Package 'ivx' reference manual

Title:	Robust Econometric Inference
Description:	Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.
Authors:	Kostas Vasilopoulos [cre, aut], Efthymios Pavlidis [aut]
Maintainer:	Kostas Vasilopoulos <[email protected]>
License:	GPL-3
Version:	1.1.0
Built:	2024-10-18 06:42:23 UTC
Source:	CRAN

Autocorrelation tests

Description

Autocorrelation tests

Usage

ac_test(x, lag_max = 5)
ac_test(x, lag_max = 5)

Arguments

`x`	the residuals or an `ivx` object.
`lag_max`	the maximum length of lags.

Examples

obj <- ivx(hpi ~ cpi + def + int + log(res), data = ylpc)
lmtest::bgtest(hpi ~ cpi + def + int + log(res), data = ylpc)
ac_test(obj, 5)

obj <- ivx(hpi ~ cpi + def + int + log(res), data = ylpc)
lmtest::bgtest(hpi ~ cpi + def + int + log(res), data = ylpc)
ac_test(obj, 5)

Tests for autocorrelation

Description

ac_test_wald: Wald test
ac_test_lb: Ljung-Box
ac_test_bp: Box-Pierce
ac_test_bg: Breusch-Godfrey

Usage

ac_test_wald(x, lag)

ac_test_lb(x, lag)

ac_test_bp(x, lag)

ac_test_bg(x, order, type, fill)
ac_test_wald(x, lag)

ac_test_lb(x, lag)

ac_test_bp(x, lag)

ac_test_bg(x, order, type, fill)

Arguments

`x`	an `ivx` model or a `⁠numeric vector⁠`, usually the residuals from an ols regression.
`lag`	the number of lags.
`order`	lag TODO
`type`	the type of test statistic to be returned. Either "Chisq" for the Chi-squared test statistic or "F" for the F test statistic.
`fill`	starting values for the lagged residuals in the auxiliary regression. By default 0 but can also be set to NA.

Details

#' If p-value < 0.051: You can reject the null hypothesis assuming a 5% chance of making a mistake. So you can assume that your values are showing dependence on each other.

Value

a numeric scalar or numeric vector.

Examples


mdl <- ivx(hpi ~ cpi + inv, data = ylpc)
ac_test_wald(mdl)


mdl <- ivx(hpi ~ cpi + inv, data = ylpc)
ac_test_wald(mdl)

Calculate the delta coefficient

Description

Computes the long-run correlation coefficient between the residuals of the predictive regression and the autoregressive model for the regressor.

Usage

delta(object)
delta(object)

Arguments

object

on object of class "ivx"

Value

A vector of the estimated correlation coefficients. This should have row and column names corresponding to the parameter names given by the coef method.

Examples

mod <- ivx(Ret ~ LTY, data = monthly)

delta(mod)
mod <- ivx(Ret ~ LTY, data = monthly)

delta(mod)

`extract` method for `ivx` objects

Description

extract method for ivx objects

Usage

extract.ivx(
  model,
  include.wald = TRUE,
  include.nobs = TRUE,
  include.aic = FALSE,
  include.bic = FALSE,
  include.rsquared = FALSE,
  include.adjrs = FALSE,
  ...
)

extract.ivx_ar(
  model,
  include.wald = TRUE,
  include.nobs = TRUE,
  include.aic = FALSE,
  include.bic = FALSE,
  include.rsquared = FALSE,
  include.adjrs = FALSE,
  ...
)
extract.ivx(
  model,
  include.wald = TRUE,
  include.nobs = TRUE,
  include.aic = FALSE,
  include.bic = FALSE,
  include.rsquared = FALSE,
  include.adjrs = FALSE,
  ...
)

extract.ivx_ar(
  model,
  include.wald = TRUE,
  include.nobs = TRUE,
  include.aic = FALSE,
  include.bic = FALSE,
  include.rsquared = FALSE,
  include.adjrs = FALSE,
  ...
)

Arguments

`model`	A statistical model object.
`include.wald`	Report the Wald statistic.
`include.nobs`	Report the number of observations in the GOF block?
`include.aic`	Report Akaike's Information Criterion (AIC) in the GOF block?
`include.bic`	Report the Bayesian Information Criterion (BIC) in the GOF block?
`include.rsquared`	Report the R-squared.
`include.adjrs`	Report the Adjusted R-squared.
`...`	Custom parameters, which are handed over to subroutines. Currently not in use.

ivx fits predictive regression models. The method allows standard chi-square testing for regressors with different degrees of persistence, from stationary to mildly explosive, and can be used for both short- and long-horizon predictive regressions.

Usage

ivx(
  formula,
  data,
  horizon,
  na.action,
  weights,
  contrasts = NULL,
  offset,
  model = TRUE,
  x = FALSE,
  y = FALSE,
  ...
)

## S3 method for class 'ivx'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
ivx(
  formula,
  data,
  horizon,
  na.action,
  weights,
  contrasts = NULL,
  offset,
  model = TRUE,
  x = FALSE,
  y = FALSE,
  ...
)

## S3 method for class 'ivx'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

`formula`	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
`data`	n optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
`horizon`	is the horizon (default horizon = 1 corresponds to a short-horizon regression).
`na.action`	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of `options`, and is `na.fail` if that is unset. The ‘factory-fresh’ default is `na.omit`. Another possible value is `NULL`, no action. Value `na.exclude` can be useful.
`weights`	an optional vector of weights to be used in the fitting process. Should be `NULL` or a numeric vector. If non-NULL, weighted least squares is used with weights `weights` (that is, minimizing `sum(w*e^2)`); otherwise ordinary least squares is used.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector or matrix of extents matching those of the response. One or more offset terms can be included in the formula instead or as well, and if more than one are specified their sum is used. See model.offset
`model`	logical. If `TRUE` the model.frame of the fit is returned.
`x`	an object of class "ivx", usually, a result of a call to ivx.
`y`	logical. If `TRUE` the response of the fit is returned.
`...`	additional arguments to be passed to the low level regression fitting functions (see lm).
`digits`	the number of significant digits to use when printing.

Value

an object of class "ivx".

References

Magdalinos, T., & Phillips, P. (2009). Limit Theory for Cointegrated Systems with Moderately Integrated and Moderately Explosive Regressors. Econometric Theory, 25(2), 482-526.

Kostakis, A., Magdalinos, T., & Stamatogiannis, M. P. (2014). Robust econometric inference for stock return predictability. The Review of Financial Studies, 28(5), 1506-1553.

Examples


# Univariate
ivx(Ret ~ LTY, data = kms)

# Multivariate
ivx(Ret ~ LTY + TBL, data = kms)

# Longer horizon
ivx(Ret ~ LTY + TBL, data = kms, horizon = 4)

wt <- runif(nrow(kms))
ivx(Ret ~ LTY, data = kms, weights = wt)

# Univariate
ivx(Ret ~ LTY, data = kms)

# Multivariate
ivx(Ret ~ LTY + TBL, data = kms)

# Longer horizon
ivx(Ret ~ LTY + TBL, data = kms, horizon = 4)

wt <- runif(nrow(kms))
ivx(Ret ~ LTY, data = kms, weights = wt)

Fitting IVX-AR Models

Description

ivx_ar implements the Yang et al (2020) new instrumental variable based Wald statistic (IVX-AR) which accounts for serial correlation and heteroscedasticity in the error terms of the linear predictive regression model.

Usage

ivx_ar(
  formula,
  data,
  horizon,
  ar = "auto",
  ar_ic = c("bic", "aic", "aicc"),
  ar_max = 5,
  ar_grid = function(x) seq(x - 0.3, x + 0.3, by = 0.02),
  na.action,
  contrasts = NULL,
  offset,
  model = TRUE,
  x = FALSE,
  y = FALSE,
  ...
)

## S3 method for class 'ivx_ar'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
ivx_ar(
  formula,
  data,
  horizon,
  ar = "auto",
  ar_ic = c("bic", "aic", "aicc"),
  ar_max = 5,
  ar_grid = function(x) seq(x - 0.3, x + 0.3, by = 0.02),
  na.action,
  contrasts = NULL,
  offset,
  model = TRUE,
  x = FALSE,
  y = FALSE,
  ...
)

## S3 method for class 'ivx_ar'
print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

`formula`	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
`data`	n optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
`horizon`	is the horizon (default horizon = 1 corresponds to a short-horizon regression).
`ar`	Method to include the autoregressive terms. "auto" find the optimal ar order by using the information criteria. `ar = 0` reduces to simple `ivx`. `ar > 1` uses a fixed order to estimate the model.
`ar_ic`	Information criterion to be used in model selection.
`ar_max`	Maximum ar order of model to fit.
`ar_grid`	The ar grid sequence of which to iterate.
`na.action`	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of `options`, and is `na.fail` if that is unset. The ‘factory-fresh’ default is `na.omit`. Another possible value is `NULL`, no action. Value `na.exclude` can be useful.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`offset`	this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector or matrix of extents matching those of the response. One or more offset terms can be included in the formula instead or as well, and if more than one are specified their sum is used. See model.offset
`model`	logical. If `TRUE` the model.frame of the fit is returned.
`x`	an object of class "ivx_ar", usually, a result of a call to ivx_ar.
`y`	logical. If `TRUE` the response of the fit is returned.
`...`	additional arguments to be passed to the low level regression fitting functions (see lm).
`digits`	the number of significant digits to use when printing.

References

Yang, B., Long, W., Peng, L., & Cai, Z. (2020). Testing the Predictability of US Housing Price Index Returns Based on an IVX-AR Model. Journal of the American Statistical Association, 1-22. DOI: doi:10.1080/01621459.2019.1686392

Examples


ivx_ar(hpi ~ log(res) + cpi, ylpc)

ivx_ar(hpi ~ log(res) + cpi, ylpc, ar_ic = "aic")

ivx_ar(hpi ~ log(res) + cpi, ylpc, ar = 1)

ivx_ar(hpi ~ log(res) + cpi, ylpc)

ivx_ar(hpi ~ log(res) + cpi, ylpc, ar_ic = "aic")

ivx_ar(hpi ~ log(res) + cpi, ylpc, ar = 1)

Fitter Functions for IVX-AR Models

Description

Basic function called by ivx_ar to fit predictive models. These should only be used directly by experienced users.

Usage

ivx_ar_fit(
  y,
  x,
  horizon = 1,
  offset = NULL,
  ar = "auto",
  ar_max = 5,
  ar_ic = "bic",
  ar_grid = function(x) seq(x - 0.3, x + 0.3, by = 0.02),
  ...
)
ivx_ar_fit(
  y,
  x,
  horizon = 1,
  offset = NULL,
  ar = "auto",
  ar_max = 5,
  ar_ic = "bic",
  ar_grid = function(x) seq(x - 0.3, x + 0.3, by = 0.02),
  ...
)

Arguments

`y`	vector of observations of length `n`, or a matrix with `n` rows.
`x`	design matrix of dimension `n * p`.
`horizon`	is the horizon (default horizon = 1 corresponds to a short-horizon regression).
`offset`	(numeric of length `n`). This can be used to specify an a priori known component to be included in the linear predictor during fitting.
`ar`	Method to include the autoregressive terms. "auto" find the optimal ar order by using the information criteria. `ar = 0` reduces to simple `ivx`. `ar > 1` uses a fixed order to estimate the model.
`ar_max`	Maximum ar order of model to fit.
`ar_ic`	Information criterion to be used in model selection.
`ar_grid`	The ar grid sequence of which to iterate.
`...`	Further arguments passed to the function which is fitting the best AR model. If `ar = "auto"` then the internal function `auto_ar` is used, if `ar = "forecast"` then the the function `forecast::auto.arima` is used. If ar is of fixed length then `arima` is used.

Examples


ivx_ar_fit(monthly$Ret, as.matrix(monthly$LTY))

ivx_ar_fit(monthly$Ret, as.matrix(monthly$LTY), ar = 1)

ivx_ar_fit(monthly$Ret, as.matrix(monthly$LTY))

ivx_ar_fit(monthly$Ret, as.matrix(monthly$LTY), ar = 1)

Fitter Functions for IVX Models

Description

Basic function called by ivx to fit predictive models. These should only be used directly by experienced users.

Usage

ivx_fit(y, x, horizon = 1, offset = NULL, ...)

ivx_wfit(y, x, w, horizon = 1, offset = NULL, ...)
ivx_fit(y, x, horizon = 1, offset = NULL, ...)

ivx_wfit(y, x, w, horizon = 1, offset = NULL, ...)

Arguments

`y`	vector of observations of length `n`, or a matrix with `n` rows.
`x`	design matrix of dimension `n * p`.
`horizon`	is the horizon (default horizon = 1 corresponds to a short-horizon regression).
`offset`	(numeric of length `n`). This can be used to specify an a priori known component to be included in the linear predictor during fitting.
`...`	currently disregarded.
`w`	vector of weights (length `n`) to be used in the fitting process for the `wfit` functions. Weighted least squares is used with weights `w`, i.e., `sum(w * e^2)` is minimized.

Examples

ivx_fit(monthly$Ret, as.matrix(monthly$LTY))
ivx_fit(monthly$Ret, as.matrix(monthly$LTY))

KMS Monthly data

Description

The dataset that was used in Kostakis et al (2015), containing a range of financial variables.

Date: year-month-date (monthly frequency)
DE: dividend payout ratio
LTY: long-term yield
DY: dividend yield
DP: dividend-price ratio
TBL: T-bill rate
EP: earnings-price ratio
BM: book-to-market value ratio
INF: inflation rate
DFY: default yield spread
NTIS: net equity expansion
TMS: term spread
Ret: S&P 500 value-weighted log excess returns

Usage

kms
kms

Format

A data.frame with 13 variables and 1,033 observations.

Source

https://drive.google.com/open?id=1FdT2STHO2Lnlweom4AwICVf-rpVMfgV4

KMS Quarterly data

Description

The dataset that was used in Kostakis et al (2015), containing a range of financial variables.

Date: year-month-date
DE: dividend payout ratio
LTY: long-term yield
DY: dividend yield
DP: dividend-price ratio
TBL: T-bill rate
EP: earnings-price ratio
BM: book-to-market value ratio
INF: inflation rate
DFY: default yield spread
NTIS: net equity expansion
TMS: term spread
Ret: S&P 500 value-weighted log excess returns

Usage

kms_quarterly
kms_quarterly

Format

A data.frame with 13 variables and 345 observations.

Source

https://drive.google.com/open?id=1FdT2STHO2Lnlweom4AwICVf-rpVMfgV4

Summarizing IVX Model Fits

Description

summary method for class "ivx".

Usage

## S3 method for class 'ivx'
summary(object, ...)

## S3 method for class 'summary.ivx'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  ...
)
## S3 method for class 'ivx'
summary(object, ...)

## S3 method for class 'summary.ivx'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  ...
)

Arguments

`object`	object of class "ivx", usually, a result of a call to ivx.
`...`	further arguments passed to or from other methods.
`x`	an object of class `"summary.lm"`, usually, a result of a call to `summary.lm`.
`digits`	the number of significant digits to use when printing.
`signif.stars`	logical. If `TRUE`, ‘significance stars’ are printed for each coefficient.

Examples

mod <- ivx(Ret ~ LTY, data = monthly)

summary(mod)
mod <- ivx(Ret ~ LTY, data = monthly)

summary(mod)

Summarizing IVX-AR Model Fits

Description

summary method for class "ivx".

Usage

## S3 method for class 'ivx_ar'
summary(object, ...)

## S3 method for class 'summary.ivx_ar'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  ...
)
## S3 method for class 'ivx_ar'
summary(object, ...)

## S3 method for class 'summary.ivx_ar'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  signif.stars = getOption("show.signif.stars"),
  ...
)

Arguments

`object`	object of class "ivx_ar", usually, a result of a call to ivx_ar.
`...`	further arguments passed to or from other methods.
`x`	an object of class `"summary.lm"`, usually, a result of a call to `summary.lm`.
`digits`	the number of significant digits to use when printing.
`signif.stars`	logical. If `TRUE`, ‘significance stars’ are printed for each coefficient.

Examples

mod <- ivx_ar(Ret ~ LTY, data = kms)

summary(mod)
mod <- ivx_ar(Ret ~ LTY, data = kms)

summary(mod)

Calculate Variance-Covariance Matrix for a Fitted Model Object

Description

Calculate Variance-Covariance Matrix for a Fitted Model Object

Usage

## S3 method for class 'ivx'
vcov(object, complete = TRUE, ...)

## S3 method for class 'summary.ivx'
vcov(object, complete = TRUE, ...)
## S3 method for class 'ivx'
vcov(object, complete = TRUE, ...)

## S3 method for class 'summary.ivx'
vcov(object, complete = TRUE, ...)

Arguments

`object`	a fitted ivx and summary.ivx object.
`complete`	logical indicating if the full variance-covariance matrix should be returned. When complete = TRUE, vcov() is compatible with coef().
`...`	additional arguments for method functions.

Value

A matrix of the estimated covariances between the parameter estimates of the model. This should have row and column names corresponding to the parameter names given by the coef method.

Examples

mod <- ivx(Ret ~ LTY, data = monthly)

vcov(mod)
mod <- ivx(Ret ~ LTY, data = monthly)

vcov(mod)

YLPC Quarterly data

Description

The dataset that was used in ..., containing a range of variables.

Usage

ylpc
ylpc

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 174 rows and 12 columns.

Source

https://www.tandfonline.com/doi/suppl/10.1080/01621459.2019.1686392/suppl_file/uasa_a_1686392_sm7226.zip

Package 'ivx'

Help Index

Autocorrelation tests

Description

Usage

Arguments

Examples

Tests for autocorrelation

Description

Usage

Arguments

Details

Value

See Also

Examples

Calculate the delta coefficient

Description

Usage

Arguments

Value

Examples

extract method for ivx objects

Description

Usage

Arguments

Fitting IVX Models

Description

Usage

Arguments

Value

References

Examples

Fitting IVX-AR Models

Description

Usage

Arguments

References

Examples

Fitter Functions for IVX-AR Models

Description

Usage

Arguments

Examples

Fitter Functions for IVX Models

Description

Usage

Arguments

Examples

KMS Monthly data

Description

Usage

Format

Source

KMS Quarterly data

Description

Usage

Format

Source

Summarizing IVX Model Fits

Description

Usage

Arguments

Examples

Summarizing IVX-AR Model Fits

Description

Usage

Arguments

Examples

Calculate Variance-Covariance Matrix for a Fitted Model Object

Description

Usage

Arguments

Value

Examples

YLPC Quarterly data

Description

Usage

Format

Source

`extract` method for `ivx` objects