Understanding the Link Between Uncertainty and Imports by glmnet

According to the graph below, suggested by Fernando Leibovici, the increase in uncertainty that began in late 2024 aligns with a rise in imports, indicating that US importers accelerated their purchases as a precaution against expected tariff increases or supply chain disruptions.

When we model the variables with the glmnet engine, we can see that this impact is limited and negative. This could confirm the thought that Fernando Leibovici suggested that this alignment is mostly due to gold imports.

Source code:

library(tidyverse)library(tidymodels)library(tidyquant)library(timetk)#Imports of Goods and Services: Balance of Payments Basis (BOPTIMP)df_imports <-   tq_get("BOPTIMP", get = "economic.data") %>%   select(date = observation_date, imports = BOPTIMP)#Economic Policy Uncertainty Index: Categorical Index: Trade policy (EPUTRADE)df_uncertainty <-   tq_get("EPUTRADE", get = "economic.data") %>%   select(date = observation_date, uncertainty = EPUTRADE)#Merging the datasetsdf_merged <-   df_imports %>%   left_join(df_uncertainty) %>%   drop_na()df_merged %>%   plot_acf_diagnostics(date,                       imports,                       .ccf_vars = "uncertainty")#Correlationdf_merged %>%   filter(date >= first(date) - months(3)) %>%   tq_performance(Ra = imports,                  Rb = uncertainty,                  performance_fun = table.Correlation)#Data splitsplits <- initial_time_split(df_merged, prop = 0.8)df_train <- training(splits)df_test <- testing(splits)#Bootstrapping for tuningset.seed(12345)df_folds <- bootstraps(df_train,                       times = 100)#Recipe Preprocessing Specificationrec_spec <-   recipe(imports ~ ., data = training(splits)) %>%  step_fourier(date, period = 30, K = 1) %>%  step_date(date, features = c("month", "year")) %>%   step_rm(date) %>%   step_dummy(all_nominal_predictors(), one_hot = TRUE) %>%   step_normalize(all_numeric_predictors())  #Model Specificationmodel_spec <-   linear_reg(  mode = "regression",   penalty = tune()) %>%  set_engine("glmnet")#Workflow setswflow_ <-   workflow_set(    preproc = list(recipe = rec_spec),    models = list(model = model_spec)  ) #Tuning and evaluating all the modelsgrid_ctrl <-  control_grid(    save_pred = TRUE,    parallel_over = "everything",    save_workflow = TRUE  )grid_results <-  wflow_ %>%  workflow_map(    seed = 98765,    resamples = df_folds,    grid = 10,    control = grid_ctrl  )#Accuracy of the grid resultsgrid_results %>%   rank_results(select_best = TRUE,                rank_metric = "rsq") %>%  select(Models = wflow_id, .metric, mean)# A tibble: 2 × 3#Models         .metric      mean#  <chr>        <chr>       <dbl>#1 recipe_model rmse    18389.   #2 recipe_model rsq         0.804#Finalizing the model with the best parametersbest_param <-   grid_results %>%  extract_workflow_set_result("recipe_model") %>%   select_best(metric = "rsq")wflw_fit <-   grid_results %>%   extract_workflow("recipe_model") %>%   finalize_workflow(best_param) %>%   fit(df_train)#Variable importancelibrary(DALEXtra)#Processed data frame for variable importance calculationimp_data <-   rec_spec %>%   prep() %>%   bake(new_data = NULL) #Explainer objectexplainer_ <-   explain_tidymodels(    wflw_fit %>% extract_fit_parsnip(),     data = imp_data %>% select(-imports),     y = imp_data$imports,    label = "",    verbose = FALSE  )#Model Studiolibrary(modelStudio)set.seed(1983)modelStudio::modelStudio(explainer_,                         B = 100,                         viewer = "browser")