# A-short-introduction-to-MEFM

library(MEFM)

# 1. Model generation and estimation

In ‘MEFM’, we mainly consider a main effect matrix factor model (MEFM) such that for any time $$t\in[T]$$, $\mathbf{Y}_t = \mu_t \mathbf{1}_p \mathbf{1}_q^\top + \boldsymbol{\alpha}_t \mathbf{1}_q^\top + \mathbf{1}_p \boldsymbol{\beta}_t^\top + \mathbf{A}_r \mathbf{F}_t \mathbf{A}_c^\top + \mathbf{E}_t ,$ where $$\mathbf{Y}_t$$ is an observation of dimension $$p\times q$$, $$\mu_t$$ is a scalar representing the time-varying grand mean, $$\boldsymbol{\alpha}_t \in \mathbb{R}^p$$ is the time-varying row effect, $$\boldsymbol{\beta}_t \in \mathbb{R}^q$$ is the time-varying column effect, $$\mathbf{E}_t$$ is the noise matrix with the same dimension as the observation, and $$\mathbf{A}_r \mathbf{F}_t \mathbf{A}_c^\top$$ is the common component in tradition factor models with $$\mathbf{A}_r \in \mathbb{R}^{p\times k_r}$$, $$\mathbf{A}_c \in \mathbb{R}^{q\times k_c}$$, $$\mathbf{F}_t \in \mathbb{R}^{k_r \times k_c}$$ being the row loading, column loading and core factors, respectively.

We may simulate such model series by using the base class ‘array’ in R. An example data is generated using the ‘gen_MEFM’ function below. For details of the data generation mechanism, see Lam and Cen (2024). For reproducibility, a seed parameter is required, which is 2024 by default.

TT <- 60 #time length
d <- c(60,60) #spatial dimensions
r <- c(2,2) #rank of core tensor
re <- c(2,2) #rank of common component in error
eta <- list(c(0,0), c(0,0)) #strong factors
coef_f <- c(0.7, 0.3, -0.4, 0.2, -0.1) #AR(5) coefficients for core factor
coef_fe <- c(-0.7, -0.3, -0.4, 0.2, 0.1) #AR(5) coefficients for common component in error
coef_e <- c(0.8, 0.4, -0.4, 0.2, -0.1) #AR(5) coefficients for idiosyncratic part in error
param_mu <- c(0,1) #Normal mean and variance for mu
param_alpha <- c(0,1) #Normal mean and variance for alpha
param_beta <- c(0,1) #Normal mean and variance for beta
data_example <- gen_MEFM(TT,d,r,re,eta, coef_f, coef_fe, coef_e, param_mu, param_alpha, param_beta)

With the ‘est_MEFM’ function, the factor structure can be estimated in one go. The number of factors could be either provided or not provided, in the latter case the function estimates the number of factors using the eigenvalue-ratio-based estimator. For the details of estimation, see Lam and Cen (2024). As an example, we show the estimated number of factors and also the estimation error.

est_result <- est_MEFM(data_example$MEFM) paste0('Estimated number of factors: ', est_result$r[1], ', ', est_result$r[2]) #> [1] "Estimated number of factors: 2, 2" paste0('Estimation error for mu: ', sum((est_result$mu - data_example$mu)^2)/TT) #> [1] "Estimation error for mu: 0.000204405935075275" paste0('Estimation error for alpha: ', sum((est_result$alpha - data_example$alpha)^2)/prod(dim(data_example$alpha)))
#> [1] "Estimation error for alpha: 0.0183163947105022"

paste0('The true mu at time 10 is: ', data_example$mu[time_select]) #> [1] "The true mu at time 10 is: -1.12135130216029" paste0('The test statistic is: ', asymp_i ) #> [1] "The test statistic is: 2.22064506054644" As the test statistic is outside of $$[\text{-}1.96, 1.96]$$, we reject the null hypothesis under $$5\%$$ significance level. For $$\boldsymbol{\alpha}_t$$ and $$\boldsymbol{\beta}_t$$, we only demonstrate their asymptotic normality here. Consider the first three entries at time $$t=10$$, we have gamma_alpha_inv <- matrix(0, nrow=3, ncol=3) for (j in 1:3){ gamma_alpha_inv[j,j] <- (make_gamma(hat.E, type = 'alpha', j))^(-1) } asymp_alpha_example <- (d[2])^0.5 * gamma_alpha_inv %*% (est_result$alpha[time_select, 1:3] - data_example$alpha[time_select, 1:3]) gamma_beta_inv <- matrix(0, nrow=3, ncol=3) for (j in 1:3){ gamma_beta_inv[j,j] <- (make_gamma(hat.E, type = 'beta', j))^(-1) } asymp_beta_example <- (d[1])^0.5 * gamma_beta_inv %*% (est_result$beta[time_select, 1:3] - data_example$beta[time_select, 1:3]) paste0('The test statistic using true alpha: ', asymp_alpha_example[1], ', ', asymp_alpha_example[2], ', ', asymp_alpha_example[3]) #> [1] "The test statistic using true alpha: -1.10145252408513, 0.914153410430621, 0.425092850778305" paste0('The test statistic using true beta: ', asymp_beta_example[1], ', ', asymp_beta_example[2], ', ', asymp_beta_example[3]) #> [1] "The test statistic using true beta: 0.531048546382394, -0.354637264024393, 0.684069398353172" # 3. Asymptotic normality of estimated loading matrix rows Under certain conditions (Lam and Cen 2024), the residue between the row of the estimated loading matrix and its corresponding true row under some rotation can be shown to be asymptotically normal. A consistent covariance matrix estimator can be computed by the ‘sigmaD_MEFM’ function. For instance, to compute the residue on the first row of the estimated column loading matrix, the covariance matrix estimator is obtained below. The rotation matrix is also computed afterwards. # computing the covariance matrix estimator r2 <- r[2] eta <- floor(0.2*((TT * prod(d))^0.25)) D2 <- diag(x=(svd(est_result$covMatrix[[2]])$d)[1:r2], nrow=r2, ncol=r2) # HAC_cov: HAC-type covariance matrix estimator HAC_cov <- sigmaD_MEFM(2, D2, est_result$A[[2]], est_result$Ct, data_example$MEFM - est_result$Yt, 1, eta) # computing the rotation matrix A2 <- data_example$A[[2]]
Amk_i <- data_example$A[[1]] R_ast_i <- 0 for (tt in 1:TT){ R_ast_i <- R_ast_i + tensorMiss::unfold(matrix(data_example$Ft[tt,,], ncol=r2),2) %*% t(Amk_i) %*% Amk_i %*% t(tensorMiss::unfold(matrix(data_example$Ft[tt,,], ncol=r2),2)) } R_ast_i <- A2 %*% R_ast_i %*% t(A2) R_ast_i <- R_ast_i/TT Z2_i <- diag(x = diag(t(A2) %*% A2), nrow=r2, ncol=r2) Q2_i <- A2 %*% diag(x=diag(solve(Z2_i))^0.5, nrow=r2, ncol=r2) # H2_i: rotation matrix H2_i <- solve(D2) %*% t(est_result$A[[2]]) %*% R_ast_i %*% Q2_i %*% solve(t(Q2_i)%*% Q2_i)

Eventually, the standardised residue is shown below and should follow a standard normal distribution when dimensions are increased.

HAC_cov.eigen <- eigen(HAC_cov)
HAC_cov.sqrt <- HAC_cov.eigen$vectors %*% diag(sqrt(HAC_cov.eigen$values)) %*% solve(HAC_cov.eigen$vectors) A2_1 <- TT * (solve(HAC_cov.sqrt) %*% D2) %*% (matrix(est_result$A[[2]], nrow=d[2], ncol=r2)[1,] - (H2_i %*% Q2_i[1,]))
A2_1
#>            [,1]
#> [1,]  0.1819581
#> [2,] -0.5004283

# 4. Comparison with traditional factor models

This section acts as a toy example on how to test the necessity of using MEFM instead of traditional factor models (FM). Before that, if the data is indeed generated using FM, we can see that MEFM will not make the estimation worse off and actually it is always better to use MEFM.

paste0('MSE of estimating MEFM on FM-generated data: ', sum((est_MEFM(data_example$FM)$Yt -  data_example$FM)^2) / sum(data_example$FM^2))
#> [1] "MSE of estimating MEFM on FM-generated data: 0.172853133412508"
paste0('MSE of estimating FM on FM-generated data: ', sum((est_FM(data_example$FM)$Ct -  data_example$FM)^2) / sum(data_example$FM^2))
#> [1] "MSE of estimating FM on FM-generated data: 0.179171222681373"

However, if the data has MEFM structure, then estimation based on FM is hardly satisfying even if we use much larger number of factors, as shown below.

paste0('MSE of estimating MEFM on MEFM-generated data: ', sum((est_MEFM(data_example$MEFM)$Yt -  data_example$MEFM)^2) / sum(data_example$MEFM^2))
#> [1] "MSE of estimating MEFM on MEFM-generated data: 0.11303111038302"
paste0('MSE of estimating FM on MEFM-generated data (using number of factors as (3,3)): ', sum((est_FM(data_example$MEFM)$Ct -  data_example$MEFM)^2) / sum(data_example$MEFM^2))
#> [1] "MSE of estimating FM on MEFM-generated data (using number of factors as (3,3)): 0.331459654558444"
paste0('MSE of estimating FM on MEFM-generated data (using number of factors as (30,30)): ', sum((est_FM(data_example$MEFM, r=c(30,30))$Ct -  data_example$MEFM)^2) / sum(data_example$MEFM^2))
#> [1] "MSE of estimating FM on MEFM-generated data (using number of factors as (30,30)): 0.11429915366256"

It is hence worthy to test if using FM with slightly larger number of factors is sufficient. According to Lam and Cen (2024), we perform testing as follows.

MEFM_on_MEFM <- est_MEFM(data_example$MEFM) FM_on_MEFM <- est_FM(data_example$MEFM, r=c(MEFM_on_MEFM$r[1] +1, MEFM_on_MEFM$r[1] +1))

x_alpha <- make_xy(MEFM_on_MEFM$Yt - data_example$MEFM, type = 'alpha')
y_alpha <- make_xy(FM_on_MEFM$Ct - data_example$MEFM, type = 'alpha')
x_beta <- make_xy(MEFM_on_MEFM$Yt - data_example$MEFM, type = 'beta')
y_beta <- make_xy(FM_on_MEFM$Ct - data_example$MEFM, type = 'beta')

paste0('Computed alpha_reject: ', sum(y_alpha >= qHat(x_alpha, 0.95))/length(y_alpha))
#> [1] "Computed alpha_reject: 1"
paste0('Computed beta_reject: ', sum(y_beta >= qHat(x_beta, 0.95))/length(y_beta))
#> [1] "Computed beta_reject: 1"

We expect the computed $$\boldsymbol{\alpha}_\text{reject}$$ and $$\boldsymbol{\beta}_\text{reject}$$ to be close to $$5\%$$ if FM is sufficient, and clearly it is not in our case.

## References

Lam, Clifford, and Zetai Cen. 2024. “Matrix-Valued Factor Model with Time-Varying Main Effects.”