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phoring

A Comprehensive Econometric Estimation and Diagnostic Suite for R
Developed by Md. Abid Siddique Emon.
Unified formula-based estimation, robust covariance structures, automated specification testing, economic interpretation, and publication-ready academic visualizations.

Overview

In conventional R workflows, estimating an econometric model and conducting comprehensive diagnostic testing requires loading and coordinating multiple separate packages (sandwich, lmtest, car, plm, AER, tseries, quantreg):

# Conventional fragmented workflow (requiring multiple packages):
library(sandwich); library(lmtest); library(car); library(tseries); library(plm)
mod <- lm(wage ~ educ + exper + tenure, data = df)
coeftest(mod, vcov = vcovHC(mod, type = "HC1"))
bptest(mod)
vif(mod)
dwtest(mod)
resettest(mod)

With phoring, standard formula syntax delivers full coefficient estimates, robust and clustered standard errors, comprehensive diagnostic test batteries, economic interpretation guidance, and publication-ready tables in a single call:

library(phoring)

# Estimate regression with robust standard errors and automated diagnostics:
reg(wage ~ educ + exper + tenure, data = wage_data)

1. Plain-English Interpretation & Diagnostic Tools

A. Plain-English Model Interpretation (interpret)

Auto-detects functional forms (Log-Log elasticity, Log-Level semi-elasticity, Level-Log, Level-Level) and solves for quadratic turning points \(x^* = -\beta_1 / (2\beta_2)\):

data(wage_data)

# Quadratic Mincer equation:
mod <- reg(wage ~ educ + exper + I(exper^2), data = wage_data)
interpret(mod)
# Automatically reports:
# * Shape: Inverted U-shaped (Maximum)
# * Turning point: exper* = 28.4 years (within sample range [1, 45])
# * Insight: Experience increases wage up to 28.4 years, after which diminishing returns dominate.

# Log-Log Constant Elasticity Model:
data(house_prices)
mod_log <- reg(log(price) ~ log(rooms) + crime, data = house_prices)
interpret(mod_log)
# Reports: "A 1% increase in rooms is associated with a 1.25% change in price."

B. Outlier & Influential Observation Detection (outliers)

Computes Cook’s Distance (\(D > 4/n\)), Studentized Residuals (\(|r| > 2.5\)), and Leverage (\(h > 2k/n\)) to flag points distorting estimates:

outliers(mod, top_n = 5)

C. Structural Break / Parameter Stability Test (chow_test)

Tests whether coefficients differ systematically across two subsamples (e.g. before/after policy, male vs. female):

chow_test(wage ~ educ + exper, split_by = "female", data = wage_data)

D. Publication-Grade Academic Visualization Suite (plot & econ_plot)

Zero external graphics dependencies — generates publication-ready figures with serif typography, L-frame axes (bty = "l"), and Oxford navy palette. Every model in the package has dedicated diagnostic visualization supporting individual panel selection via which =:

# Standard regression (13 diagnostic views available):
plot(mod)                           # Academic 2x2 grid (Resid vs Fitted, Q-Q, Scale-Loc, Cook's)
plot(mod, which = "leverage")       # Individual panel: Residuals vs Leverage
plot(mod, which = "added_var")      # Partial regression (added-variable) plots
plot(mod, which = "forest")         # Coefficient forest plot with 95% CIs
plot(mod, which = "cusum")          # Parameter stability CUSUM test

# Specialized model-specific plots:
plot(mod_iv)                        # IV/2SLS: First-stage, instrument strength, Hausman
plot(mod_panel)                     # Panel: Entity FE, within-demeaned, residuals
plot(mod_logit)                     # Logit: ROC curve, calibration, confusion matrix
plot(mod_qreg)                      # Quantile regression: Interquantile coefficient trajectories
plot(mod_did)                       # DiD: Parallel trends, 2x2 treatment trajectory, event study
plot(mod_rdd)                       # RDD: Discontinuity scatter, McCrary density sorting
plot(mod_coint)                     # Cointegration: Long-run comovement & ECM equilibrium
plot(mod_garch)                     # GARCH: Volatility clustering & conditional sigma

E. Non-Nested Model Selection: Davidson-MacKinnon J-Test (j_test)

Tests competing theoretical specifications against each other when neither model is a subset of the other (Davidson and MacKinnon, 1981):

m1 <- reg(wage ~ educ + exper, data = wage_data)
m2 <- reg(wage ~ tenure + mother_educ, data = wage_data)
j_test(m1, m2)

F. Goldfeld-Quandt Heteroskedasticity Test (gq_test)

Orders the sample by a regressor, omits central observations, and tests whether error variance differs across subsamples (Goldfeld and Quandt, 1965):

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

2. Causal Inference & Policy Evaluation

A. Sharp Regression Discontinuity Design (rdd)

Estimates Local Average Treatment Effects (LATE) at policy thresholds using local linear regression with triangular/uniform kernels and McCrary density continuity checks:

# Sharp RDD at cutoff score = 0:
rdd(outcome ~ test_score, data = df, cutoff = 0)

B. Difference-in-Differences (did)

2x2 means breakdown and treatment interaction with clustered standard errors:

data(min_wage)
did(fte ~ treated * post, data = min_wage)

C. Instrumental Variables 2SLS (iv)

Estimates 2SLS with Weak Instrument First-Stage \(F\)-statistic (\(F > 10\)), Wu-Hausman endogeneity test, and Sargan overidentification test:

data(college_distance)
iv(wage ~ exper + tenure + south | educ ~ near_college + mother_educ, data = college_distance)

D. Table 1 Covariate Balance Testing (desc)

Generates publication-ready “Table 1” balance checks with normalized differences and two-sample \(t\)-tests:

desc(wage_data, vars = c("wage", "educ", "exper"), by = "female")

3. Distributional & Quantile Regression (qreg)

Estimate effects across the entire conditional distribution (10th, 25th, median 50th, 75th, 90th percentiles) with robust Huber sandwich standard errors:

data(wage_data)

# Median regression (tau = 0.5):
qreg(wage ~ educ + exper, data = wage_data, tau = 0.5)

# Multi-quantile distribution profile:
qreg(wage ~ educ + exper, data = wage_data, tau = c(0.1, 0.25, 0.5, 0.75, 0.9))

4. Complete Panel Data Econometrics (panel)

Supports all major panel estimators with entity-clustered standard errors by default:

data(firm_investment)

# 1. One-Way Fixed Effects (Within Estimator)
panel(inv ~ value + capital, data = firm_investment, id = "firm_id", time = "year", model = "fe")

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

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

# 4. First Differences (FD)
panel(inv ~ value + capital, data = firm_investment, id = "firm_id", time = "year", model = "fd")

# 5. Between Estimator (Long-Run Group Means)
panel(inv ~ value + capital, data = firm_investment, id = "firm_id", time = "year", model = "between")

Every panel run automatically includes: * Hausman Specification Test: FE vs. RE test (\(p < 0.05 \rightarrow\) Fixed Effects required). * Breusch-Pagan LM Test for Panel: Tests Pooled OLS vs. Random Effects (\(p < 0.05 \rightarrow\) Panel effects present). * Wooldridge Panel AR(1) Test: Tests for first-order serial correlation in errors.

5. Dynamic Models & Time Series Econometrics

data(macro_data)

# 1. Koyck Distributed Lag Model:
# Computes Short-run multiplier, Long-run multiplier, Median lag, and Mean lag:
koyck(inflation ~ unemployment, data = macro_data)

# 2. Durbin's h Test for Dynamic / Autoregressive Models:
# (Standard Durbin-Watson is invalid when lagged Y_{t-1} is a regressor):
m_dyn <- reg(inflation ~ inf_lag + unemployment, data = macro_data)
durbin_h(m_dyn, lag_var = "inf_lag")

# 3. Prais-Winsten & Cochrane-Orcutt FGLS AR(1) Correction:
# Transforms data via rho-differencing to eliminate serial correlation:
prais_winsten(inflation ~ fed_funds + unemployment, data = macro_data)

# 4. Error Correction Model (ECM):
# Estimates short-run adjustments and speed of adjustment (gamma) to equilibrium:
ecm(inflation ~ unemployment + fed_funds, data = macro_data, lags = 1)

# 5. Vector Autoregression (VAR):
# System estimation with AIC/BIC selection and pairwise Granger causality matrix:
var_model(macro_data, vars = c("inflation", "unemployment", "fed_funds"), p = 1)

# 6. Multi-Test Stationarity Battery (ADF + Phillips-Perron + KPSS in 1 table):
unit_root(macro_data$inflation)

# 7. Engle-Granger Two-Step Cointegration Test:
coint(inflation ~ unemployment + fed_funds, data = macro_data)

# 8. Bidirectional Granger Causality Test:
granger(inflation ~ fed_funds, data = macro_data, lags = 2)

# 9. Engle's ARCH LM Test (Volatility Clustering):
m_ts <- reg(inflation ~ fed_funds, data = macro_data, diagnostics = FALSE)
arch_test(m_ts, lags = 2)

6. Complete Microeconometrics & Binary Choice

data(labor_supply)

# 1. Logit with Odds Ratios, ROC-AUC, and Average Marginal Effects (AME):
logit(inlf ~ educ + exper + age + nwifeinc + kidslt6, data = labor_supply)

# 2. Probit with Average Marginal Effects:
probit(inlf ~ educ + exper + age + nwifeinc + kidslt6, data = labor_supply)

# 3. Tobit Censored Regression (with McDonald-Moffitt Decomposition):
tobit(hours ~ educ + exper + age + nwifeinc, data = labor_supply, left = 0)

# 4. Count Data (Poisson Regression with Cameron & Trivedi Overdispersion Test):
count_reg(docvis ~ educ + age + kidslt6, data = labor_supply)

7. Model Comparison & Word Export (word_table)

Compile multiple specifications side-by-side with custom labels and export directly to Microsoft Word (.docx) or HTML:

m1 <- reg(wage ~ educ, data = wage_data, diagnostics = FALSE)
m2 <- reg(wage ~ educ + exper, data = wage_data, diagnostics = FALSE)
m3 <- reg(wage ~ educ + exper + tenure, data = wage_data, diagnostics = FALSE)

# Export to Word with custom column headers:
word_table(m1, m2, m3, 
           labels = c("Bivariate", "+ Experience", "+ Tenure"),
           file = "wage_models.docx", 
           title = "Table 1: Determinants of Wages")

Curated Practice Datasets Included in phoring

Dataset Category Observations What It’s Used For
house_prices Hedonic Pricing / OLS 506 Harrison-Rubinfeld housing values, crime rates, pollution (nox), VIF & RESET tests.
wage_data Labor Economics / OLS 600 Mincer wage equations, experience curvature, gender wage differential, firm clustering.
college_distance Instrumental Variables (2SLS) 800 Card (1995) returns to schooling using college proximity (near_college) as IV. Weak IV & Hausman tests.
firm_investment Panel Data (Micro) 200 (40 \(\times\) 5) Grunfeld corporate investment: FE, TWFE, RE, FD, Hausman test, and BP panel test.
crime_panel Panel Data (Macro/Policy) 200 (50 \(\times\) 4) County crime rates, police presence, and unemployment with entity-clustered SEs.
labor_supply Binary & Censored Micro 753 Mroz (1987) female labor supply (inlf), annual hours (hours for Tobit), and doctor visits (docvis for Poisson).
min_wage Causal Inference / DiD 400 Card & Krueger (1994) NJ & PA fast food minimum wage hike, 2x2 means breakdown.
macro_data Time Series Econometrics 120 quarters US inflation, unemployment, fed funds rate. unit_root(), coint(), granger(), arch_test(), and ardl().

Installation

You can install phoring directly from GitHub:

# install.packages("remotes")
remotes::install_github("abidsiddique/phoring")

Author & Citation

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.