Calculate direct index according to the Fisher hedonic double imputation method

Description

By the parameters 'dependent_variable', 'continue_variable' and 'categorical_variables' as regression model is compiled. With the model, a direct series of index figures is estimated by use of hedonic regression.

Usage

calculate_fisher(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  number_of_observations = FALSE
)

Arguments

Details

N.B.: the independent variables must be entered transformed (and ready) in the parameters. Hence, not: log(floor_area), but transform the variable in advance and then provide log_floor_area. This does not count for the dependent variable. This should be entered untransformed

Within the data, it is not neccesary to filter the data on relevant variables or complete records. This is taken care of in the function.

Value

table with index, imputation averages, number of observations and confidence intervals per period

Author(s)

Farley Ishaak

Calculate the geometric average of a series of values

Description

The equation for the calculation is:: exp(mean(log(series_values)))

Usage

calculate_geometric_average(values)

Arguments

Value

geometric average

Author(s)

Farley Ishaak

Calculate imputation averages with the 1st period as base period

Description

Prices are estimated based on a provided Hedonic model The model values are calculated for each period in the data With these values, new prices of base period observations are estimated. With this function, imputations according to the Laspeyres and Paasche method can be estimated.

Usage

calculate_hedonic_imputation(
  dataset_temp = dataset,
  period_temp = "period_var_temp",
  dependent_variable_temp = dependent_variable,
  independent_variables_temp = independent_variables,
  number_of_observations_temp = number_of_observations,
  period_list_temp = period_list
)

Arguments

Value

Table with imputation averages per period

Author(s)

Farley Ishaak

Calculate a matrix with hedonic imputation averages, re-estimated time series imputation averages and corresponding index series.

Description

Based on a hedonic model, a series of imputed values is calculated in below steps: 1: for every period average imputed prices are estimated with the 1st period as base period. 2: the above is repeated for each possible base period. This result in an equal number of series as the number of periods. 3: All series are re-estimated with a time series model (state space). This step is optionally skipped with a parameter (state_space_model = NULL) 4: the series imputed values are transformed into index series. This matrix can be used for an index calculations according to the HMTS method.

Usage

calculate_hedonic_imputationmatrix(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  periods_in_year,
  number_of_observations = TRUE,
  production_since = NULL,
  number_preliminary_periods
)

Arguments

Details

Parameter 'production_since': To simulate a series, where 1 period a time expires (as in production), a manual choice in the past is possible. Until this period, all periods are imputed. After that, 1 period is added.

Value

$Matrix_HMTS_index table with index series based on estimations with time series re-estimations $Matrix_HMTS table with estimated values based on time series re-estimations $Matrix_HMS_index table with index series based on estimations with the hedonic model $Matrix_HMS table with estimated values based on the hedonic model $Matrix_HMTS_analysis table with analysis values of the time series model per base period

Author(s)

Farley Ishaak

Calculate HMTS index (Hedonic Multilateral Time series re-estimation Splicing)

Description

Based on a hedonic model, an index is calculated in below steps. See also Ishaak, Ouwehand, Remoy & De Haan (2023). 1: for each period, average imputed prices are calculated with the first period as base period. 2: step 1 is repeated for every possible base period. This result in as many series of imputed values as the number of periods. 3: All series with imputed prices are re-estimated with a Kalman filter (also time series model/state space model) This step can be turned off with a parameter. 4: The series of imputed values are transformed into index series. 5: a specified (parameter) window is chosen of index figures that continues in the calculation. This step can be turned off with a parameter. 6: Of the remaining index figures, the geometric average per period is calculated. The remaining index figures form the final index.

Usage

calculate_hmts(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period,
  periods_in_year,
  production_since = NULL,
  number_preliminary_periods,
  number_of_observations,
  resting_points
)

Arguments

Details

Parameter 'production_since': To simulate a series, where 1 period a time expires (as in production), a manual choice in the past is possible. Until this period, all periods are imputed. After that, 1 period is added.

Parameter 'resting_points': If TRUE, the output is a list of tables. These tables can be called with a $ after the output. $Index table with periods, index and number of observations $Window table with the index figures within the chosen window $Chosen_index_series table with index series before the window splice $Matrix_HMTS_index table with index series based on re-estimated imputations (time series model) $Matrix_HMTS table with re-estimated imputations (time series model) $Matrix_HMTS_index table with index series based on estimated imputations (hedonic model) $Matrix_HMTS table with estimated imputations (time series model) $Matrix_HMTS_analyse table with diagnostic values of the time series model per base period

Value

$Matrix_HMTS_index table with index series based on estimations with time series re-estimations $Matrix_HMTS table with estimated values based on time series re-estimations $Matrix_HMS_index table with index series based on estimations with the hedonic model $Matrix_HMS table with estimated values based on the hedonic model $Matrix_HMTS_analysis table with analysis values of the time series model per base period

table with periods, index (and optional confidence intervals) and number of observations. If resting_points = TRUE, then list with tables. See general description and examples.

Author(s)

Farley Ishaak

Calculate HMTS index only (Hedonic Multilateral Time series re-estimation Splicing)

Description

Based on a hedonic model, an index is calculated in below steps. See also Ishaak, Ouwehand, Remoy & De Haan (2023). 1: for each period, average imputed prices are calculated with the first period as base period. 2: step 1 is repeated for every possible base period. This result in as many series of imputed values as the number of periods. 3: All series with imputed prices are re-estimated with a Kalman filter (also time series model/state space model) This step can be turned off with a parameter. 4: The series of imputed values are transformed into index series. 5: a specified (parameter) window is chosen of index figures that continues in the calculation. This step can be turned off with a parameter. 6: Of the remaining index figures, the geometric average per period is calculated. The remaining index figures form the final index.

Usage

calculate_hmts_index(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period,
  periods_in_year,
  production_since = NULL,
  number_preliminary_periods,
  number_of_observations = NULL,
  resting_points
)

Arguments

Details

Parameter 'production_since': To simulate a series, where 1 period a time expires (as in production), a manual choice in the past is possible. Until this period, all periods are imputed. After that, 1 period is added.

Parameter 'resting_points': If TRUE, the output is a list of tables. These tables can be called with a $ after the output. $Index table with periods, index and number of observations $Window table with the index figures within the chosen window $Chosen_index_series table with index series before the window splice $Matrix_HMTS_index table with index series based on re-estimated imputations (time series model) $Matrix_HMTS table with re-estimated imputations (time series model) $Matrix_HMTS_index table with index series based on estimated imputations (hedonic model) $Matrix_HMTS table with estimated imputations (time series model)l $Matrix_HMTS_analyse table with diagnostic values of the time series model per base period

Value

$Matrix_HMTS_index table with index series based on estimations with time series re-estimations $Matrix_HMTS table with estimated values based on time series re-estimations $Matrix_HMS_index table with index series based on estimations with the hedonic model $Matrix_HMS table with estimated values based on the hedonic model $Matrix_HMTS_analysis table with analysis values of the time series model per base period

table with periods, index and number of observations. If resting_points = TRUE, then list with tables. See general description and examples.

Author(s)

Farley Ishaak

Transform series into index

Description

The index can be calculated in two ways:

• from a series of values

• from a series of mutations (from_growth_rate = TRUE)

Usage

calculate_index(periods, values, reference_period = NULL)

Arguments

Details

N.B. with from_growth_rate: The series of mutations must be equally long to the series of values. The vector should, therefore, also contain a mutation for the first period (this is likely 1). In the calculation, this first mutation is not used.

N.B. for the reference period: The first value is on default set to 100. An adjusted reference period can be provided in the paramater. The reference period can also be a part of a period. E.g. if the series contains months (2019jan, 2019feb), the reference period can be a year (2019).

Value

Index series

Author(s)

Farley Ishaak

Calculate direct index according to the Laspeyres hedonic double imputation method

Description

By the parameters 'dependent_variable', 'continue_variable' and 'categorical_variables' as regression model is compiled. With the model, a direct series of index figures is estimated by use of hedonic regression.

Usage

calculate_laspeyres(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  index = TRUE,
  number_of_observations = FALSE,
  imputation = FALSE
)

Arguments

Details

N.B.: the independent variables must be entered transformed (and ready) in the parameters. Hence, not: log(floor_area), but transform the variable in advance and then provide log_floor_area. This does not count for the dependent variable. This should be entered untransformed/

Within the data, it is not necessary to filter the data on relevant variables or complete records. This is taken care of in the function.

Value

table with index, imputation averages, number of observations and confidence intervals per period

Author(s)

Farley Ishaak

Calculate direct index according to the Paasche hedonic double imputation method

Description

By the parameters 'dependent_variable', 'continue_variable' and 'categorical_variables' as regression model is compiled. With the model, a direct series of index figures is estimated by use of hedonic regression.

Usage

calculate_paasche(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  index = TRUE,
  number_of_observations = FALSE,
  imputation = FALSE
)

Arguments

Details

N.B.: the independent variables must be entered transformed (and ready) in the parameters. Hence, not: log(floor_area), but transform the variable in advance and then provide log_floor_area. This does not count for the dependent variable. This should be entered untransformed

Within the data, it is not necessary to filter the data on relevant variables or complete records. This is taken care of in the function.

Value

table with index, imputation averages, number of observations and confidence intervals per period

Author(s)

Farley Ishaak

Calculate index based on specified method (Fisher, Laspeyres, Paasche, HMTS)

Description

Central hub function to calculate index figures using different methods.

Usage

calculate_price_index(
  method,
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables,
  reference_period = NULL,
  number_of_observations = TRUE,
  periods_in_year = 4,
  production_since = NULL,
  number_preliminary_periods = 3,
  resting_points = FALSE,
  index = TRUE,
  imputation = FALSE
)

Arguments

Value

A data.frame (or list for when method is HMTS with resting_points = TRUE)

Author(s)

Vivek Gajadhar

Examples

# Laspeyres index
Tbl_Laspeyres <- calculate_price_index(
  method = "laspeyres",
  dataset = data_constraxion,
  period_variable = "period",
  dependent_variable = "price",
  continuous_variables = "floor_area",
  categorical_variables = "neighbourhood_code",
  reference_period = 2015,
  number_of_observations = TRUE,
  imputation = FALSE
)
head(Tbl_Laspeyres)

# Paasche index
Tbl_Paasche <- calculate_price_index(
  method = "paasche",
  dataset = data_constraxion,
  period_variable = "period",
  dependent_variable = "price",
  continuous_variables = "floor_area",
  categorical_variables = "neighbourhood_code",
  reference_period = 2015,
  number_of_observations = TRUE,
  imputation = FALSE
)
head(Tbl_Paasche)

# Fisher index (geometric mean of Laspeyres and Paasche)
Tbl_Fisher <- calculate_price_index(
  method = "fisher",
  dataset = data_constraxion,
  period_variable = "period",
  dependent_variable = "price",
  continuous_variables = "floor_area",
  categorical_variables = "neighbourhood_code",
  reference_period = 2015,
  number_of_observations = TRUE
)
head(Tbl_Fisher)

Calculate the trend line for a provided time series of numeric values

Description

Calculate the trend line with the state space method for a provided time series (chronological order is assumed). The series are calculated with the package KFAS.

Usage

calculate_trend_line_kfas(original_series, periodicity, resting_points)

Arguments

Value

Trend line

Author(s)

Pim Ouwehand, Farley Ishaak

Default update function

Description

This function is used in the function: calculate_trend_line_KFAS()

Usage

custom_update_function(params, model)

Arguments

Value

Newmodel

Author(s)

Vivek Gajadhar

A real estate example dataframe

Description

A subset of data from a fictitious real estate data frame containing transaction prices and some categorical and numerical characteristics of each dwelling.

Usage

data_constraxion

Format

A data frame with 7,800 rows and 6 columns:

period

A (string) vector indicating a time period

price

A (string) vector indicating the transaction price of the dwelling

floor_area

A real-valued vector of (the logarithm of) the floor area of the dwelling

dist_trainstation

A real-valued vector of (the logarithm of) the distance of the dwelling to the nearest train station

neighbourhood_code

A categorical code/string referring to the neighbourhood the dwelling belongs to

dummy_large_city

A vector indicating whether the dwelling belongs to a large city or not

Source

A fictitious dataset for illustration purposes

Examples

data(data_constraxion)
head(data_constraxion)

Determine_initial_parameters

Description

Determine startvalues within state space models This function is used in the function: calculate_trend_line_KFAS()

Usage

determine_initial_parameters(
  model,
  initial_values,
  FUN = custom_update_function
)

Arguments

Value

New initial startvalues

Author(s)

Pim Ouwehand, Farley Ishaak

Estimate time series parameters

Description

Estimate parameters to estimate trend lines This function is used in the function: calculate_trend_line_KFAS()#'

Usage

estimate_ts_parameters(model, initial_values)

Arguments

Value

Parameter for the time series model

Author(s)

Pim Ouwehand, Farley Ishaak

Select the state space model type

Description

This function is used in the function: calculate_trend_line_KFAS()

Usage

select_state_space_model(series, initial_values_all)

Arguments

Value

modelvalues (level, slope) of the chosen state space model and the provided time series

Author(s)

Pim Ouwehand

Set starting values for hyperparameters in state space models

Description

Set starting values for hyperparameters in state space models

Usage

set_startvalues(a, b, c, d, e)

Value

starting values for hyperparameters

Run forward and backward pass of time series estimation

Description

Calculate a trend line based on a provided model. This function is used in the function: calculate_trend_line_KFAS()

Usage

smooth_ts(fittedmodel)

Arguments

Value

A list containing multiple elements; sub-list signalsubconf[, 1] provides the estimated trend line.

Author(s)

Pim Ouwehand, Farley Ishaak

Validate Input Data for Hedonic Index Calculation

Description

This function checks whether the dataset contains all required variables, whether the dependent and continuous variables are numeric, and whether the period variable is formatted correctly (e.g., "2020Q1", "2020M01"). It ensures that the data is suitable for further processing in hedonic index calculations.

Usage

validate_input(
  dataset,
  period_variable,
  dependent_variable,
  continuous_variables,
  categorical_variables
)

Arguments

Value

Returns TRUE invisibly if all checks pass. Otherwise, an error is thrown.

Author(s)

David Pietersz