1.6677655       1     14
## 8  -3.23305498  6.2330550       3     17
## 9   2.95445995 -4.9544600      -2     15
## 10 -2.76365909  5.7636591       3     18
## 11  3.28633320 -5.2863332      -2     16
## 12  0.08232098  0.9176790       1     17

Note that for this decomposition, the method is specified for the price indicator and IndexNumR uses the appropriate quantity indicator. For Bennet and Montgomery indicators the same method is used for the quantity indicator as for the price indicator. If a Laspeyres price indicator is requested then the corresponding volume indicator is a Paasche indicator. The reverse is true if the Paasche indicator is used for prices.