Type:	Package
Title:	Value-Price Analysis with Bayesian and Panel Data Methods
Version:	0.1.0
Description:	Provides tools for analyzing the relationship between direct prices (based on labor values) and prices of production using Bayesian generalized linear models, panel data methods, partial least squares regression, canonical correlation analysis, and panel vector autoregression. Includes functions for model comparison, out-of-sample validation, and structural break detection. Here, methods use raw accounting data with explicit temporal structure, following Gomez Julian (2023) <doi:10.17605/OSF.IO/7J8KF> and standard econometric techniques for panel data analysis.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1.0)
Imports:	stats, utils, Metrics
Suggests:	rstanarm, loo, plm, lme4, pls, vars, panelvar, strucchange, lmtest, sandwich, dplyr, tidyr, tibble, testthat (≥ 3.0.0), knitr, rmarkdown
Config/testthat/edition:	3
VignetteBuilder:	knitr
URL:	https://github.com/isadorenabi/valueprhr
BugReports:	https://github.com/isadorenabi/valueprhr/issues
NeedsCompilation:	no
Packaged:	2025-12-02 21:29:21 UTC; ROG
Author:	Jose Mauricio Gomez Julian [aut, cre]
Maintainer:	Jose Mauricio Gomez Julian <isadore.nabi@pm.me>
Repository:	CRAN
Date/Publication:	2025-12-09 08:10:02 UTC

valueprhr: Value-Price Analysis with Bayesian and Panel Data Methods

Description

This package provides tools for analyzing the relationship between direct prices (based on labor values) and prices of production using various econometric and statistical methods.

Main Functions

prepare_panel_data

Convert wide-format price matrices to panel data

fit_bayesian_glm_sectors

Fit Bayesian GLM for each sector

fit_twoway_fe

Fit two-way fixed effects panel model

fit_mundlak_cre

Fit Mundlak correlated random effects model

fit_pls_multivariate

Fit PLS regression with CV selection

run_cca_var_analysis

Run sparse CCA and panel VAR analysis

rolling_window_cv

Perform rolling window cross-validation

test_structural_breaks

Test for structural breaks

compare_models

Generate model comparison table

Author(s)

Maintainer: Jose Mauricio Gomez Julian isadore.nabi@pm.me (ORCID)

See Also

Useful links:

• https://github.com/isadorenabi/valueprhr

• Report bugs at https://github.com/isadorenabi/valueprhr/issues

Create Time-Series Aggregated Data

Description

Aggregates panel data to time series by computing means across sectors.

Usage

aggregate_to_timeseries(panel_data, vars = c("log_direct", "log_production"))

Arguments

Value

Data frame with one row per year containing aggregated values.

Examples

set.seed(123)
panel <- data.frame(
  year = rep(2000:2005, 3),
  sector = rep(c("A", "B", "C"), each = 6),
  log_direct = rnorm(18, mean = 5),
  log_production = rnorm(18, mean = 5)
)
ts_agg <- aggregate_to_timeseries(panel)
head(ts_agg)

Bayesian Generalized Linear Models for Sector Analysis

Description

Functions for fitting Bayesian GLMs sector by sector.

Calculate Mode Using Kernel Density Estimation

Description

Estimates the mode of a numeric vector using kernel density estimation. Falls back to median if density estimation fails.

Usage

calculate_mode(x)

Arguments

Value

A single numeric value representing the estimated mode.

Examples

set.seed(123)
x <- rnorm(100, mean = 5, sd = 1)
calculate_mode(x)

Canonical Correlation Analysis and Panel VAR

Description

Functions for sparse CCA and panel vector autoregression.

Check if Required Package is Available

Description

Checks for package availability and provides informative error message.

Usage

check_package(pkg, reason = "this functionality")

Arguments

Value

TRUE invisibly if package is available, otherwise stops with error.

Examples

check_package("stats", "basic statistics")

Choose Number of Principal Components

Description

Internal function to select PCs based on variance threshold.

Usage

choose_n_pcs(pca_obj, min_k, max_k, threshold)

Arguments

Value

Integer number of components.

Compare All Models

Description

Generates a comprehensive comparison table of all fitted models.

Usage

compare_models(
  twoway_result = NULL,
  mundlak_result = NULL,
  bayes_result = NULL,
  pls_result = NULL
)

Arguments

Value

A data frame with model comparison metrics.

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2019, 5),
    sector = rep(LETTERS[1:5], each = 20),
    log_direct = rnorm(100, 5, 0.5),
    log_production = rnorm(100, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(100, 0, 0.1)

  twoway <- fit_twoway_fe(panel)
  mundlak <- fit_mundlak_cre(panel)

  comparison <- compare_models(
    twoway_result = twoway,
    mundlak_result = mundlak
  )
  print(comparison)
}

Compute Multivariate MAE

Description

Compute Multivariate MAE

Usage

compute_multivariate_mae(Y_actual, Y_predicted)

Arguments

Value

Numeric MAE value.

Compute Multivariate Metrics

Description

Internal function to compute all metrics for multivariate predictions.

Usage

compute_multivariate_metrics(Y_actual, Y_predicted)

Arguments

Value

List of metrics.

Compute Multivariate R-squared

Description

Compute Multivariate R-squared

Usage

compute_multivariate_r2(Y_actual, Y_predicted)

Arguments

Value

Numeric R-squared value.

Compute Multivariate RMSE

Description

Compute Multivariate RMSE

Usage

compute_multivariate_rmse(Y_actual, Y_predicted)

Arguments

Value

Numeric RMSE value.

Compute OOS Degradation

Description

Compares out-of-sample metrics to in-sample metrics.

Usage

compute_oos_degradation(insample_metrics, cv_results)

Arguments

Value

Data frame with degradation percentages.

Examples

insample <- list(rmse_fe = 0.05, rmse_m = 0.04)
cv_res <- data.frame(
  rmse_fe_all = c(0.06, 0.055, 0.058),
  rmse_m_all = c(0.045, 0.042, 0.044)
)
degradation <- compute_oos_degradation(insample, cv_res)
print(degradation)

Compute PLS Cross-Validation Metrics

Description

Internal function to compute CV metrics for each component number.

Usage

compute_pls_cv_metrics(Y_actual, cv_predictions, ncomp)

Arguments

Value

Data frame with CV metrics by component.

Compute BIC for Panel VAR Model

Description

Internal function to compute approximate BIC.

Usage

compute_pvar_bic(pvar_obj)

Arguments

Value

Numeric BIC value.

Compute In-Sample R-squared

Description

Calculates the coefficient of determination (R-squared) for predictions.

Usage

compute_r2(actual, predicted)

Arguments

Value

A single numeric R-squared value, or NA if not computable.

Examples

actual <- c(1, 2, 3, 4, 5)
predicted <- c(1.1, 1.9, 3.1, 4.0, 4.9)
compute_r2(actual, predicted)

Create Mundlak-Transformed Panel Data

Description

Adds sector-level means and within-deviations for Mundlak/CRE estimation.

Usage

create_mundlak_data(panel_data, x_var = "log_direct")

Arguments

Value

Panel data with additional columns:

x_mean_sector

Sector-level mean of x_var

x_within

Within-sector deviation (x - sector mean)

Examples

set.seed(123)
panel <- data.frame(
  year = rep(2000:2002, 3),
  sector = rep(c("A", "B", "C"), each = 3),
  log_direct = rnorm(9, mean = 5),
  log_production = rnorm(9, mean = 5)
)
panel_mundlak <- create_mundlak_data(panel)
head(panel_mundlak)

Create Summary Table from Sector Results

Description

Internal function to create a data frame summary from sector results.

Usage

create_sector_summary_table(results_list)

Arguments

Value

Data frame with summary statistics.

Data Preparation Functions

Description

Functions for preparing and transforming price data for analysis.

Evaluate In-Sample Model Performance

Description

Computes various error metrics comparing predictions to actual values.

Usage

evaluate_insample(predicted, actual)

Arguments

Value

A list containing:

mae_log

MAE in log scale

rmse_log

RMSE in log scale

mae_orig

MAE in original scale (after exp transformation)

rmse_orig

RMSE in original scale

mae_rel_range

MAE as percentage of range

Examples

set.seed(123)
actual <- log(runif(50, 100, 200))
predicted <- actual + rnorm(50, 0, 0.1)
evaluate_insample(predicted, actual)

Export Results to CSV

Description

Exports analysis results to CSV files.

Usage

export_results_csv(
  comparison_table = NULL,
  cv_results = NULL,
  sector_summary = NULL,
  output_dir = tempdir(),
  prefix = "valueprhr"
)

Arguments

Value

Character vector of created file paths.

Examples

comparison <- data.frame(
  model = c("Model A", "Model B"),
  R2 = c(0.95, 0.92)
)
files <- export_results_csv(comparison_table = comparison)
print(files)

Extract Estimated Breakpoints

Description

Internal function to estimate breakpoint locations.

Usage

extract_breakpoints(ts_data)

Arguments

Value

Vector of estimated breakpoint years, or NULL.

Extract Top CCA Loadings

Description

Extracts the variables with highest absolute loadings for each CCA component.

Usage

extract_cca_loadings(
  cca_result,
  n_top = 5L,
  which_matrix = c("both", "X", "Y")
)

Arguments

Value

Data frame with top loadings.

Examples

set.seed(123)
n <- 50
p <- 20
X <- matrix(rnorm(n * p), n, p)
Y <- X %*% matrix(rnorm(p * 5), p, 5) + matrix(rnorm(n * 5, 0, 0.5), n, 5)
colnames(X) <- paste0("X", 1:p)
colnames(Y) <- paste0("Y", 1:5)

cca_res <- run_sparse_cca(X, Y, n_components = 2)
top_loads <- extract_cca_loadings(cca_res)
print(top_loads)

Extract PLS Variable Importance

Description

Extracts variable importance scores from a fitted PLS model.

Usage

extract_pls_importance(pls_result, ncomp = NULL)

Arguments

Value

Data frame with variable names and importance scores.

Examples

if (requireNamespace("pls", quietly = TRUE)) {
  set.seed(123)
  n <- 50
  p <- 10
  X <- matrix(rnorm(n * p), n, p)
  colnames(X) <- paste0("X", 1:p)
  Y <- X[, 1:3] %*% diag(c(1, 0.5, 0.3)) + matrix(rnorm(n * 3, 0, 0.5), n, 3)
  colnames(Y) <- paste0("Y", 1:3)

  result <- fit_pls_multivariate(X, Y, max_components = 5)
  importance <- extract_pls_importance(result)
  print(head(importance))
}

Extract Random Effects Variance Components

Description

Internal function to extract variance components from plm summary.

Usage

extract_re_variances(summary_obj)

Arguments

Value

A list with id (between) and idios (within) variance components.

Extract Sector Coefficients

Description

Extracts intercepts and slopes from all sector models.

Usage

extract_sector_coefficients(sector_results)

Arguments

Value

Data frame with sector names, intercepts, and slopes.

Examples

if (requireNamespace("rstanarm", quietly = TRUE)) {
  set.seed(123)
  years <- 2000:2010
  direct <- data.frame(
    Year = years,
    A = 100 + cumsum(rnorm(11)),
    B = 120 + cumsum(rnorm(11))
  )
  production <- data.frame(
    Year = years,
    A = 102 + cumsum(rnorm(11)),
    B = 118 + cumsum(rnorm(11))
  )
  results <- fit_bayesian_glm_sectors(direct, production,
                                       chains = 2, iter = 1000)
  coefs <- extract_sector_coefficients(results$results)
  print(coefs)
}

Fit Aggregated VAR Model

Description

Fits a VAR model on time-aggregated (mean across sectors) data.

Usage

fit_aggregated_var(panel_data, max_lags = 6L, difference = TRUE)

Arguments

Value

A list containing:

model

The fitted vars::VAR model

selected_lag

Lag selected by information criteria

irf

Impulse response functions (if computed)

fevd

Forecast error variance decomposition (if computed)

Examples

if (requireNamespace("vars", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2019, 5),
    sector = rep(LETTERS[1:5], each = 20),
    log_direct = rnorm(100, 5, 0.5),
    log_production = rnorm(100, 5, 0.5)
  )

  result <- fit_aggregated_var(panel)
  print(result$selected_lag)
}

Fit Bayesian GLM for Each Sector

Description

Fits separate Bayesian generalized linear models for each sector, regressing production prices on direct prices.

Usage

fit_bayesian_glm_sectors(
  direct_prices,
  production_prices,
  chains = 4L,
  iter = 4000L,
  seed = 12345L,
  verbose = TRUE
)

Arguments

Details

This function requires the 'rstanarm' and 'loo' packages to be installed. Each sector model uses a Gaussian family with identity link and weakly informative priors.

Value

A list with two elements:

results

List of results for each sector

summary_table

Data frame with summary statistics for all sectors

Examples

if (requireNamespace("rstanarm", quietly = TRUE)) {
  set.seed(123)
  years <- 2000:2010

  direct <- data.frame(
    Year = years,
    Agriculture = 100 + cumsum(rnorm(11, 2, 1)),
    Manufacturing = 120 + cumsum(rnorm(11, 2, 1))
  )

  production <- data.frame(
    Year = years,
    Agriculture = 102 + cumsum(rnorm(11, 2, 1)),
    Manufacturing = 118 + cumsum(rnorm(11, 2, 1))
  )

  results <- fit_bayesian_glm_sectors(
    direct, production,
    chains = 2, iter = 1000
  )
  print(results$summary_table)
}

Fit Bayesian Hierarchical Panel Model

Description

Fits a Bayesian mixed effects model with random slopes by sector.

Usage

fit_bayesian_hierarchical(
  panel_data,
  include_time = TRUE,
  chains = 4L,
  iter = 4000L,
  seed = 12345L
)

Arguments

Value

A list containing:

model

The fitted rstanarm model object

r2_bayes

Bayesian R-squared (mean)

summary

Model summary for fixed effects

metrics

In-sample evaluation metrics

Examples

## Not run: 
if (requireNamespace("rstanarm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2009, 5),
    sector = rep(LETTERS[1:5], each = 10),
    time = rep(1:10, 5),
    log_direct = rnorm(50, 5, 0.5),
    log_production = rnorm(50, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(50, 0, 0.1)

  result <- fit_bayesian_hierarchical(panel, chains = 2, iter = 1000)
  print(result$r2_bayes)
}

## End(Not run)

Fit Mundlak Correlated Random Effects Model

Description

Fits a Mundlak (CRE) model that decomposes effects into within and between components, allowing for correlation between unit effects and regressors.

Usage

fit_mundlak_cre(panel_data, include_time_fe = TRUE, robust_se = TRUE)

Arguments

Details

The Mundlak transformation adds sector-level means of the regressors to a random effects model, allowing consistent estimation even when the random effects are correlated with the regressors.

Value

A list containing:

model

The fitted plm model object

summary

Model summary

panel_data_augmented

Panel data with Mundlak transformations

coeftest_robust

Robust coefficient tests

variance_components

Random effects variance components

metrics

In-sample evaluation metrics

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2009, 5),
    sector = rep(LETTERS[1:5], each = 10),
    log_direct = rnorm(50, 5, 0.5),
    log_production = rnorm(50, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(50, 0, 0.1)

  result <- fit_mundlak_cre(panel)
  print(result$variance_components)
}

Fit Panel VAR Model

Description

Fits a panel vector autoregression model with first-difference transformation.

Usage

fit_panel_var(panel_data, max_lags = 2L, verbose = TRUE)

Arguments

Value

A list containing:

model

The fitted panelvar model

best_lag

Selected lag order

bic_values

BIC for each lag order tested

Examples

if (requireNamespace("panelvar", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2019, 5),
    sector = rep(LETTERS[1:5], each = 20),
    log_direct = rnorm(100, 5, 0.5),
    log_production = rnorm(100, 5, 0.5)
  )

  result <- fit_panel_var(panel)
  print(result$best_lag)
}

Fit PLS Regression with Cross-Validation Component Selection

Description

Fits a partial least squares regression model with automatic selection of the optimal number of components via cross-validation.

Usage

fit_pls_multivariate(
  X_matrix,
  Y_matrix,
  max_components = NULL,
  cv_segments = 10L,
  scale = TRUE,
  center = TRUE
)

Arguments

Details

This function uses the pls package for PLS regression. Component selection is based on minimizing cross-validated RMSE. The function handles log-transformed data and reports metrics in both log and original scales.

Value

A list containing:

model

The fitted pls model object

optimal_ncomp

Optimal number of components by CV-RMSE

cv_table

Data frame with CV metrics by number of components

metrics_cv

CV metrics at optimal component number

metrics_insample

In-sample metrics at optimal component number

Examples

if (requireNamespace("pls", quietly = TRUE)) {
  set.seed(123)
  n <- 50
  p <- 10
  X <- matrix(rnorm(n * p), n, p)
  colnames(X) <- paste0("X", 1:p)
  Y <- X[, 1:3] %*% diag(c(1, 0.5, 0.3)) + matrix(rnorm(n * 3, 0, 0.5), n, 3)
  colnames(Y) <- paste0("Y", 1:3)

  result <- fit_pls_multivariate(X, Y, max_components = 8)
  print(result$optimal_ncomp)
  print(result$cv_table)
}

Fit and Predict with FE Sector Model

Description

Internal function to fit FE model and generate predictions.

Usage

fit_predict_fe_sector(df_train, df_test)

Arguments

Value

Numeric vector of predictions.

Fit and Predict with Mundlak Model

Description

Internal function to fit Mundlak model and generate predictions.

Usage

fit_predict_mundlak(df_train, df_test)

Arguments

Value

Numeric vector of predictions.

Fit Single Sector Bayesian GLM (Internal)

Description

Internal function to fit a Bayesian GLM for one sector.

Usage

fit_single_sector_glm(x_values, y_values, sector_name, chains, iter, seed)

Arguments

Value

A list with model results and diagnostics.

Fit Two-Way Fixed Effects Panel Model

Description

Fits a two-way fixed effects model with sector and time effects, regressing log production prices on log direct prices.

Usage

fit_twoway_fe(
  panel_data,
  robust_se = TRUE,
  cluster_type = c("group", "time", "twoway")
)

Arguments

Details

This function requires the 'plm' package. The model specification is: log_production ~ log_direct with two-way (sector and time) fixed effects.

Value

A list containing:

model

The fitted plm model object

summary

Model summary

r2_within

Within R-squared

coeftest_robust

Coefficient test with robust SE (if robust_se=TRUE)

metrics

In-sample evaluation metrics

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2009, 5),
    sector = rep(LETTERS[1:5], each = 10),
    log_direct = rnorm(50, 5, 0.5),
    log_production = rnorm(50, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(50, 0, 0.1)

  result <- fit_twoway_fe(panel)
  print(result$r2_within)
}

Format Structural Break Results

Description

Creates a formatted summary of structural break test results.

Usage

format_break_results(break_results, alpha = 0.05)

Arguments

Value

A data frame with formatted test summaries.

Examples

if (requireNamespace("strucchange", quietly = TRUE)) {
  set.seed(123)
  years <- 1980:2020
  panel <- data.frame(
    year = rep(years, 5),
    sector = rep(LETTERS[1:5], each = length(years)),
    log_direct = rnorm(length(years) * 5, 5, 0.5),
    log_production = rnorm(length(years) * 5, 5, 0.5)
  )

  break_tests <- test_structural_breaks(panel)
  summary_df <- format_break_results(break_tests)
  print(summary_df)
}

Generate Comprehensive Analysis Summary

Description

Creates a complete summary of all analysis results including models, validation, and structural break tests.

Usage

generate_analysis_summary(
  comparison_table,
  cv_summary = NULL,
  break_results = NULL,
  cca_results = NULL,
  granger_results = NULL
)

Arguments

Value

A list with formatted summary components.

Examples

comparison <- data.frame(
  model = c("Model A", "Model B"),
  R2 = c(0.95, 0.92),
  RMSE_log = c(0.05, 0.06)
)
summary_list <- generate_analysis_summary(comparison)
print(summary_list$best_model)

Extract R-squared Values from rstanarm Model

Description

Attempts to extract R-squared using loo_R2, falling back to bayes_R2.

Usage

get_r2_values(fit, verbose = FALSE)

Arguments

Value

A named numeric vector with mean, median, and mode R-squared values.

Examples

if (requireNamespace("rstanarm", quietly = TRUE)) {
  data(mtcars)
  fit <- rstanarm::stan_glm(mpg ~ wt, data = mtcars,
                            chains = 2, iter = 1000, refresh = 0)
  get_r2_values(fit)
}

Interpret Break Test Results

Description

Provides textual interpretation of structural break tests.

Usage

interpret_break_tests(break_results, alpha = 0.05)

Arguments

Value

Character string with interpretation.

Examples

if (requireNamespace("strucchange", quietly = TRUE)) {
  set.seed(123)
  years <- 1980:2020
  panel <- data.frame(
    year = rep(years, 5),
    sector = rep(LETTERS[1:5], each = length(years)),
    log_direct = rnorm(length(years) * 5, 5, 0.5),
    log_production = rnorm(length(years) * 5, 5, 0.5)
  )

  break_tests <- test_structural_breaks(panel)
  interpretation <- interpret_break_tests(break_tests)
  cat(interpretation)
}

Leave-One-Sector-Out Cross-Validation

Description

Performs LOSO CV, leaving out each sector in turn as the test set.

Usage

leave_one_sector_out(panel_data, verbose = TRUE)

Arguments

Value

A data frame with RMSE and MAE for each held-out sector.

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2019, 5),
    sector = rep(LETTERS[1:5], each = 20),
    log_direct = rnorm(100, 5, 0.5),
    log_production = rnorm(100, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(100, 0, 0.1)

  loso_results <- leave_one_sector_out(panel)
  print(loso_results)
}

Panel Granger Causality Test (Dumitrescu-Hurlin)

Description

Performs panel Granger causality tests between direct and production prices.

Usage

panel_granger_test(panel_data, lags = c(1L, 2L))

Arguments

Details

Tests both directions: direct -> production and production -> direct.

Value

A data frame with test results for each direction and lag.

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2019, 5),
    sector = rep(LETTERS[1:5], each = 20),
    log_direct = rnorm(100, 5, 0.5),
    log_production = rnorm(100, 5, 0.5)
  )

  granger_results <- panel_granger_test(panel)
  print(granger_results)
}

Panel Data Models for Value-Price Analysis

Description

Functions for fitting two-way fixed effects and Mundlak CRE models.

Partial Least Squares Regression Analysis

Description

Functions for multivariate PLS regression with cross-validation.

Predict from PLS Model

Description

Generate predictions from a fitted PLS model.

Usage

predict_pls(pls_result, newdata = NULL, ncomp = NULL)

Arguments

Value

Matrix of predictions.

Examples

if (requireNamespace("pls", quietly = TRUE)) {
  set.seed(123)
  n <- 50
  p <- 10
  X <- matrix(rnorm(n * p), n, p)
  colnames(X) <- paste0("X", 1:p)
  Y <- X[, 1:3] %*% diag(c(1, 0.5, 0.3)) + matrix(rnorm(n * 3, 0, 0.5), n, 3)
  colnames(Y) <- paste0("Y", 1:3)

  result <- fit_pls_multivariate(X, Y, max_components = 5)
  preds <- predict_pls(result)
  dim(preds)
}

Prepare Log-Transformed Matrices

Description

Extracts numeric columns from price data frames and applies log transform.

Usage

prepare_log_matrices(
  direct_prices,
  production_prices,
  exclude_cols = c("Year")
)

Arguments

Value

A list containing:

X_log

Log-transformed matrix of direct prices

Y_log

Log-transformed matrix of production prices

complete_cases

Logical vector indicating complete cases

X_clean

Subset of X_log with complete cases

Y_clean

Subset of Y_log with complete cases

Examples

set.seed(123)
direct <- data.frame(
  Year = 2000:2005,
  A = runif(6, 100, 200),
  B = runif(6, 100, 200)
)
production <- data.frame(
  Year = 2000:2005,
  A = runif(6, 100, 200),
  B = runif(6, 100, 200)
)
matrices <- prepare_log_matrices(direct, production)
str(matrices)

Prepare Panel Data from Wide Format Matrices

Description

Converts wide-format price matrices (with Year as first column and sectors as subsequent columns) into long-format panel data suitable for panel regression analysis.

Usage

prepare_panel_data(direct_prices, production_prices, log_transform = TRUE)

Arguments

Value

A data frame in panel (long) format with columns:

year

Year of observation

sector

Sector identifier

sector_id

Numeric sector identifier

time

Time index (year minus minimum year plus 1)

direct_price

Direct price value

production_price

Price of production value

log_direct

Log of direct price (if log_transform = TRUE)

log_production

Log of production price (if log_transform = TRUE)

Examples

set.seed(123)
years <- 2000:2010
sectors <- c("Agriculture", "Manufacturing", "Services")

direct <- data.frame(
  Year = years,
  Agriculture = 100 + cumsum(rnorm(11)),
  Manufacturing = 120 + cumsum(rnorm(11)),
  Services = 90 + cumsum(rnorm(11))
)

production <- data.frame(
  Year = years,
  Agriculture = 102 + cumsum(rnorm(11)),
  Manufacturing = 118 + cumsum(rnorm(11)),
  Services = 92 + cumsum(rnorm(11))
)

panel <- prepare_panel_data(direct, production)
head(panel)

Print Analysis Summary

Description

Prints a formatted summary of analysis results to console.

Usage

print_analysis_summary(summary_list, verbose = TRUE)

Arguments

Value

Invisible NULL.

Examples

comparison <- data.frame(
  model = c("Two-Way FE", "Mundlak CRE"),
  R2 = c(0.95, 0.92),
  RMSE_log = c(0.05, 0.06)
)
summary_list <- generate_analysis_summary(comparison)
print_analysis_summary(summary_list)

Robust Summary Statistics with Bootstrap Confidence Intervals

Description

Computes mean, standard deviation, median, MAD, trimmed mean, and bootstrap confidence intervals for a numeric vector.

Usage

robust_summary(x, bootstrap_reps = 200L, trim_proportion = 0.1)

Arguments

Value

A list containing:

mean

Arithmetic mean

sd

Standard deviation

median

Median

mad

Median Absolute Deviation (scaled)

tmean

Trimmed mean

ci

Vector of length 2 with 95 percent bootstrap CI bounds

Examples

set.seed(123)
x <- rnorm(50)
robust_summary(x)

Rolling Window Cross-Validation

Description

Performs time-series cross-validation using rolling windows, comparing fixed effects and Mundlak models.

Usage

rolling_window_cv(
  panel_data,
  window_sizes = c(20L, 30L),
  step_size = 2L,
  test_horizon = 3L,
  verbose = TRUE
)

Arguments

Details

For each rolling window, the function fits both a fixed effects model (by sector) and a Mundlak CRE model, then evaluates predictions on the test period. Results are separated by whether test sectors were seen during training (common) or not (new).

Value

A data frame with validation results for each window, including:

window_size

Training window size

window_start

Start year of training window

window_end

End year of training window

rmse_fe_all

RMSE for FE model on all test observations

rmse_m_all

RMSE for Mundlak model on all test observations

n_test_common

Number of test obs from sectors in training

n_test_new

Number of test obs from new sectors

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2029, 5),
    sector = rep(LETTERS[1:5], each = 30),
    log_direct = rnorm(150, 5, 0.5),
    log_production = rnorm(150, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(150, 0, 0.1)

  cv_results <- rolling_window_cv(panel, window_sizes = c(15, 20))
  print(head(cv_results))
}

Run Complete CCA and VAR Analysis

Description

Convenience function to run both sparse CCA and panel/aggregated VAR.

Usage

run_cca_var_analysis(
  direct_prices,
  production_prices,
  panel_data,
  cca_components = 3L,
  verbose = TRUE
)

Arguments

Value

A list with cca, pvar, agg_var, and granger results.

Examples

set.seed(123)
years <- 2000:2019
sectors <- LETTERS[1:5]

direct <- data.frame(Year = years)
production <- data.frame(Year = years)
for (s in sectors) {
  direct[[s]] <- 100 + cumsum(rnorm(20, 2, 1))
  production[[s]] <- 102 + cumsum(rnorm(20, 2, 1))
}

panel <- prepare_panel_data(direct, production)

matrices <- prepare_log_matrices(direct, production)

result <- run_cca_var_analysis(
  direct, production, panel,
  cca_components = 2
)

Run Chow Tests at Candidate Break Years

Description

Internal function to perform Chow tests.

Usage

run_chow_tests(ts_data, break_years, min_segment)

Arguments

Value

Data frame with Chow test results.

Run CUSUM Test

Description

Internal function to perform recursive CUSUM test.

Usage

run_cusum_test(ts_data)

Arguments

Value

List with test statistic and p-value.

Run Complete Analysis Pipeline

Description

Convenience function to run the full analysis pipeline.

Usage

run_full_analysis(
  direct_prices,
  production_prices,
  run_bayesian = FALSE,
  run_cv = TRUE,
  run_breaks = TRUE,
  verbose = TRUE
)

Arguments

Value

A list with all analysis results.

Examples

set.seed(123)
years <- 2000:2019
sectors <- LETTERS[1:5]

direct <- data.frame(Year = years)
production <- data.frame(Year = years)
for (s in sectors) {
  direct[[s]] <- 100 + cumsum(rnorm(20, 2, 1))
  production[[s]] <- 102 + cumsum(rnorm(20, 2, 1))
}

if (requireNamespace("plm", quietly = TRUE)) {
  results <- run_full_analysis(
    direct, production,
    run_bayesian = FALSE,
    run_cv = FALSE
  )
  print(results$comparison)
}

Run MOSUM Test

Description

Internal function to perform OLS-MOSUM test.

Usage

run_mosum_test(ts_data)

Arguments

Value

List with test statistic and p-value.

Run Sparse CCA with PCA Preprocessing

Description

Performs canonical correlation analysis on PCA-reduced price matrices, with optional sparsity penalties.

Usage

run_sparse_cca(
  X_matrix,
  Y_matrix,
  n_components = 3L,
  variance_threshold = 0.9,
  min_pcs = 8L,
  max_pcs = 12L
)

Arguments

Details

The function first reduces dimensionality using PCA, then applies CCA. Falls back to base R cancor if specialized packages unavailable.

Value

A list containing:

method

Method used for CCA

correlations

Canonical correlations

U_loadings

X loadings in PC space

V_loadings

Y loadings in PC space

W_X_original

X loadings projected to original variables

W_Y_original

Y loadings projected to original variables

n_pcs_x

Number of PCs used for X

n_pcs_y

Number of PCs used for Y

Examples

set.seed(123)
n <- 50
p <- 20
X <- matrix(rnorm(n * p), n, p)
Y <- X %*% matrix(rnorm(p * 5), p, 5) + matrix(rnorm(n * 5, 0, 0.5), n, 5)
colnames(X) <- paste0("X", 1:p)
colnames(Y) <- paste0("Y", 1:5)

result <- run_sparse_cca(X, Y, n_components = 2)
print(result$correlations)

Run Supremum F Test

Description

Internal function to perform supF test for unknown break date.

Usage

run_supf_test(ts_data)

Arguments

Value

List with test statistic and p-value.

Safe MAE Calculation

Description

Calculates Mean Absolute Error handling non-finite values.

Usage

safe_mae(actual, predicted)

Arguments

Value

A single numeric value for MAE, or NA if calculation not possible.

Examples

actual <- c(1, 2, 3, 4, 5)
predicted <- c(1.1, 2.2, 2.9, 4.1, 5.2)
safe_mae(actual, predicted)

Safe Percentage Calculation

Description

Calculates percentage while handling division by zero and non-finite values.

Usage

safe_pct(numerator, denominator)

Arguments

Value

Numeric vector of percentages, with NA for invalid calculations.

Examples

safe_pct(c(10, 20, 30), c(100, 0, 200))

Safe RMSE Calculation

Description

Calculates Root Mean Squared Error handling non-finite values.

Usage

safe_rmse(actual, predicted)

Arguments

Value

A single numeric value for RMSE, or NA if calculation not possible.

Examples

actual <- c(1, 2, 3, 4, 5)
predicted <- c(1.1, 2.2, 2.9, 4.1, 5.2)
safe_rmse(actual, predicted)

Structural Break Tests

Description

Functions for testing structural breaks in the price relationship.

Summarize Rolling Window Results

Description

Computes summary statistics from rolling window cross-validation.

Usage

summarize_cv_results(cv_results, bootstrap_reps = 300L)

Arguments

Value

A data frame with summary statistics by model and partition.

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2029, 5),
    sector = rep(LETTERS[1:5], each = 30),
    log_direct = rnorm(150, 5, 0.5),
    log_production = rnorm(150, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(150, 0, 0.1)

  cv_results <- rolling_window_cv(panel, window_sizes = c(15, 20))
  summary_stats <- summarize_cv_results(cv_results)
  print(summary_stats)
}

Model Comparison and Summary Functions

Description

Functions for comparing models and generating summary tables.

Test Mundlak Specification

Description

Performs Wald test on the sector-mean coefficient to test whether fixed effects would be preferred over random effects.

Usage

test_mundlak_specification(mundlak_result)

Arguments

Details

Under the null hypothesis that the sector means coefficient equals zero, random effects would be appropriate. Rejection suggests fixed effects should be used.

Value

A list with test statistic, degrees of freedom, and p-value.

Examples

if (requireNamespace("plm", quietly = TRUE)) {
  set.seed(123)
  panel <- data.frame(
    year = rep(2000:2009, 5),
    sector = rep(LETTERS[1:5], each = 10),
    log_direct = rnorm(50, 5, 0.5),
    log_production = rnorm(50, 5, 0.5)
  )
  panel$log_production <- panel$log_direct * 0.95 + rnorm(50, 0, 0.1)

  mundlak_fit <- fit_mundlak_cre(panel)
  test_result <- test_mundlak_specification(mundlak_fit)
  print(test_result)
}

Test for Structural Breaks

Description

Performs multiple structural break tests on the aggregated time series relationship between direct and production prices.

Usage

test_structural_breaks(panel_data, chow_years = NULL, min_segment = 10L)

Arguments

Details

This function aggregates panel data to a single time series by taking means across sectors, then applies various structural break tests from the strucchange package.

Value

A list containing:

chow

Chow test results for candidate years

cusum

CUSUM test results

mosum

MOSUM test results

supf

supremum F test results

breakpoints

Estimated breakpoint dates

aggregated_data

The aggregated time series used

Examples

if (requireNamespace("strucchange", quietly = TRUE)) {
  set.seed(123)
  years <- 1980:2020
  panel <- data.frame(
    year = rep(years, 5),
    sector = rep(LETTERS[1:5], each = length(years)),
    log_direct = rnorm(length(years) * 5, 5, 0.5),
    log_production = rnorm(length(years) * 5, 5, 0.5)
  )

  break_tests <- test_structural_breaks(panel)
  print(break_tests$cusum)
}

Utility Functions for Value-Price Analysis

Description

Internal utility functions used across the package.

Validate Panel Data Structure

Description

Checks that panel data has required columns and valid structure.

Usage

validate_panel_data(panel_data, require_log = TRUE)

Arguments

Value

TRUE invisibly if valid, otherwise stops with informative error.

Examples

set.seed(123)
panel <- data.frame(
  year = rep(2000:2002, 3),
  sector = rep(c("A", "B", "C"), each = 3),
  log_direct = rnorm(9),
  log_production = rnorm(9)
)
validate_panel_data(panel)

Out-of-Sample Validation Functions

Description

Functions for rolling window and leave-one-sector-out validation.