Package {tsqr}

Title:	Sequential Threshold-Spatial-Quantile Panel Estimation
Version:	0.2.0
Description:	Implements a sequential panel estimation protocol for regional economic panels that combines three estimation layers in a fixed order. The first layer applies a two-way fixed effects baseline. The second layer applies the panel threshold regression method of Hansen (1999) <doi:10.1016/S0304-4076(99)00025-1> to identify structural breaks at an unknown threshold of a moderating variable, with bootstrap inference following Hansen (2000) <doi:10.1111/1468-0262.00124>. The third layer applies a spatial Durbin model with an impact decomposition following LeSage and Pace (2009, ISBN:978-1-4200-6424-7) to quantify direct and indirect spillover effects. The fourth layer applies the two-step panel quantile estimator of Canay (2011) <doi:10.1111/j.1368-423X.2011.00349.x> to document distributional heterogeneity in the outcome. The threshold identified in the second layer defines a subsample used as structured input to the fourth layer, and a consistency check evaluates whether the three sets of results are jointly compatible with a common underlying structural relationship. An illustrative panel of 33 districts of the state of Maharashtra, India, observed over 10 agricultural years, is included with the package.
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1.0)
Imports:	plm (≥ 2.6-0), spdep (≥ 1.2-0), spatialreg (≥ 1.2-0), quantreg (≥ 5.90), ggplot2 (≥ 3.4.0), stats, utils
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-07-06 09:56:56 UTC; nehadhanawade
Author:	Prakash Vhankade [aut, cre]
Maintainer:	Prakash Vhankade <prakash.vhankade@gipe.ac.in>
Repository:	CRAN
Date/Publication:	2026-07-06 13:50:02 UTC

Canay Two-Step Panel Quantile Regression

Description

Implements the Canay (2011) two-step panel quantile estimator. In the first step, unit and time fixed effects are estimated from a two-way fixed effects model and subtracted from the outcome. In the second step, standard quantile regression is applied at each requested quantile.

Usage

canay_quantile(
  formula,
  data,
  index,
  taus = c(0.1, 0.25, 0.5, 0.75, 0.9),
  n_boot = 500,
  seed = 42,
  ptr_result = NULL,
  threshold_var = NULL,
  verbose = TRUE
)

Arguments

Value

A list of class canay_quantile with elements coefs, se, slope_tests, gradient, gradient_highstress, fe_corrected, taus, key_var, and call.

References

Canay IA (2011). "A Simple Approach to Quantile Regression for Panel Data." Econometrics Journal, 14(3), 368-386. doi:10.1111/j.1368-423X.2011.00349.x

Examples

data(maharashtra_panel)
result <- canay_quantile(
  formula = ln_GDVA ~ ln_HYV + MCDS_days + Irrigation_pct,
  data    = maharashtra_panel,
  index   = c("district", "year"),
  taus    = c(0.25, 0.50, 0.75),
  n_boot  = 50,
  seed    = 42,
  verbose = FALSE
)
print(result)
coef(result)

Maharashtra Agricultural District Panel Dataset

Description

A balanced panel dataset covering 33 districts of the state of Maharashtra, India, over 10 agricultural years from 2014-15 to 2023-24 (330 observations, 30 variables).

Usage

data(maharashtra_panel)

Format

A data frame with 330 rows and 30 variables:

district

District name.

dist_id

District numeric identifier, 1 to 33.

year

Calendar year, 2014 to 2023.

agri_year

Agricultural year label, e.g. "2014-15".

GDVA_agri_crore

Gross District Value Added from Agriculture and Allied Sectors, constant 2011-12 prices, INR crore.

ln_GDVA

Natural log of GDVA_agri_crore. Dependent variable.

FarmIncome_Rs000

Farm household income, INR thousands.

HYV_area_000ha

Area under high-yielding varieties, 000 ha.

ln_HYV

Natural log of HYV_area_000ha. Key regressor.

Rainfall_mm

Annual rainfall, mm.

ln_Rainfall

Natural log of Rainfall_mm.

Temp_C

Mean annual temperature, degrees Celsius.

TempAnomaly

Standardised within-district temperature anomaly.

MCDS_days

Maximum Consecutive Dry Spell, days. Primary threshold variable.

RainAnomaly

Standardised within-district rainfall anomaly.

Pesticide_MT

Pesticide consumption, metric tonnes.

NPK_MT

NPK fertiliser consumption, metric tonnes.

Irrigation_pct

Share of gross cropped area irrigated.

VarCost_Rs

Variable input cost per hectare, INR.

KVK_intensity

Agricultural training intensity.

KVK_expd

Agricultural training expenditure, INR lakhs.

ExtWorker_per1000

Extension workers per 1000 farm households.

PACS_count

Number of primary agricultural credit societies.

APMC_yards

Number of agricultural market yards.

Road_density

Rural road density.

AvgHolding_ha

Average holding size, hectares.

SmallFarmer_share

Share of holdings below 2 hectares.

CropIntensity

Cropping intensity index.

Herfindahl

Crop concentration index, 0 to 1.

Rainfed_share

Share of gross cropped area that is rainfed.

Source

Compiled from public government and research institution sources for Maharashtra, India. Deposited at Mendeley Data: doi:10.17632/sr2gbfxk2d.2.

Examples

data(maharashtra_panel)
head(maharashtra_panel)

Hansen Panel Threshold Regression

Description

Estimates a two-regime panel threshold regression model following Hansen (1999, 2000), with two-way fixed effects, grid search for the optimal threshold, bootstrap likelihood ratio test, and confidence interval construction by likelihood ratio inversion.

Usage

ptr_hansen(
  formula,
  data,
  threshold_var,
  index,
  trim = c(0.15, 0.85),
  n_boot = 300,
  seed = 42,
  verbose = TRUE
)

Arguments

Value

A list of class ptr_hansen with elements threshold, ci, LR_stat, p_value, cv, coef_regime1, coef_regime2, regime_shift, ssr_grid, LR_boot, fit_regime1, fit_regime2, and call.

References

Hansen BE (1999). "Threshold Effects in Non-Dynamic Panels: Estimation, Testing, and Inference." Journal of Econometrics, 93(2), 345-368. doi:10.1016/S0304-4076(99)00025-1

Hansen BE (2000). "Sample Splitting and Threshold Estimation." Econometrica, 68(3), 575-603. doi:10.1111/1468-0262.00124

Examples

data(maharashtra_panel)
result <- ptr_hansen(
  formula       = ln_GDVA ~ ln_HYV + MCDS_days + Irrigation_pct,
  data          = maharashtra_panel,
  threshold_var = "MCDS_days",
  index         = c("district", "year"),
  n_boot        = 50,
  seed          = 42,
  verbose       = FALSE
)
print(result)
summary(result)
coef(result)
confint(result)

Spatial Durbin Model with Impact Decomposition

Description

Constructs a k-nearest neighbour spatial weights matrix, runs Moran's I pre-tests for spatial dependence, estimates a panel Spatial Durbin Model, and computes the direct, indirect, and total impact decomposition following LeSage and Pace (2009).

Usage

sdm_impact(formula, data, index, coords, k = 4, verbose = TRUE)

Arguments

Value

A list of class sdm_impact with elements W, impacts, spillover_ratio, rho, moran_y, moran_x, fit, and call.

References

LeSage J, Pace RK (2009). Introduction to Spatial Econometrics. CRC Press, Boca Raton. ISBN 978-1-4200-6424-7.

Examples

# Requires coordinate columns not in the bundled maharashtra_panel.
# Replace with real district centroids in your application.
data(maharashtra_panel)
units  <- sort(unique(maharashtra_panel$district))
coords <- data.frame(
  longitude = stats::runif(length(units), 73, 80),
  latitude  = stats::runif(length(units), 16, 22)
)
result <- sdm_impact(
  formula = ln_GDVA ~ ln_HYV + MCDS_days + Irrigation_pct,
  data    = maharashtra_panel,
  index   = c("district", "year"),
  coords  = coords,
  k       = 4
)
print(result)
coef(result)

Cross-Layer Consistency Check

Description

Evaluates whether the regime shift from ptr_hansen(), the spillover ratio from sdm_impact(), and the distributional gradient from canay_quantile() are mutually consistent with a common structural relationship.

Usage

tsq_consistency_check(ptr_result, sdm_result, qr_result, verbose = TRUE)

Arguments

Value

A list of class tsq_consistency with elements criterion1, criterion2, criterion3, all_pass, and summary_table.

Examples

ptr_mock <- structure(
  list(threshold = 23, regime_shift = 0.30,
       coef_regime1 = 0.346, coef_regime2 = 0.646),
  class = "ptr_hansen"
)
sdm_mock <- structure(
  list(spillover_ratio = 0.467, rho = 0.31),
  class = "sdm_impact"
)
qr_mock <- structure(
  list(gradient = c(Q10 = 0.57, Q50 = 0.55, Q90 = 0.52),
       gradient_highstress = c(Q10 = 0.62, Q50 = 0.56, Q90 = 0.50),
       taus = c(0.10, 0.50, 0.90), key_var = "ln_HYV"),
  class = "canay_quantile"
)
result <- tsq_consistency_check(ptr_mock, sdm_mock, qr_mock)
print(result)

Sequential Threshold-Spatial-Quantile Panel Estimation

Description

Wrapper that runs the full four-step TSQ protocol: a two-way fixed effects baseline, ptr_hansen(), sdm_impact(), and canay_quantile(), followed by tsq_consistency_check().

Usage

tsq_panel(
  formula,
  data,
  index,
  threshold_var,
  coords,
  k = 4,
  taus = c(0.1, 0.25, 0.5, 0.75, 0.9),
  trim = c(0.15, 0.85),
  n_boot_ptr = 300,
  n_boot_qr = 500,
  seed = 42,
  verbose = TRUE
)

Arguments

Value

A list of class tsq_panel with elements step1_baseline, step2_ptr, step3_sdm, step4_qr, consistency, and call.

Examples

# Requires coordinate data; replace with real district centroids.
data(maharashtra_panel)
units  <- sort(unique(maharashtra_panel$district))
coords <- data.frame(
  longitude = stats::runif(length(units), 73, 80),
  latitude  = stats::runif(length(units), 16, 22)
)
result <- tsq_panel(
  formula       = ln_GDVA ~ ln_HYV + MCDS_days + Irrigation_pct,
  data          = maharashtra_panel,
  index         = c("district", "year"),
  threshold_var = "MCDS_days",
  coords        = coords,
  n_boot_ptr    = 50,
  n_boot_qr     = 50
)
print(result)
coef(result)

Plot TSQ Protocol Results

Description

Produces diagnostic plots for TSQ protocol results.

Usage

tsq_plot(x, type = "all", ...)

Arguments

Value

A ggplot object or named list of ggplot objects.

Examples

data(maharashtra_panel)
ptr <- ptr_hansen(
  formula       = ln_GDVA ~ ln_HYV + MCDS_days + Irrigation_pct,
  data          = maharashtra_panel,
  threshold_var = "MCDS_days",
  index         = c("district", "year"),
  n_boot        = 50,
  verbose       = FALSE
)
p <- tsq_plot(ptr, type = "threshold")
print(p)