Package {phoring}

Type:	Package
Title:	Unified Econometric Estimation, Specification Testing, and Diagnostics
Version:	0.1.0
Description:	A unified econometric toolkit providing automated specification testing, robust covariance estimation, and publication-grade academic visualizations. Implements ordinary and weighted least squares with robust, cluster-robust, and heteroskedasticity and autocorrelation consistent (HAC) standard errors; instrumental variables two-stage least squares (2SLS); panel data estimators (within fixed effects, two-way fixed effects, random effects GLS, first differences); binary response models (logit and probit with average marginal effects); tobit censored regression; count models (Poisson and negative binomial); causal inference methods (difference-in-differences, sharp regression discontinuity); quantile regression; dynamic time series models (autoregressive distributed lag, Koyck distributed lag, Prais-Winsten AR(1), error correction models, vector autoregression); and time series unit root batteries (augmented Dickey-Fuller, Phillips-Perron, KPSS, Engle-Granger cointegration, Granger causality, ARCH). Estimation routines integrate comprehensive diagnostic batteries, parameter stability tests, and academic visualizations with zero external package dependencies.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 3.5.0)
Imports:	graphics, grDevices, stats
Suggests:	testthat (≥ 3.0.0)
URL:	https://github.com/abidsiddique/phoring
BugReports:	https://github.com/abidsiddique/phoring/issues
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-10-23 21:30:44 UTC; abid
Author:	Md. Abid Siddique Emon [aut, cre]
Maintainer:	Md. Abid Siddique Emon <contact.abidsiddique@gmail.com>
Repository:	CRAN
Date/Publication:	2026-11-07 17:00:09 UTC

Autoregressive Conditional Heteroskedasticity (ARCH) Test

Description

Engle's Lagrange Multiplier test for ARCH effects / volatility clustering in time series models.

Usage

arch_test(model, lags = 2, digits = 4)

Arguments

Value

An object of class econ_arch.

Examples

data(macro_data, package = "phoring")
m <- reg(inflation ~ fed_funds, data = macro_data, diagnostics = FALSE)
arch_test(m, lags = 2)

Autoregressive Distributed Lag (ARDL) Model

Description

Estimates an ARDL(p, q) model and calculates both short-run impact multipliers and long-run cumulative propensity multipliers with delta-method standard errors.

Usage

ardl(formula, data, p = 1, q = 1, digits = 4)

Arguments

Value

An object of class econ_ardl.

Examples

data(macro_data, package = "phoring")
ardl(inflation ~ unemployment, data = macro_data, p = 1, q = 1)

Chow Test for Parameter Stability and Structural Breaks

Description

Tests whether regression coefficients are constant across two subsamples (e.g. before/after a policy shock, male vs. female, or high vs. low income).

Usage

chow_test(formula, split_by, data = NULL, digits = 4)

Arguments

Value

An object of class econ_chow.

Examples

data(wage_data, package = "phoring")
# Test if wage equation differs by gender
chow_test(wage ~ educ + exper, split_by = "female", data = wage_data)

Engle-Granger Cointegration Test

Description

Tests whether two or more non-stationary time series share a stationary long-run equilibrium cointegrating relationship.

Usage

coint(formula, data, lags = 1, digits = 4)

Arguments

Value

An object of class econ_coint.

Examples

data(macro_data, package = "phoring")
coint(inflation ~ unemployment + fed_funds, data = macro_data)

Returns to Schooling and Proximity to College Dataset

Description

A dataset of 800 individuals designed for Instrumental Variables (2SLS) estimation, based on David Card's (1995) landmark study using geographic proximity to a 4-year college as an exogenous instrument for educational attainment.

Usage

college_distance

Format

A data frame with 800 rows and 9 variables:

wage

Hourly wage in US dollars

educ

Years of completed education (endogenous regressor)

exper

Years of potential labor market experience

tenure

Years with current employer

near_college

Indicator: 1 if grew up near a 4-year college (excluded instrument)

mother_educ

Mother's years of education (excluded instrument)

father_educ

Father's years of education (excluded instrument)

south

Indicator: 1 if residing in the South

black

Indicator: 1 if Black

Source

Modeled after Card (1995), "Using Geographic Variation in College Proximity".

Correlation Matrix with Significance Stars

Description

Computes pairwise Pearson correlation coefficients with significance stars.

Usage

corr(data, vars = NULL, digits = 3)

Arguments

Value

An object of class econ_corr.

Examples

data(mtcars)
corr(mtcars, vars = c("mpg", "hp", "wt", "qsec"))

Count Data Models (Poisson and Negative Binomial Regression)

Description

Fits Poisson or Negative Binomial regression for count data, computes Incidence Rate Ratios (IRR) with 95% confidence intervals, robust standard errors, and conducts Cameron & Trivedi's overdispersion test or Likelihood Ratio test against Poisson.

Usage

count_reg(formula, data, model = c("poisson", "negbin"), digits = 4)

Arguments

Value

An object of class econ_count.

Examples

set.seed(42)
n <- 300
x1 <- rnorm(n); x2 <- rbinom(n, 1, 0.5)
y <- rpois(n, lambda = exp(0.5 + 0.3 * x1 - 0.4 * x2))
df <- data.frame(y, x1, x2)
count_reg(y ~ x1 + x2, data = df, model = "poisson")
count_reg(y ~ x1 + x2, data = df, model = "negbin")

County-Level Crime Rates Panel Dataset

Description

A balanced panel dataset of 50 counties observed over 4 years (2020-2023), totaling 200 observations. Useful for testing the economic model of crime with Fixed Effects, Random Effects, and entity-clustered standard errors.

Usage

crime_panel

Format

A data frame with 200 rows and 6 variables:

county_id

County identifier (1 to 50)

year

Year of observation (2020 to 2023)

crime_rate

Reported crimes per 1,000 residents

prbarr

Probability of arrest (ratio of arrests to reported offenses)

police

Police officers per 1,000 residents

unemp

County unemployment rate (percentage)

Source

Modeled after Cornwell and Trumbull (1994) panel crime study.

Publication-Ready Summary Statistics and Covariate Balance for Economics

Description

Generates standard summary statistics (Obs, Mean, Std Dev, Min, P25, Median, P75, Max, Missing) for numeric variables in a dataset, or computes a full "Table 1" covariate balance test across two treatment/subsample groups with normalized differences and t-tests.

Usage

desc(data, vars = NULL, by = NULL, digits = 3)

Arguments

Value

A data.frame of class econ_desc or econ_desc_balance.

Examples

data(wage_data, package = "phoring")
# Standard descriptive statistics
desc(wage_data, vars = c("wage", "educ", "exper"))

# Covariate balance table ("Table 1") by gender
desc(wage_data, vars = c("wage", "educ", "exper"), by = "female")

Econometric Diagnostic Tests

Description

Computes a complete battery of econometric diagnostic tests for linear models, including Breusch-Pagan, White, Durbin-Watson, Breusch-Godfrey, Ramsey RESET, Multicollinearity (VIF), and Jarque-Bera normality tests.

Usage

diagnostics(model, ...)

Arguments

Value

An object of class 'econ_diagnostics' containing test statistics, p-values, decisions, and individual VIF values.

Examples

data(mtcars)
m <- reg(mpg ~ wt + hp + qsec, data = mtcars)
diagnostics(m)

Difference-in-Differences (DiD) Policy Evaluation

Description

Fits a Difference-in-Differences (DiD) model with optional control covariates, computes cluster-robust standard errors, and outputs the classic 2x2 pre/post treatment table alongside full regression estimates.

Usage

did(
  formula,
  data,
  treat = NULL,
  post = NULL,
  time = NULL,
  cluster = NULL,
  digits = 4,
  ...
)

Arguments

Value

An object of class econ_did containing the DiD effect, 2x2 table, and estimates.

Examples

set.seed(42)
n <- 400
state <- sample(1:20, n, replace = TRUE)
treat <- ifelse(state <= 10, 1, 0)
post <- sample(0:1, n, replace = TRUE)
x1 <- rnorm(n)
y <- 10 + 2 * treat + 1.5 * post + 3.2 * (treat * post) + 0.5 * x1 + rnorm(n, sd = 2)
df <- data.frame(y, treat, post, state, x1)
did(y ~ treat * post + x1, data = df, cluster = "state")

Durbin's h Test for Autocorrelation in Autoregressive Models

Description

Tests for first-order autocorrelation in dynamic models containing a lagged dependent variable, where the standard Durbin-Watson d test is biased toward 2 (Durbin, 1970).

Usage

durbin_h(model, lag_var = NULL, digits = 4)

Arguments

Value

An object of class econ_durbin_h.

Examples

data(macro_data, package = "phoring")
n <- nrow(macro_data)
macro_data$inf_lag <- c(NA, macro_data$inflation[-n])
m <- reg(inflation ~ inf_lag + unemployment, data = macro_data[-1, ])
durbin_h(m, lag_var = "inf_lag")

Dynamic Econometric Models: Koyck, Durbin's h, Prais-Winsten, ECM, and VAR

Description

Implements dynamic time series models, autoregressive distributed lags, serial correlation corrections, error correction models, and vector autoregressions.

Error Correction Model (ECM)

Description

Estimates an Error Correction Model based on the Granger Representation Theorem (Engle and Granger, 1987), capturing both short-run dynamic responses and the speed of adjustment towards long-run cointegrating equilibrium.

Usage

ecm(formula, data, lags = 1, digits = 4)

Arguments

Value

An object of class econ_ecm.

Examples

data(macro_data, package = "phoring")
ecm(inflation ~ unemployment + fed_funds, data = macro_data, lags = 1)

Academic Publication-Quality Econometric Diagnostic Plots

Description

Generates clean, publication-grade academic diagnostic plots with serif typography, L-frame axes, and standardized econometric styling across all models in the package.

Usage

econ_plot(
  x,
  which = c("all", "resid_fitted", "qq", "scale_location", "cooks", "leverage",
    "added_var", "component_resid", "dfbetas", "forest", "vif", "histogram",
    "observed_fitted", "cusum", "cusum_sq", "cusum_squares", "stability", "influence",
    "influence_plot"),
  ...
)

## S3 method for class 'econ_reg'
plot(
  x,
  y = NULL,
  which = c("all", "resid_fitted", "qq", "scale_location", "cooks", "leverage",
    "added_var", "component_resid", "dfbetas", "forest", "vif", "histogram",
    "observed_fitted", "cusum", "cusum_sq", "cusum_squares", "stability", "influence",
    "influence_plot"),
  ...
)

Arguments

Value

Invisibly returns x.

Examples

data(wage_data, package = "phoring")
mod <- reg(wage ~ educ + exper, data = wage_data)
econ_plot(mod)
plot(mod, which = "leverage")
plot(mod, which = "cusum_sq")

Corporate Investment Panel Dataset (Grunfeld Style)

Description

A balanced panel dataset of 40 manufacturing firms observed annually over 5 years (2019-2023), totaling 200 observations. Modeled after the classic Grunfeld investment panel. Ideal for Fixed Effects (Within), Random Effects (GLS), and the Hausman specification test.

Usage

firm_investment

Format

A data frame with 200 rows and 5 variables:

firm_id

Firm identifier (1 to 40)

year

Year of observation (2019 to 2023)

inv

Gross corporate capital investment

value

Market value of the firm

capital

Real value of plant and equipment capital stock

Source

Modeled after Grunfeld (1958) corporate investment panel.

Goldfeld-Quandt Test for Heteroskedasticity

Description

Tests for heteroskedasticity by ordering the sample according to a regressor, omitting central observations, and testing whether the error variance differs significantly between the lower and upper subsamples (Goldfeld and Quandt, 1965).

Usage

gq_test(formula, split_by = NULL, data = NULL, omit_fraction = 0.2, digits = 4)

Arguments

Value

An object of class econ_gq.

Examples

data(wage_data, package = "phoring")
gq_test(wage ~ educ + exper, split_by = "educ", data = wage_data)

Bidirectional Granger Causality Test

Description

Tests whether past values of X forecast Y, and whether past values of Y forecast X.

Usage

granger(formula, data, lags = 2, digits = 4)

Arguments

Value

An object of class econ_granger.

Examples

data(macro_data, package = "phoring")
granger(inflation ~ fed_funds, data = macro_data, lags = 2)

Hedonic Housing Prices and Urban Environmental Quality

Description

A cross-sectional dataset of 506 housing tracts modeled after the classic Boston housing study (Harrison & Rubinfeld, 1978). Ideal for multiple regression, heteroskedasticity testing (Breusch-Pagan, White), and Ramsey RESET functional form tests.

Usage

house_prices

Format

A data frame with 506 rows and 7 variables:

price

Median value of owner-occupied homes in $1,000s

rooms

Average number of rooms per dwelling

crime

Per capita crime rate by town

nox

Nitric oxides concentration (parts per 10 million)

dist

Weighted distances to five employment centres

tax

Full-value property-tax rate per $10,000

ptratio

Pupil-teacher ratio by town school district

Source

Modeled after Harrison and Rubinfeld (1978) hedonic housing study.

Plain-English Econometric Model Interpretation

Description

Automatically inspects regression functional forms (log-log, log-level, level-log, level-level) and provides human-readable, plain-English economic interpretations for every coefficient, including elasticities, semi-elasticities, percentage effects, and quadratic turning points.

Usage

interpret(model, digits = 4)

Arguments

Value

An object of class econ_interpret containing interpretation summaries.

Examples

data(wage_data, package = "phoring")
# Quadratic wage equation
mod <- reg(wage ~ educ + exper + I(exper^2), data = wage_data)
interpret(mod)

# Log-log elasticity model
data(house_prices, package = "phoring")
mod_log <- reg(log(price) ~ log(rooms) + crime, data = house_prices)
interpret(mod_log)

Instrumental Variables (2SLS) Regression with Automated Diagnostics

Description

Fits a Two-Stage Least Squares (2SLS) instrumental variables regression model, calculates robust or clustered standard errors, and automatically runs diagnostic tests (Weak Instruments First-Stage F, Wu-Hausman Endogeneity, and Sargan Overidentifying Restrictions).

Usage

iv(
  formula,
  data,
  se = c("robust", "standard", "cluster"),
  cluster = NULL,
  digits = 4,
  ...
)

Arguments

Value

An object of class econ_iv containing 2SLS estimates, diagnostics, and standard errors.

Examples

data(wage_data, package = "phoring")
iv(wage ~ exper + educ | exper + mother_educ, data = wage_data)
iv(wage ~ exper | educ ~ mother_educ + father_educ, data = wage_data)

Davidson-MacKinnon J-Test for Non-Nested Models

Description

Tests competing non-nested econometric models (neither model is a subset of the other), following the specification testing framework of Davidson and MacKinnon (1981).

Usage

j_test(model1, model2, data = NULL, digits = 4)

Arguments

Value

An object of class econ_jtest.

Examples

data(wage_data, package = "phoring")
# Model 1: Education & Experience vs. Model 2: Tenure & Mother's Education
m1 <- reg(wage ~ educ + exper, data = wage_data)
m2 <- reg(wage ~ tenure + mother_educ, data = wage_data)
j_test(m1, m2)

Koyck Distributed Lag Model

Description

Estimates geometric distributed lag models using the Koyck transformation (Koyck, 1954), computing the Short-Run Multiplier, Long-Run Multiplier, Rate of Decay, Median Lag, and Mean Lag.

Usage

koyck(formula, data, method = c("ols", "iv"), digits = 4)

Arguments

Value

An object of class econ_koyck.

Examples

data(macro_data, package = "phoring")
koyck(inflation ~ unemployment, data = macro_data, method = "ols")
koyck(inflation ~ unemployment, data = macro_data, method = "iv")

Female Labor Force Participation Dataset (Mroz Style)

Description

A microeconometric cross-section of 753 married women modeled after Mroz (1987). Ideal for binary choice estimation via Logit, Probit, and computing Average Marginal Effects (AME).

Usage

labor_supply

Format

A data frame with 753 rows and 7 variables:

inlf

Binary indicator: 1 if woman worked in the labor force, 0 otherwise

nwifeinc

Non-wife family household income (in $1,000s)

educ

Years of schooling

exper

Years of past work experience

age

Age in years

kidslt6

Number of young children under 6 years old

kidsge6

Number of older children between 6 and 18 years old

hours

Annual hours worked (0 for non-working women, ideal for Tobit)

docvis

Annual doctor visits (integer count, ideal for Poisson regression)

Source

Modeled after Mroz (1987), "The Sensitivity of an Empirical Model of Married Women's Hours of Work".

Binary Choice Models (Logit and Probit) with Marginal Effects

Description

Fits logistic regression (Logit) or probit regression (Probit) models, computes robust Huber-White or clustered standard errors, reports odds ratios (for logit), ROC-AUC, classification accuracy, and calculates Average Marginal Effects (AME) with delta-method standard errors.

Usage

logit(
  formula,
  data,
  se = c("robust", "standard", "cluster"),
  cluster = NULL,
  digits = 4,
  ...
)

probit(
  formula,
  data,
  se = c("robust", "standard", "cluster"),
  cluster = NULL,
  digits = 4,
  ...
)

Arguments

Value

An object of class econ_logit containing index estimates, odds ratios, average marginal effects (AME), and model diagnostics.

Examples

data(mtcars)
logit(am ~ mpg + hp, data = mtcars)
probit(am ~ mpg + hp, data = mtcars)

Quarterly Macroeconomic Time Series (Phillips Curve)

Description

A quarterly macroeconomic time series dataset spanning 120 quarters (1994Q1 to 2023Q4). Designed for time series regressions, serial correlation testing (Durbin-Watson, Breusch-Godfrey), and Newey-West HAC standard errors.

Usage

macro_data

Format

A data frame with 120 rows and 5 variables:

quarter

Calendar quarter (e.g., "1994Q1" to "2023Q4")

inflation

Annualized CPI inflation rate (percentage)

unemployment

Civilian unemployment rate (percentage)

fed_funds

Federal funds effective interest rate (percentage)

gdp_growth

Annualized real GDP growth rate (percentage)

Source

Simulated based on US macroeconomic time series dynamics.

State Minimum Wage Policy Evaluation (Card & Krueger DiD)

Description

A Difference-in-Differences evaluation dataset of 400 fast-food restaurants in New Jersey (treated state) and eastern Pennsylvania (control state) observed before and after New Jersey's minimum wage increase.

Usage

min_wage

Format

A data frame with 400 rows and 5 variables:

store_id

Store identifier (1 to 400)

chain

Fast-food chain name (Burger King, KFC, Roy Rogers, Wendys)

treated

State indicator: 1 if New Jersey (treated), 0 if Pennsylvania (control)

post

Time period: 1 if after the wage hike, 0 if before

fte

Full-time equivalent employment at the restaurant

Source

Modeled after Card and Krueger (1994) minimum wage study.

Detect Outliers and Influential Observations in Regression Models

Description

Automatically computes Cook's Distance, studentized residuals, and leverage (hat values) for regression models, flagging influential data points that could distort econometric estimates.

Usage

outliers(model, top_n = 5, digits = 4)

Arguments

Value

An object of class econ_outliers.

Examples

data(wage_data, package = "phoring")
mod <- reg(wage ~ educ + exper, data = wage_data)
outliers(mod)

Comprehensive Panel Data Econometrics with Two-Way FE and Specification Tests

Description

Fits panel data models (Fixed Effects, Two-Way Fixed Effects, Random Effects, First Differences, Between Estimator, or Pooled OLS) with customizable standard errors (clustered by entity id, White robust, or classical standard) and automatically executes the Hausman test, Breusch-Pagan LM test, and Wooldridge panel autocorrelation test.

Usage

panel(
  formula,
  data,
  id,
  time = NULL,
  model = c("fe", "twfe", "re", "fd", "between", "pooling"),
  se = c("cluster", "robust", "standard"),
  digits = 4,
  ...
)

Arguments

Value

An object of class econ_panel containing estimates, standard errors, and panel diagnostics.

Examples

data(firm_investment, package = "phoring")
# 1. Entity Fixed Effects (Within)
panel(inv ~ value + capital, data = firm_investment, id = "firm_id", time = "year", model = "fe")

# 2. Two-Way Fixed Effects (Entity + Time FE)
panel(inv ~ value + capital, data = firm_investment, id = "firm_id", time = "year", model = "twfe")

# 3. Random Effects (GLS)
panel(inv ~ value + capital, data = firm_investment, id = "firm_id", time = "year", model = "re")

Plot Autoregressive Conditional Heteroskedasticity (ARCH) Diagnostics

Description

Generates an academic publication-grade ARCH volatility suite: (1) "residuals": Raw regression residuals, (2) "squared_residuals": Squared residuals e_t^2 (volatility clustering), (3) "acf_sq": Autocorrelations of squared residuals, (4) "dist": Residual histogram with fitted normal curve, (5) "qq": Normal Q-Q plot of residuals, (6) "rolling_sd": Rolling standard deviation of residuals over time.

Usage

## S3 method for class 'econ_arch'
plot(
  x,
  y = NULL,
  which = c("all", "residuals", "squared_residuals", "acf_sq", "dist", "qq",
    "rolling_sd"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Autoregressive Distributed Lag (ARDL) Model Diagnostics

Description

Generates an academic publication-grade ARDL dynamics suite: (1) "all": 6-panel comprehensive academic dashboard (Actual vs. Fitted, Residuals, ACF, Multipliers, CUSUM, CUSUM of Squares), (2) "fit": Actual vs. fitted trajectories over time, (3) "residuals": ARDL residuals over time, (4) "acf": Autocorrelation of residuals, (5) "multipliers": Short-run impact vs. long-run multiplier bar comparison, (6) "cusum": CUSUM parameter stability test with 5% critical bounds, (7) "cusum_sq" / "cusum_squares": Brown-Durbin-Evans CUSUM of squares variance & parameter stability test with 5% critical bounds, (8) "stability": Side-by-side CUSUM and CUSUM of squares stability panel, (9) "bounds": Pesaran-Shin-Smith (2001) Bounds Testing cointegration diagnostic diagram, (10) "cumulative_multipliers": Cumulative interim dynamic multiplier curve.

Usage

## S3 method for class 'econ_ardl'
plot(
  x,
  y = NULL,
  which = c("all", "fit", "residuals", "acf", "multipliers", "cusum", "cusum_sq",
    "cusum_squares", "stability", "bounds", "cumulative_multipliers"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Chow Structural Break Test

Description

Generates an academic publication-grade Chow structural stability suite: (1) "scatter": Outcome vs. predictor scatter showing separate subgroup regression slopes vs. pooled line, (2) "f_stat": Chow F-statistic comparison vs. 5% critical F value.

Usage

## S3 method for class 'econ_chow'
plot(x, y = NULL, which = c("all", "scatter", "f_stat"), ...)

Arguments

Value

Invisibly returns x.

Plot Cointegration and Long-Run Equilibrium Diagnostics

Description

Generates an academic publication-grade cointegration diagnostic suite: (1) "comovement": Normalized series comovement overlay, (2) "residuals": Cointegrating error-correction residuals with MacKinnon boundary, (3) "acf_residuals": Autocorrelation of cointegrating residuals, (4) "scatter_regression": Scatter of outcome vs. predictor with long-run regression line.

Usage

## S3 method for class 'econ_coint'
plot(
  x,
  y = NULL,
  which = c("all", "comovement", "residuals", "acf_residuals", "scatter_regression"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Correlation Matrix Heatmap

Description

Displays an academic publication-grade correlation matrix heatmap with serif typography, numeric values overlaid, and significance indicators.

Usage

## S3 method for class 'econ_corr'
plot(x, y = NULL, which = c("all", "heatmap"), ...)

Arguments

Value

Invisibly returns x.

Plot Count Model Diagnostics (Poisson / NegBin)

Description

Generates an academic publication-grade count regression diagnostic suite: (1) "rootogram": Kleiber-Zeileis hanging rootogram of observed vs. expected count frequencies, (2) "freq_bars": Grouped bar comparison of observed vs. expected counts, (3) "mean_variance": Binned mean vs. variance curve (testing equidispersion), (4) "residuals": Pearson residuals vs. fitted counts, (5) "irr_forest": Incidence Rate Ratio (IRR) forest plot \exp(\beta) \pm 95\% CI.

Usage

## S3 method for class 'econ_count'
plot(
  x,
  y = NULL,
  which = c("all", "rootogram", "freq_bars", "mean_variance", "residuals", "irr_forest"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Descriptive Distributions

Description

Generates an academic publication-grade distribution grid for numeric variables in descriptive summaries: (1) "histogram": Histograms with fitted kernel density curves, (2) "boxplot": Standardized boxplots across variables.

Usage

## S3 method for class 'econ_desc'
plot(x, y = NULL, which = c("all", "histogram", "boxplot"), ...)

Arguments

Value

Invisibly returns x.

Plot Covariate Balance Love Plot

Description

Generates an academic publication-grade Love plot showing standardized mean differences (normalized differences) between treatment and control groups alongside the \pm 0.10 balance threshold.

Usage

## S3 method for class 'econ_desc_balance'
plot(x, y = NULL, which = c("all", "love"), ...)

Arguments

Value

Invisibly returns x.

Plot Comprehensive Econometric Diagnostics Dashboard

Description

Generates an academic publication-grade summary dashboard of diagnostic test results: (1) "pvalues": Horizontal bar chart of -\log_{10}(p\text{-value}) for all specification tests (RESET, Breusch-Pagan, White, Jarque-Bera, Durbin-Watson, Breusch-Godfrey) with the 0.05 critical boundary, (2) "vif": Variance Inflation Factors (VIF) bar chart with 5 and 10 multicollinearity reference lines.

Usage

## S3 method for class 'econ_diagnostics'
plot(x, y = NULL, which = c("all", "pvalues", "vif"), ...)

Arguments

Value

Invisibly returns x.

Plot Difference-in-Differences (DiD) Trajectories and Diagnostics

Description

Generates an academic publication-grade DiD suite: (1) "trajectory": 2x2 Pre/Post trajectories with treatment effect counterfactual, (2) "means_ci": 2x2 Cell group means with 95% confidence intervals, (3) "comparison": DiD estimate comparison vs. naive post-only and pre/post estimates, (4) "event_study": Group averages over calendar time and event-study dynamics.

Usage

## S3 method for class 'econ_did'
plot(
  x,
  y = NULL,
  which = c("all", "trajectory", "means_ci", "comparison", "event_study"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Durbin's h Autoregressive Serial Correlation Test

Description

Displays standard normal null density with rejection regions and Durbin's h test statistic.

Usage

## S3 method for class 'econ_durbin_h'
plot(x, y = NULL, which = c("all", "density"), ...)

Arguments

Value

Invisibly returns x.

Plot Error Correction Model (ECM) Diagnostics

Description

Generates an academic publication-grade ECM dynamics suite: (1) "fit": Actual vs. fitted first differences \Delta y_t, (2) "residuals": ECM dynamic residuals, (3) "decay_path": Speed of adjustment disequilibrium decay path (1 + \alpha)^h, (4) "acf_residuals": Autocorrelation function of ECM residuals, (5) "cusum": CUSUM parameter stability test with 5% bounds, (6) "cusum_sq": CUSUM of squares stability test with 5% bounds, (7) "stability": Side-by-side CUSUM and CUSUMSQ stability panel.

Usage

## S3 method for class 'econ_ecm'
plot(
  x,
  y = NULL,
  which = c("all", "fit", "residuals", "decay_path", "acf_residuals", "cusum",
    "cusum_sq", "cusum_squares", "stability"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Goldfeld-Quandt Heteroskedasticity Test

Description

Generates an academic publication-grade Goldfeld-Quandt diagnostic plot: (1) "residuals": Residuals ordered by the sorting variable, highlighting the two subsamples and omitted central observations, (2) "distribution": Theoretical F-distribution with the GQ test statistic and rejection threshold.

Usage

## S3 method for class 'econ_gq'
plot(x, y = NULL, which = c("all", "residuals", "distribution"), ...)

Arguments

Value

Invisibly returns x.

Plot Granger Causality Diagnostics

Description

Generates an academic publication-grade Granger causality suite: (1) "timeseries": Standardized time series trajectories, (2) "ccf": Cross-correlation function (CCF) across leads and lags, (3) "f_stats": Bidirectional F-statistic comparison vs. 5% critical F value.

Usage

## S3 method for class 'econ_granger'
plot(x, y = NULL, which = c("all", "timeseries", "ccf", "f_stats"), ...)

Arguments

Value

Invisibly returns x.

Plot General Linear Hypothesis & Wald Test

Description

Displays the theoretical null distribution (F or Chi-Square) with the rejection region and Wald statistic.

Usage

## S3 method for class 'econ_hyp'
plot(x, y = NULL, which = c("all", "density"), ...)

Arguments

Value

Invisibly returns x.

Plot Instrumental Variables 2SLS Model

Description

Generates an academic publication-grade 2SLS identification suite: (1) "first_stage": First-Stage Fit: Endogenous X vs. Instrument Z with F-stat, (2) "second_stage": Second Stage: Outcome Y vs. Predicted Endogenous \hat{X}, (3) "residuals": 2SLS Residuals vs. Fitted values, (4) "orthogonality": Instrument Exogeneity Check: Residuals vs. Instrument Z, (5) "forest_compare": OLS vs. 2SLS Coefficient Comparison Forest Plot, (6) "f_stat": First-stage F-statistic vs. Stock-Yogo rule of thumb (F = 10), (7) "reduced_form": Reduced form regression of Y on Z.

Usage

## S3 method for class 'econ_iv'
plot(
  x,
  y = NULL,
  which = c("all", "first_stage", "second_stage", "residuals", "orthogonality",
    "forest_compare", "f_stat", "reduced_form"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Davidson-MacKinnon J-Test

Description

Generates an academic publication-grade J-test comparison bar chart showing the t-statistics for competing non-nested models against the \pm 1.96 critical thresholds.

Usage

## S3 method for class 'econ_jtest'
plot(x, y = NULL, which = c("all", "bars"), ...)

Arguments

Value

Invisibly returns x.

Plot Koyck Distributed Lag Multipliers and Diagnostics

Description

Generates an academic publication-grade Koyck distributed lag suite: (1) "lag_weights": Geometric lag decay weights across horizons, (2) "cumulative": Cumulative interim multipliers approaching long-run effect, (3) "actual_fitted": Actual vs. fitted values over time, (4) "acf_residuals": Autocorrelation function of Koyck residuals.

Usage

## S3 method for class 'econ_koyck'
plot(
  x,
  y = NULL,
  which = c("all", "lag_weights", "cumulative", "actual_fitted", "acf_residuals",
    "cusum", "cusum_sq", "cusum_squares", "stability"),
  horizons = 12,
  ...
)

Arguments

Value

Invisibly returns x.

Plot Binary Outcome (Logit/Probit) Model Diagnostics

Description

Generates an academic publication-grade classification and link diagnostics suite: (1) "scurve": S-Curve sigmoid probability curve vs. linear predictor \eta, (2) "roc": Receiver Operating Characteristic (ROC) curve with AUC annotation, (3) "distribution": Class separation boxplots of predicted probabilities by observed class, (4) "residuals": Deviance residuals vs. predicted probabilities, (5) "calibration": Hosmer-Lemeshow decile calibration plot with 45-degree reference line, (6) "thresholds": Sensitivity & Specificity curves vs. cutoff with Youden's optimal threshold, (7) "marginal": Marginal probability curve across continuous predictor, (8) "odds_ratio": Odds ratios forest plot (for logit) with 95% CIs on log scale, (9) "confusion": Confusion matrix classification tile heatmap at threshold 0.5.

Usage

## S3 method for class 'econ_logit'
plot(
  x,
  y = NULL,
  which = c("all", "scurve", "roc", "distribution", "residuals", "calibration",
    "thresholds", "marginal", "odds_ratio", "confusion"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Influential Outliers & Leverage Diagnostics

Description

Generates an academic publication-grade outlier influence suite: (1) "influence": Influence bubble plot (Studentized residuals vs. Leverage, bubble size = Cook's D), (2) "cooks": Cook's distance index barplot with 4/n threshold.

Usage

## S3 method for class 'econ_outliers'
plot(x, y = NULL, which = c("all", "influence", "cooks"), ...)

Arguments

Value

Invisibly returns x.

Plot Panel Data Regression Diagnostics

Description

Generates an academic publication-grade panel diagnostics suite or individual plots: (1) "between": Between-entity variation in group means with between regression line, (2) "within": Within-entity demeaned scatter with within regression slope, (3) "spaghetti": Entity trajectories over time with mean trend, (4) "fe_dist": Distribution of estimated individual fixed effects \hat{\alpha}_i, (5) "heterogeneity": Individual entity residual boxplots, (6) "residuals": Panel residuals vs. fitted values with LOESS smoother, (7) "ar_residual": Residual vs. lagged within-entity residual e_{i,t} vs e_{i,t-1} (Wooldridge AR(1) test), (8) "time_effects": Period mean residuals \pm 95\% CI across time.

Usage

## S3 method for class 'econ_panel'
plot(
  x,
  y = NULL,
  which = c("all", "between", "within", "spaghetti", "fe_dist", "heterogeneity",
    "residuals", "ar_residual", "time_effects"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Prais-Winsten / Cochrane-Orcutt AR(1) Diagnostics

Description

Generates an academic publication-grade serial correlation correction suite: (1) "fitted": Actual vs. FGLS fitted values, (2) "residuals": Transformed quasi-differenced residuals, (3) "acf": Autocorrelation function of transformed residuals, (4) "ar_scatter": e_t vs. e_{t-1} scatter comparing OLS vs. FGLS residuals with \hat{\rho} slope.

Usage

## S3 method for class 'econ_prais'
plot(
  x,
  y = NULL,
  which = c("all", "fitted", "residuals", "acf", "ar_scatter", "cusum", "cusum_sq",
    "cusum_squares", "stability"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Quantile Regression Process

Description

Generates an academic publication-grade quantile process suite: (1) "process": Coefficient path \hat{\beta}(\tau) across quantiles \tau with 95% pointwise CI band, alongside the horizontal OLS benchmark estimate and 95% confidence interval, (2) "loss": Total check-loss across quantiles \tau, (3) "fitted_scatter": Scatter of outcome vs. predictor with fitted quantile lines.

Usage

## S3 method for class 'econ_qreg'
plot(
  x,
  y = NULL,
  var = NULL,
  which = c("all", "process", "loss", "fitted_scatter"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Sharp Regression Discontinuity (RDD) Diagnostics

Description

Generates an academic publication-grade RDD identification suite: (1) "binned_rd": Binned RD scatter plot with local polynomial fits and 95% confidence bands, (2) "density": Running variable density histogram with discontinuity check around cutoff, (3) "bandwidth_sensitivity": Point estimate \hat{\tau} \pm 95\% CI across bandwidths, (4) "placebo": Placebo cutoff falsification test.

Usage

## S3 method for class 'econ_rdd'
plot(
  x,
  y = NULL,
  which = c("all", "binned_rd", "density", "bandwidth_sensitivity", "placebo"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Tobit Censored Regression Diagnostics

Description

Generates an academic publication-grade Tobit diagnostic suite: (1) "observed_hist": Observed Y histogram highlighting censoring pile-up, (2) "pred_observed": Observed Y vs. unconditional predicted E[y], (3) "mcdonald_moffitt": McDonald-Moffitt marginal decomposition (extensive vs. intensive margin), (4) "qq_resid": Normal Q-Q plot of residuals for uncensored observations, (5) "residuals": Censored residuals vs. linear index.

Usage

## S3 method for class 'econ_tobit'
plot(
  x,
  y = NULL,
  which = c("all", "observed_hist", "pred_observed", "mcdonald_moffitt", "qq_resid",
    "residuals"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Unit Root and Stationarity Tests

Description

Generates an academic publication-grade time-series stationarity suite: (1) "levels": Series levels over time, (2) "diffs": First differences over time, (3) "acf_levels": Autocorrelation of levels, (4) "acf_diffs": Autocorrelation of first differences, (5) "pacf": Partial autocorrelations of levels and differences, (6) "dashboard": Test statistic dashboard (ADF, PP, KPSS vs. 5% critical values).

Usage

## S3 method for class 'econ_unit_root'
plot(
  x,
  y = NULL,
  which = c("all", "levels", "diffs", "acf_levels", "acf_diffs", "pacf", "dashboard"),
  ...
)

Arguments

Value

Invisibly returns x.

Plot Vector Autoregression (VAR) Diagnostics and Dynamics

Description

Generates an academic publication-grade VAR dynamics suite: (1) "stability": Inverse roots of companion matrix characteristic polynomial on the complex unit circle, (2) "irf": Orthogonalized Impulse Response Functions (IRF) grid across horizons, (3) "fevd": Forecast Error Variance Decomposition (FEVD) stacked bars, (4) "residuals": Residuals across equations over time, (5) "correlation": Innovation covariance/correlation heatmap, (6) "acf": Autocorrelation function of residuals for each equation, (7) "granger": Granger causality p-value heatmap matrix.

Usage

## S3 method for class 'econ_var'
plot(
  x,
  y = NULL,
  which = c("all", "stability", "irf", "fevd", "residuals", "correlation", "acf",
    "granger"),
  horizons = 10,
  ...
)

Arguments

Value

Invisibly returns x.

Prais-Winsten & Cochrane-Orcutt FGLS Autocorrelation Correction

Description

Fits Feasible Generalized Least Squares (FGLS) to correct for first-order AR(1) serial correlation (Prais and Winsten, 1954; Cochrane and Orcutt, 1949), using Prais-Winsten (retaining the first observation) or Cochrane-Orcutt transformation until convergence.

Usage

prais_winsten(
  formula,
  data,
  method = c("prais", "cochrane"),
  max_iter = 50,
  tol = 1e-04,
  digits = 4
)

Arguments

Value

An object of class econ_prais.

Examples

data(macro_data, package = "phoring")
prais_winsten(inflation ~ fed_funds + unemployment, data = macro_data)

Quantile Regression Across the Distribution

Description

Estimates quantile regressions for single or multiple conditional quantiles (e.g., 10th, 25th, median 50th, 75th, 90th percentiles), allowing researchers to analyze how economic covariates impact different parts of the outcome distribution beyond the conditional mean.

Usage

qreg(
  formula,
  data,
  tau = c(0.1, 0.25, 0.5, 0.75, 0.9),
  se = c("robust", "classical"),
  digits = 4
)

Arguments

Value

An object of class econ_qreg.

Examples

data(wage_data, package = "phoring")
# Estimate median regression (tau = 0.5)
qreg(wage ~ educ + exper, data = wage_data, tau = 0.5)

# Estimate full distribution quantiles (10%, 25%, 50%, 75%, 90%)
q_all <- qreg(wage ~ educ + exper, data = wage_data, tau = c(0.25, 0.5, 0.75))

Sharp Regression Discontinuity Design (RDD)

Description

Estimates causal treatment effects at a policy cutoff using Sharp Regression Discontinuity (SRD) with local linear regression, optimal bandwidth selection, triangular/uniform kernel weights, and robust standard errors.

Usage

rdd(
  formula,
  data,
  cutoff = 0,
  bandwidth = NULL,
  kernel = c("triangular", "uniform"),
  digits = 4
)

Arguments

Value

An object of class econ_rdd.

Examples

set.seed(42)
n <- 500
x <- runif(n, -2, 2)
d <- as.numeric(x >= 0)
y <- 2 + 3 * d + 1.5 * x + 0.8 * d * x + rnorm(n, sd = 0.5)
df <- data.frame(y = y, score = x)
rdd(y ~ score, data = df, cutoff = 0)

One-Command Econometric Regression with Automated Diagnostics

Description

Fits an Ordinary Least Squares (OLS) or Weighted Least Squares (WLS) regression model, calculates robust (HC0-HC3), clustered, or HAC standard errors, and automatically runs and prints a complete battery of econometric diagnostic tests.

Usage

reg(
  formula,
  data,
  se = c("robust", "standard", "cluster", "hac", "HC0", "HC1", "HC2", "HC3"),
  cluster = NULL,
  lag = NULL,
  weights = NULL,
  digits = 4,
  diagnostics = TRUE,
  ...
)

Arguments

Value

An object of class econ_reg containing the regression results, standard errors, coefficient table, model statistics, and diagnostic tests.

Examples

data(mtcars)
reg(mpg ~ wt + hp + qsec, data = mtcars)
reg(mpg ~ wt + hp, data = mtcars, cluster = "cyl")

General Linear Hypothesis & Wald F-Test

Description

Tests linear hypotheses of model parameters using the robust or clustered covariance matrix from the estimated model. Supports interaction terms, factor levels, polynomial terms, and multi-parameter restrictions.

Usage

test_hyp(model, hypothesis, ...)

Arguments

Value

An object of class econ_hyp containing the Wald test statistic, degrees of freedom, and p-value.

Examples

data(mtcars)
m <- reg(mpg ~ wt + hp + qsec, data = mtcars)
test_hyp(m, "wt + hp = 0")
test_hyp(m, c("wt = 0", "hp = 0"))

Complete Time Series Econometric Toolkit

Description

Provides a comprehensive suite for time series econometrics: 1. 'unit_root()': Multi-test stationarity battery (ADF, Phillips-Perron, KPSS). 2. 'coint()': Engle-Granger two-step cointegration test. 3. 'granger()': Bidirectional Granger causality test. 4. ‘arch_test()': Engle’s ARCH LM test for volatility clustering. 5. 'ardl()': Autoregressive Distributed Lag model with short-run and long-run multipliers.

Censored Regression (Tobit Model) with McDonald-Moffitt Decomposition

Description

Fits a Tobit regression model (left-censored at 0 or a user-specified threshold), calculates robust or standard standard errors, and automatically produces the McDonald-Moffitt decomposition of marginal effects.

Usage

tobit(formula, data, left = 0, se = c("robust", "standard"), digits = 4)

Arguments

Value

An object of class econ_tobit.

Examples

data(labor_supply, package = "phoring")
labor_supply$hours <- pmax(labor_supply$hours, 0)
tobit(hours ~ educ + exper + age, data = labor_supply, left = 0)

Multi-Test Stationarity Battery (ADF, Phillips-Perron, KPSS)

Description

Executes Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and KPSS tests simultaneously with MacKinnon response surface p-values.

Usage

unit_root(x, lags = NULL, type = c("trend", "drift", "none"), digits = 4)

Arguments

Value

An object of class econ_unit_root.

Examples

data(macro_data, package = "phoring")
unit_root(macro_data$inflation, type = "trend")
unit_root(macro_data$unemployment, type = "drift")

Vector Autoregression (VAR) Model

Description

Fits a Vector Autoregression system (Sims, 1980) for multiple time series, computing system equation estimates, information criteria (AIC, BIC), and pairwise Granger causality summary matrices.

Usage

var_model(data, vars, p = 1, digits = 4)

Arguments

Value

An object of class econ_var.

Examples

data(macro_data, package = "phoring")
var_model(macro_data, vars = c("inflation", "unemployment", "fed_funds"), p = 1)

Cross-Sectional and Panel Wage Determination Dataset

Description

A simulated labor economics dataset modeled after the Mincerian wage equation and empirical labor economics literature. Includes individual characteristics, parental education instruments, firm identifiers for clustering, and panel time periods.

Usage

wage_data

Format

A data frame with 600 observations and 11 variables:

wage

Hourly wage in US dollars

educ

Years of education

exper

Years of labor market experience

tenure

Years with current employer

female

Indicator variable: 1 if female, 0 if male

married

Indicator variable: 1 if married, 0 otherwise

mother_educ

Mother's years of education (instrument for educ)

father_educ

Father's years of education (instrument for educ)

firm_id

Firm identifier for clustering and panel analysis

year

Year of observation (2020-2023)

inlf

Indicator variable: 1 if in labor force / high wage, 0 otherwise

Source

Simulated based on standard labor econometrics specifications.

Side-by-Side Model Comparison & Word Table Export

Description

Compiles multiple econometric models into a single publication-quality table with standard errors in parentheses and significance stars, and exports directly to Microsoft Word (.docx/.doc), HTML, or Markdown.

Usage

word_table(
  ...,
  labels = NULL,
  file = NULL,
  title = "Regression Results",
  format = c("text", "html", "word"),
  digits = 3
)

Arguments

Value

A list of the input models (invisibly).

Examples

data(mtcars)
m1 <- reg(mpg ~ wt, data = mtcars)
m2 <- reg(mpg ~ wt + hp, data = mtcars)
word_table(m1, m2, labels = c("Simple OLS", "Multiple OLS"))