from pyeconlab import DynamicProductLevelExportSystem #-Local Imports-# from dataset_info import TARGET_DATASET_DIR, CHAPTER_RESULTS DATASET_DIR = TARGET_DATASET_DIR['nber'] RESULTS_DIR = CHAPTER_RESULTS["G"] data = pd.read_hdf(DATASET_DIR + "nber-export-sitcr2l4-1962to2000.h5", "D") data = data.rename(columns={ 'eiso3c': 'country', 'sitc4': 'productcode', 'value': 'export' }) data = data.set_index(["year"]) system = DynamicProductLevelExportSystem() system.from_df(data) #-Year 2000-# ys = system[2000] ys.rca_matrix(complete_data=True) ys.mcp_matrix() ys.compute_pci() ys.auto_adjust_pci_sign() pci = ys.pci.copy() #-Example Proximity Values-# from pyeconlab.trade.classification import SITCR2 sitc_to_name = SITCR2().code_description_dict() prox1 = ys.proximity_matrix() products1 = ["8423", "0711"]
# ----------------# # -Setup a System-# # ----------------# # -Export Data-# for year in xrange(1995, 2013 + 1, 1): df = pd.read_stata(SOURCE_DIR + "baci-sitcr3-export-%s.dta" % (year)) df = df.rename(columns={"eiso3c": "country", "sitcr3": "productcode", "value": "export"}) df = df.set_index(["year"]) if year == 1995: data = df else: data = data.append(df) s = DynamicProductLevelExportSystem() s.from_df(data) # -2013 Cross Section-# xs = s[2000] xs.rca_matrix(complete_data=True) xs.mcp_matrix() xs.proximity_matrix() xs.compute_pci() xs.auto_adjust_pci_sign(product_datum=("33400", "-ve")) # -Oil is negative, un-complex-# prox = xs.proximity pci = xs.pci.to_dict() # -Parts and Components Product Code Sets-# allcodes = set(prox.index)
import pandas as pd import numpy as np import matplotlib.pyplot as plt from pyeconlab import DynamicProductLevelExportSystem #-Local Imports-# from dataset_info import TARGET_DATASET_DIR, CHAPTER_RESULTS DATASET_DIR = TARGET_DATASET_DIR['nber'] RESULTS_DIR = CHAPTER_RESULTS["G"] data = pd.read_hdf(DATASET_DIR+"nber-export-sitcr2l4-1962to2000.h5", "D") data = data.rename(columns={'eiso3c' : 'country', 'sitc4' : 'productcode', 'value' : 'export'}) data = data.set_index(["year"]) system = DynamicProductLevelExportSystem() system.from_df(data) #-Year 2000-# ys = system[2000] ys.rca_matrix(complete_data=True) ys.mcp_matrix() ys.compute_pci() ys.auto_adjust_pci_sign() pci = ys.pci.copy() #-Example Proximity Values-# from pyeconlab.trade.classification import SITCR2 sitc_to_name = SITCR2().code_description_dict() prox1 = ys.proximity_matrix() products1 = ["8423", "0711"]