diff --git a/.github/workflows/build_and_test.yml b/.github/workflows/build_and_test.yml index c220888c..569c9f3b 100644 --- a/.github/workflows/build_and_test.yml +++ b/.github/workflows/build_and_test.yml @@ -4,7 +4,7 @@ on: [push, pull_request] jobs: build: - runs-on: ubuntu-latest + runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] @@ -12,21 +12,23 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: persist-credentials: false - - name: Setup Python - uses: actions/setup-python@v4 + - name: Setup Miniconda using Python ${{ matrix.python-version }} + uses: conda-incubator/setup-miniconda@v2 with: + miniforge-variant: Mambaforge + auto-update-conda: true + activate-environment: ogusa-dev + environment-file: environment.yml python-version: ${{ matrix.python-version }} + auto-activate-base: false - name: Build shell: bash -l {0} run: | - conda init bash - conda env create - conda activate ogusa-dev pip install -e . pip install pytest-cov pip install pytest-pycodestyle diff --git a/cs-config/cs_config/functions.py b/cs-config/cs_config/functions.py index f775868b..d7d90bea 100644 --- a/cs-config/cs_config/functions.py +++ b/cs-config/cs_config/functions.py @@ -87,7 +87,11 @@ def get_inputs(meta_param_dict): json.load( open( os.path.join( - "..", "..", "ogusa", "ogusa_default_parameters.json" + CUR_DIR, + "..", + "..", + "ogusa", + "ogusa_default_parameters.json", ) ) ) @@ -256,7 +260,11 @@ def run_model(meta_param_dict, adjustment): json.load( open( os.path.join( - "..", "..", "ogusa", "ogusa_default_parameters.json" + CUR_DIR, + "..", + "..", + "ogusa", + "ogusa_default_parameters.json", ) ) ) @@ -310,7 +318,11 @@ def run_model(meta_param_dict, adjustment): json.load( open( os.path.join( - "..", "..", "ogusa", "ogusa_default_parameters.json" + CUR_DIR, + "..", + "..", + "ogusa", + "ogusa_default_parameters.json", ) ) ) diff --git a/ogusa/data/PSID/psid1968to2015.RData b/data/PSID/psid1968to2015.RData similarity index 100% rename from ogusa/data/PSID/psid1968to2015.RData rename to data/PSID/psid1968to2015.RData diff --git a/ogusa/data/PSID/psid1968to2017.RData b/data/PSID/psid1968to2017.RData similarity index 100% rename from ogusa/data/PSID/psid1968to2017.RData rename to data/PSID/psid1968to2017.RData diff --git a/ogusa/data/PSID/psid_download.R b/data/PSID/psid_download.R similarity index 100% rename from ogusa/data/PSID/psid_download.R rename to data/PSID/psid_download.R diff --git a/ogusa/data/PSID/psid_lifetime_income.csv b/data/PSID/psid_lifetime_income.csv similarity index 100% rename from ogusa/data/PSID/psid_lifetime_income.csv rename to data/PSID/psid_lifetime_income.csv diff --git a/environment.yml b/environment.yml index d88ba56f..004feaee 100644 --- a/environment.yml +++ b/environment.yml @@ -2,9 +2,9 @@ name: ogusa-dev channels: - conda-forge dependencies: -- python>=3.7.7, <3.11 +- python>=3.7.7 - numpy -- psutil +- numba>=0.54 - scipy>=1.5.0 - pandas>=1.2.5 - matplotlib diff --git a/examples/run_og_usa.py b/examples/run_og_usa.py index 1444205b..1a56e9af 100644 --- a/examples/run_og_usa.py +++ b/examples/run_og_usa.py @@ -45,11 +45,18 @@ def main(): ) ) ) - p.age_specific = False p.tax_func_type = "GS" - c1 = Calibration( - p, iit_reform={}, estimate_tax_functions=True, client=client - ) + c = Calibration(p, estimate_tax_functions=True, client=client) + d = c.get_dict() + # # additional parameters to change + updated_params = { + "etr_params": d["etr_params"], + "mtrx_params": d["mtrx_params"], + "mtry_params": d["mtry_params"], + "mean_income_data": d["mean_income_data"], + "frac_tax_payroll": d["frac_tax_payroll"], + } + p.update_specifications(updated_params) # Run model start_time = time.time() runner(p, time_path=True, client=client) @@ -87,16 +94,15 @@ def main(): ) ) ) - p2.age_specific = False p2.tax_func_type = "GS" # Use calibration class to estimate reform tax functions from - # Tax-Calculator, specifing reform for Tax-Calculator in iit_reform + # Tax-Calculator, specifying reform for Tax-Calculator in iit_reform c2 = Calibration( p2, iit_reform=iit_reform, estimate_tax_functions=True, client=client ) # update tax function parameters in Specifications Object d = c2.get_dict() - # additional parameters to change + # # additional parameters to change updated_params = { "cit_rate": [[0.35]], "etr_params": d["etr_params"], diff --git a/ogusa/bequest_transmission.py b/ogusa/bequest_transmission.py index a8280e98..a19280ae 100644 --- a/ogusa/bequest_transmission.py +++ b/ogusa/bequest_transmission.py @@ -1,83 +1,15 @@ import pandas as pd import numpy as np import matplotlib.pyplot as plt -from scipy.stats import kde import os +from ogusa.utils import MVKDE CURDIR = os.path.split(os.path.abspath(__file__))[0] -# Will need to do some smoothing with a KDE when estimate the matrix... -def MVKDE(S, J, proportion_matrix, filename=None, plot=False, bandwidth=0.25): - """ - Generates a Multivariate Kernel Density Estimator and returns a - matrix representing a probability distribution according to given - age categories, and ability type categories. - - Args: - S (scalar): the number of age groups in the model - J (scalar): the number of ability type groups in the model. - proportion_matrix (Numpy array): SxJ shaped array that - represents the proportions of the total going to each - (s,j) combination - filename (str): the file name to save image to - plot (bool): whether or not to save a plot of the probability - distribution generated by the kde or the proportion matrix - bandwidth (scalar): used in the smoothing of the kernel. Higher - bandwidth creates a smoother kernel. - - Returns: - estimator_scaled (Numpy array): SxJ shaped array that - that represents the smoothed distribution of proportions - going to each (s,j) - - """ - proportion_matrix_income = np.sum(proportion_matrix, axis=0) - proportion_matrix_age = np.sum(proportion_matrix, axis=1) - age_probs = np.random.multinomial(70000, proportion_matrix_age) - income_probs = np.random.multinomial(70000, proportion_matrix_income) - age_frequency = np.array([]) - income_frequency = np.array([]) - age_mesh = complex(str(S) + "j") - income_mesh = complex(str(J) + "j") - j = 18 - """creating a distribution of age values""" - for i in age_probs: - listit = np.ones(i) - listit *= j - age_frequency = np.append(age_frequency, listit) - j += 1 - - k = 1 - """creating a distribution of ability type values""" - for i in income_probs: - listit2 = np.ones(i) - listit2 *= k - income_frequency = np.append(income_frequency, listit2) - k += 1 - - freq_mat = np.vstack((age_frequency, income_frequency)).T - density = kde.gaussian_kde(freq_mat.T, bw_method=bandwidth) - age_min, income_min = freq_mat.min(axis=0) - age_max, income_max = freq_mat.max(axis=0) - agei, incomei = np.mgrid[ - age_min:age_max:age_mesh, income_min:income_max:income_mesh - ] - coords = np.vstack([item.ravel() for item in [agei, incomei]]) - estimator = density(coords).reshape(agei.shape) - estimator_scaled = estimator / float(np.sum(estimator)) - if plot: - fig = plt.figure() - ax = fig.add_subplot(projection="3d") - ax.plot_surface(agei, incomei, estimator_scaled, rstride=5) - ax.set_xlabel("Age") - ax.set_ylabel("Ability Types") - ax.set_zlabel("Received proportion of total bequests") - plt.savefig(filename) - return estimator_scaled - - -def get_bequest_matrix(graphs=False): +def get_bequest_matrix( + J=7, lambdas=np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]), graphs=True +): """ Returns S x J matrix representing the fraction of aggregate bequests that go to each household by age and lifetime income group. @@ -86,11 +18,11 @@ def get_bequest_matrix(graphs=False): # Create directory if output directory does not already exist CURDIR = os.path.split(os.path.abspath(__file__))[0] output_fldr = "csv_output_files" - output_dir = os.path.join(CURDIR, output_fldr) + output_dir = os.path.join(CURDIR, "..", "data", output_fldr) if not os.access(output_dir, os.F_OK): os.makedirs(output_dir) image_fldr = "images" - image_dir = os.path.join(CURDIR, image_fldr) + image_dir = os.path.join(CURDIR, "..", "data", image_fldr) if not os.access(image_dir, os.F_OK): os.makedirs(image_dir) @@ -103,7 +35,7 @@ def get_bequest_matrix(graphs=False): # os.path.join(CURDIR, "data", "PSID", "psid_lifetime_income.pkl") # ) df = pd.read_csv( - os.path.join(CURDIR, "data", "PSID", "psid_lifetime_income.csv") + os.path.join(CURDIR, "..", "data", "PSID", "psid_lifetime_income.csv") ) # Do some tabs with data file... @@ -122,29 +54,33 @@ def get_bequest_matrix(graphs=False): if graphs: # Total inheritances by year - df.groupby("year_data").mean().plot(y="inheritance") + df.groupby("year_data").mean(numeric_only=True).plot(y="inheritance") plt.savefig(os.path.join(image_dir, "inheritance_year.png")) - df.groupby("year_data").mean().plot(y="sum_inherit") + df.groupby("year_data").mean(numeric_only=True).plot(y="sum_inherit") plt.savefig(os.path.join(image_dir, "sum_inherit_year.png")) # not that summing up inheritances gives a much larger value than # taking the inheritance variable # Fraction of inheritances in a year by age # line plot - df[df["year_data"] >= 1988].groupby("age").mean().plot(y="net_wealth") + df[df["year_data"] >= 1988].groupby("age").mean( + numeric_only=True + ).plot(y="net_wealth") plt.savefig(os.path.join(image_dir, "net_wealth_age.png")) - df[df["year_data"] >= 1988].groupby("age").mean().plot(y="inheritance") + df[df["year_data"] >= 1988].groupby("age").mean( + numeric_only=True + ).plot(y="inheritance") plt.savefig(os.path.join(image_dir, "inheritance_age.png")) # Inheritances by lifetime income group # bar plot - df[df["year_data"] >= 1988].groupby("li_group").mean().plot.bar( - y="net_wealth" - ) + df[df["year_data"] >= 1988].groupby("li_group").mean( + numeric_only=True + ).plot.bar(y="net_wealth") plt.savefig(os.path.join(image_dir, "net_wealth_li.png")) - df[df["year_data"] >= 1988].groupby("li_group").mean().plot.bar( - y="inheritance" - ) + df[df["year_data"] >= 1988].groupby("li_group").mean( + numeric_only=True + ).plot.bar(y="inheritance") plt.savefig(os.path.join(image_dir, "inheritance_li.png")) # lifecycle plots with line for each ability type @@ -188,6 +124,19 @@ def get_bequest_matrix(graphs=False): plot=graphs, bandwidth=0.5, ) + + if (J == 10) and np.array_equal( + np.squeeze(lambdas[:6]), np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09]) + ): + kde_matrix_new = np.zeros((80, J)) + kde_matrix_new[:, :6] = kde_matrix[:, :6] + kde_matrix_new[:, 6:] = ( + kde_matrix[:, 6:].sum(axis=1).reshape(80, 1) + * np.tile(np.reshape(lambdas[6:], (1, 4)), (80, 1)) + / lambdas[6:].sum() + ) + kde_matrix = kde_matrix_new + np.savetxt( os.path.join(output_dir, "bequest_matrix_kde.csv"), kde_matrix, diff --git a/ogusa/calibrate.py b/ogusa/calibrate.py index 6ad1b4e1..e7676f2b 100644 --- a/ogusa/calibrate.py +++ b/ogusa/calibrate.py @@ -51,17 +51,19 @@ def __init__( self.macro_params = macro_params.get_macro_params() # eta estimation - self.eta = transfer_distribution.get_transfer_matrix() + self.eta = transfer_distribution.get_transfer_matrix(p.J, p.lambdas) # zeta estimation - self.zeta = bequest_transmission.get_bequest_matrix() + self.zeta = bequest_transmission.get_bequest_matrix(p.J, p.lambdas) # demographics self.demographic_params = demographics.get_pop_objs( - p.E, p.S, p.T, 1, 100, p.start_year + p.E, p.S, p.T, 1, 100, p.start_year - 1, p.start_year ) # demographics for 80 period lives (needed for getting e below) - demog80 = demographics.get_pop_objs(20, 80, p.T, 1, 100, p.start_year) + demog80 = demographics.get_pop_objs( + 20, 80, p.T, 1, 100, p.start_year - 1, p.start_year + ) # earnings profiles self.e = income.get_e_interp( diff --git a/ogusa/calibrate_chi_n.py b/ogusa/calibrate_chi_n.py index c700300c..2e217f24 100644 --- a/ogusa/calibrate_chi_n.py +++ b/ogusa/calibrate_chi_n.py @@ -42,13 +42,15 @@ from ogcore import SS from ogcore import utils +CUR_PATH = os.path.split(os.path.abspath(__file__))[0] + def chi_estimate( income_tax_params, ss_params, iterative_params, chi_guesses, - baseline_dir="./OUTPUT", + baseline_dir=os.path.join(CUR_PATH, "..", "data", "OUTPUT"), ): """ -------------------------------------------------------------------- @@ -257,7 +259,7 @@ def chi_estimate( np.dot(np.dot(deriv_moments.T, W), deriv_moments) ) std_errors_chi = (np.diag(VCV_params)) ** (1 / 2.0) - sd_dir = os.path.join(baseline_dir, "Calibration/chi_std_errors.pkl") + sd_dir = os.path.join(baseline_dir, "Calibration", "chi_std_errors.pkl") with open(sd_dir, "wb") as f: pickle.dump(std_errors_chi, f) diff --git a/ogusa/demographics.py b/ogusa/demographics.py index e7a6bc82..d0b0cd82 100644 --- a/ogusa/demographics.py +++ b/ogusa/demographics.py @@ -9,12 +9,16 @@ import numpy as np import scipy.optimize as opt import pandas as pd +import matplotlib.pyplot as plt +from ogusa.utils import get_legacy_session from ogcore import parameter_plots as pp - +START_YEAR = 2023 +END_YEAR = 2023 +UN_COUNTRY_CODE = "840" # UN code for USA # create output director for figures CUR_PATH = os.path.split(os.path.abspath(__file__))[0] -OUTPUT_DIR = os.path.join(CUR_PATH, "OUTPUT", "Demographics") +OUTPUT_DIR = os.path.join(CUR_PATH, "..", "data", "OUTPUT", "Demographics") if os.access(OUTPUT_DIR, os.F_OK) is False: os.makedirs(OUTPUT_DIR) @@ -26,19 +30,99 @@ """ -def get_fert(totpers, min_yr, max_yr, graph=False): +def get_un_data( + variable_code, + country_id=UN_COUNTRY_CODE, + start_year=START_YEAR, + end_year=END_YEAR, +): + """ + This function retrieves data from the United Nations Data Portal API + for UN population data (see + https://population.un.org/dataportal/about/dataapi) + + Args: + variable_code (str): variable code for UN data + country_id (str): country id for UN data + start_year (int): start year for UN data + end_year (int): end year for UN data + + Returns: + df (Pandas DataFrame): DataFrame of UN data + """ + target = ( + "https://population.un.org/dataportalapi/api/v1/data/indicators/" + + variable_code + + "/locations/" + + country_id + + "/start/" + + str(start_year) + + "/end/" + + str(end_year) + ) + + # get data from url + response = get_legacy_session().get(target) + # Check if the request was successful before processing + if response.status_code == 200: + # Converts call into JSON + j = response.json() + # Convert JSON into a pandas DataFrame. + # pd.json_normalize flattens the JSON to accommodate nested lists + # within the JSON structure + df = pd.json_normalize(j["data"]) + # Loop until there are new pages with data + while j["nextPage"] is not None: + # Reset the target to the next page + target = j["nextPage"] + # call the API for the next page + response = get_legacy_session().get(target) + # Convert response to JSON format + j = response.json() + # Store the next page in a data frame + df_temp = pd.json_normalize(j["data"]) + # Append next page to the data frame + df = pd.concat([df, df_temp]) + # keep just what is needed from data + df = df[df.variant == "Median"] + df = df[df.sex == "Both sexes"][["timeLabel", "ageLabel", "value"]] + df.rename( + {"timeLabel": "year", "ageLabel": "age"}, axis=1, inplace=True + ) + df.loc[df.age == "100+", "age"] = 100 + df.age = df.age.astype(int) + df.year = df.year.astype(int) + df = df[df.age <= 100] + else: + print( + f"Failed to retrieve population data. HTTP status code: {response.status_code}" + ) + assert False + + return df + + +def get_fert( + totpers=100, + min_age=0, + max_age=100, + country_id=UN_COUNTRY_CODE, + start_year=START_YEAR, + end_year=END_YEAR, + graph=False, +): """ This function generates a vector of fertility rates by model period - age that corresponds to the fertility rate data by age in years - using data from the National Center for Health Statistics National - Vital Statistic System: - https://www.cdc.gov/nchs/nvss/cohort_fertility_tables.htm + age that corresponds to the fertility rate data by age in years. Args: totpers (int): total number of agent life periods (E+S), >= 3 - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, + min_age (int): age in years at which agents are born, >= 0 + max_age (int): age in years at which agents die with certainty, >= 4 + country_id (str): country id for UN data + start_year (int): start year for UN data + end_year (int): end year for UN data graph (bool): =True if want graphical output Returns: @@ -46,71 +130,74 @@ def get_fert(totpers, min_yr, max_yr, graph=False): of life """ - # Read raw data from NCHS - raw = pd.read_csv( - "ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/nvss/birth/" - + "cohort/Table01.csv", - skiprows=4, - ) - # keep only latest year in data - fert_data = raw[raw["Calendar year"] == 2005] - fert_data = fert_data[fert_data["Race of women"] == "All races 1"][ - ["Current age of women", "Live-birth order total"] - ] - fert_data.rename( - columns={ - "Current age of women": "Age", - "Live-birth order total": "Births per 1000", - }, - inplace=True, - ) - fert_rates_all = np.append( - np.append( - np.zeros(int(fert_data.Age.min()) - min_yr), - fert_data["Births per 1000"].values, - ), - np.zeros(int(max_yr - fert_data.Age.max())), + # Read UN data + df = get_un_data( + "68", country_id=country_id, start_year=start_year, end_year=end_year ) - # divide by 2000 because fertility rates per woman and we want per - # household - fert_rates_all = fert_rates_all / 2000 - # Calculate implied fertility rates in sub-bins of fert_rates_all. - fert_rates_mxyr = fert_rates_all[0:max_yr] - num_sub_bins = int(100) - len_subbins = (np.float64((max_yr - min_yr + 1) * num_sub_bins)) / totpers - fert_rates_sub = np.zeros(num_sub_bins * max_yr, dtype=float) - for i in range(max_yr): - fert_rates_sub[i * num_sub_bins : (i + 1) * num_sub_bins] = 1 - ( - (1 - fert_rates_mxyr[i]) ** (1.0 / num_sub_bins) - ) - fert_rates = np.zeros(totpers) - end_sub_bin = 0 - for i in range(totpers): - beg_sub_bin = int(end_sub_bin) - end_sub_bin = int(np.rint((i + 1) * len_subbins)) - fert_rates[i] = ( - 1 - (1 - (fert_rates_sub[beg_sub_bin:end_sub_bin])).prod() - ) + # put in vector + fert_rates = df.value.values + # fill in with zeros for ages < 15 and > 49 + # NOTE: this assumes min_year < 15 and max_age > 49 + fert_rates = np.append(fert_rates, np.zeros(max_age - 49)) + fert_rates = np.append(np.zeros(15 - min_age), fert_rates) + # divide by 1000 because fertility rates are number of births per + # 1000 woman and we want births per person (might update to account + # from fraction men more correctly - below assumes 50/50 men and women) + fert_rates = fert_rates / 2000 + # Rebin data in the case that model period not equal to one calendar + # year + fert_rates = pop_rebin(fert_rates, totpers) # if graph: # need to fix plot function for new data output - # pp.plot_fert_rates(fert_rates, age_midp, totpers, min_yr, max_yr, - # fert_data, fert_rates, output_dir=OUTPUT_DIR) + # pp.plot_fert_rates(fert_rates, age_midp, totpers, min_age, max_age, + # fert_rates, fert_rates, output_dir=OUTPUT_DIR) + + if graph: + output_dir = OUTPUT_DIR + # Using pyplot here until update to OG-Core mort rates plotting function + plt.plot( + np.arange(totpers), + fert_rates, + ) + plt.xlabel(r"Age $s$") + plt.ylabel(r"Fertility rate") + plt.legend(loc="upper left") + fontdict = {"fontsize": 9} + plt.text( + -5, + -0.2, + "Source: United Nations Population Prospects.", + **fontdict, + ) + plt.tight_layout(rect=(0, 0.03, 1, 1)) + output_path = os.path.join(output_dir, "fert_rates") + plt.savefig(output_path) + plt.close() return fert_rates -def get_mort(totpers, min_yr, max_yr, graph=False): +def get_mort( + totpers=100, + min_age=0, + max_age=100, + country_id=UN_COUNTRY_CODE, + start_year=START_YEAR, + end_year=END_YEAR, + graph=False, +): """ This function generates a vector of mortality rates by model period age. - Source: Male and Female death probabilities Actuarial Life table, - Social Security Administration Args: totpers (int): total number of agent life periods (E+S), >= 3 - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, + min_age (int): age in years at which agents are born, >= 0 + max_age (int): age in years at which agents die with certainty, >= 4 + country_id (str): country id for UN data + start_year (int): start year for UN data + end_year (int): end year for UN data graph (bool): =True if want graphical output Returns: @@ -119,85 +206,47 @@ def get_mort(totpers, min_yr, max_yr, graph=False): infmort_rate (scalar): infant mortality rate """ - # Get mortality rate by age data - infmort_rate = 0.00566 # taken from 2018 U.S. infant mortality rate - # https://www.cdc.gov/nchs/products/databriefs/db355.htm - raw_data_male = pd.read_csv( - "https://www.ssa.gov/oact/HistEst/PerLifeTables/2016/" - + "PerLifeTables_M_Alt2_TR2016.csv", - thousands=",", - skiprows=4, + # Read UN data + df = get_un_data( + "80", country_id=country_id, start_year=start_year, end_year=end_year ) - raw_data_female = pd.read_csv( - "https://www.ssa.gov/oact/HistEst/PerLifeTables/2016/" - + "PerLifeTables_M_Alt2_TR2016.csv", - thousands=",", - skiprows=4, - ) - raw_data_male.rename( - columns={ - "x": "Age", - "q(x)": "Male Mort. Rate", - "l(x)": "Num. Male Lives", - }, - inplace=True, - ) - raw_data_male = raw_data_male[ - ["Year", "Age", "Male Mort. Rate", "Num. Male Lives"] - ] - raw_data_female.rename( - columns={ - "x": "Age", - "q(x)": "Female Mort. Rate", - "l(x)": "Num. Female Lives", - }, - inplace=True, - ) - raw_data_female = raw_data_female[ - ["Year", "Age", "Female Mort. Rate", "Num. Female Lives"] - ] - raw_data = raw_data_male.merge(raw_data_female, on=["Year", "Age"]) - mort_data = raw_data[raw_data["Year"] == 2015] - age_year_all = mort_data["Age"].values + 1 - mort_rates_all = ( - ( - (mort_data["Male Mort. Rate"] * mort_data["Num. Male Lives"]) - + (mort_data["Female Mort. Rate"] * mort_data["Num. Female Lives"]) - ) - / (mort_data["Num. Male Lives"] + mort_data["Num. Female Lives"]) - ).values - age_year_all = age_year_all[np.isfinite(mort_rates_all)] - mort_rates_all = mort_rates_all[np.isfinite(mort_rates_all)] - # Calculate implied mortality rates in sub-bins of mort_rates_all. - mort_rates_mxyr = mort_rates_all[0:max_yr] - num_sub_bins = int(100) - len_subbins = (np.float64((max_yr - min_yr + 1) * num_sub_bins)) / totpers - mort_rates_sub = np.zeros(num_sub_bins * max_yr, dtype=float) - for i in range(max_yr): - mort_rates_sub[i * num_sub_bins : (i + 1) * num_sub_bins] = 1 - ( - (1 - mort_rates_mxyr[i]) ** (1.0 / num_sub_bins) - ) - mort_rates = np.zeros(totpers) - end_sub_bin = 0 - for i in range(totpers): - beg_sub_bin = int(end_sub_bin) - end_sub_bin = int(np.rint((i + 1) * len_subbins)) - mort_rates[i] = ( - 1 - (1 - (mort_rates_sub[beg_sub_bin:end_sub_bin])).prod() - ) - mort_rates[-1] = 1 # Mortality rate in last period is set to 1 + # put in vector + mort_rates_data = df.value.values + # In UN data, mortality rates for 0 year olds are the infant + # mortality rates + infmort_rate = mort_rates_data[0] + # Rebin data in the case that model period not equal to one calendar + # year + mort_rates = pop_rebin(mort_rates_data[1:], totpers) + + # Mortality rate in last period is set to 1 + mort_rates[-1] = 1 if graph: - pp.plot_mort_rates_data( - totpers, - min_yr, - max_yr, - age_year_all, - mort_rates_all, - infmort_rate, - mort_rates, - output_dir=OUTPUT_DIR, + output_dir = OUTPUT_DIR + # Using pyplot here until update to OG-Core mort rates plotting function + plt.plot( + df.age.values, + mort_rates_data, ) + plt.xlabel(r"Age $s$") + plt.ylabel(r"Mortality rate $\rho_{s}$") + plt.legend(loc="upper left") + fontdict = {"fontsize": 9} + plt.text( + -5, + -0.2, + "Source: United Nations Population Prospects.", + **fontdict, + ) + plt.tight_layout(rect=(0, 0.03, 1, 1)) + # Save or return figure + if output_dir: + output_path = os.path.join(output_dir, "mort_rates") + plt.savefig(output_path) + plt.close() + else: + plt.show() return mort_rates, infmort_rate @@ -221,6 +270,7 @@ def pop_rebin(curr_pop_dist, totpers_new): """ # Number of periods in original data assert totpers_new >= 3 + # Number of periods in original data totpers_orig = len(curr_pop_dist) if int(totpers_new) == totpers_orig: curr_pop_new = curr_pop_dist @@ -243,22 +293,29 @@ def pop_rebin(curr_pop_dist, totpers_new): return curr_pop_new -def get_imm_resid(totpers, min_yr, max_yr): +def get_imm_rates( + totpers=100, + min_age=0, + max_age=100, + country_id=UN_COUNTRY_CODE, + start_year=START_YEAR, + end_year=END_YEAR, + graph=False, +): """ Calculate immigration rates by age as a residual given population levels in different periods, then output average calculated immigration rate. We have to replace the first mortality rate in this function in order to adjust the first implied immigration rate - (Source: Population data come Census National Population Characteristics - 2010-2019, Annual Estimates of the Resident Population by Single - Year of Age and Sex for the United States: April 1, 2010 to - July 1, 2019 (NC-EST2019-AGESEX-RES)) Args: totpers (int): total number of agent life periods (E+S), >= 3 - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, + min_age (int): age in years at which agents are born, >= 0 + max_age (int): age in years at which agents die with certainty, >= 4 + country_id (str): country id for UN data + start_year (int): start year for UN data + end_year (int): end year for UN data graph (bool): =True if want graphical output Returns: @@ -266,70 +323,90 @@ def get_imm_resid(totpers, min_yr, max_yr): each period of life, length E+S """ - pop_data = pd.read_csv( - "https://www2.census.gov/programs-surveys/popest/" - + "technical-documentation/file-layouts/2010-2019/" - + "nc-est2019-agesex-res.csv" + # Read UN data + num_years = 4 # note that code below only uses four years, in future, this should be more flexbile + imm_start_year = ( + start_year - num_years + ) # year to start finding residual imm rate + imm_end_year = ( + start_year + num_years - 1 + ) # last year to find residual imm rate + df = get_un_data( + "47", + country_id=country_id, + start_year=imm_start_year, + end_year=imm_end_year, ) - pop_data = pop_data[pop_data["SEX"] == 0][ - [ - "AGE", - "POPESTIMATE2016", - "POPESTIMATE2017", - "POPESTIMATE2018", - "POPESTIMATE2019", - ] - ] - pop_data.rename( - columns={ - "AGE": "Age", - "POPESTIMATE2016": "2016", - "POPESTIMATE2017": "2017", - "POPESTIMATE2018": "2018", - "POPESTIMATE2019": "2019", - }, - inplace=True, + + # separate pop dist by year and put into dictionary of arrays + pop_dict = {} + for t in range(num_years): + pop_dist = df[ + (df.year == start_year + t) & (df.age <= 100) & (df.age > 0) + ].value.values + pop_dict[t] = pop_rebin(pop_dist, totpers) + pop_dict[t] = pop_dist + + # Create num_years - 1 years of estimated immigration rates for youngest age + # individuals + imm_mat = np.zeros((num_years - 1, totpers)) + pop_list = [] + for t in range(num_years): + pop_list.append(pop_dict[t][0]) + pop11vec = np.array(pop_list[:-1]) + pop21vec = np.array(pop_list[1:]) + fert_rates = get_fert( + totpers, min_age, max_age, country_id, start_year, end_year, False ) - pop_data_samp = pop_data[ - (pop_data["Age"] >= min_yr - 1) & (pop_data["Age"] <= max_yr - 1) - ] - pop_2016, pop_2017, pop_2018, pop_2019 = ( - np.array(pop_data_samp["2016"], dtype="f"), - np.array(pop_data_samp["2017"], dtype="f"), - np.array(pop_data_samp["2018"], dtype="f"), - np.array(pop_data_samp["2019"], dtype="f"), + mort_rates, infmort_rate = get_mort( + totpers, min_age, max_age, country_id, start_year, end_year, False ) - pop_2016_EpS = pop_rebin(pop_2016, totpers) - pop_2017_EpS = pop_rebin(pop_2017, totpers) - pop_2018_EpS = pop_rebin(pop_2018, totpers) - pop_2019_EpS = pop_rebin(pop_2019, totpers) - # Create three years of estimated immigration rates for youngest age - # individuals - imm_mat = np.zeros((3, totpers)) - pop11vec = np.array([pop_2016_EpS[0], pop_2017_EpS[0], pop_2018_EpS[0]]) - pop21vec = np.array([pop_2017_EpS[0], pop_2018_EpS[0], pop_2019_EpS[0]]) - fert_rates = get_fert(totpers, min_yr, max_yr, False) - mort_rates, infmort_rate = get_mort(totpers, min_yr, max_yr, False) newbornvec = np.dot( - fert_rates, np.vstack((pop_2016_EpS, pop_2017_EpS, pop_2018_EpS)).T + fert_rates, np.vstack((pop_dict[0], pop_dict[1], pop_dict[2])).T ) imm_mat[:, 0] = (pop21vec - (1 - infmort_rate) * newbornvec) / pop11vec - # Estimate 3 years of immigration rates for all other-aged + # Estimate num_years - 1 years of immigration rates for all other-aged # individuals - pop17mat = np.vstack( - (pop_2016_EpS[:-1], pop_2017_EpS[:-1], pop_2018_EpS[:-1]) + pop_mat_dict = {} + pop_mat_dict[0] = np.vstack( + (pop_dict[0][:-1], pop_dict[1][:-1], pop_dict[2][:-1]) ) - pop18mat = np.vstack( - (pop_2016_EpS[1:], pop_2017_EpS[1:], pop_2018_EpS[1:]) + pop_mat_dict[1] = np.vstack( + (pop_dict[0][1:], pop_dict[1][1:], pop_dict[2][1:]) ) - pop19mat = np.vstack( - (pop_2017_EpS[1:], pop_2018_EpS[1:], pop_2019_EpS[1:]) + pop_mat_dict[2] = np.vstack( + (pop_dict[1][1:], pop_dict[2][1:], pop_dict[3][1:]) ) - mort_mat = np.tile(mort_rates[:-1], (3, 1)) - imm_mat[:, 1:] = (pop19mat - (1 - mort_mat) * pop17mat) / pop18mat + mort_mat = np.tile(mort_rates[:-1], (num_years - 1, 1)) + imm_mat[:, 1:] = ( + pop_mat_dict[2] - (1 - mort_mat) * pop_mat_dict[0] + ) / pop_mat_dict[1] # Final estimated immigration rates are the averages over 3 years imm_rates = imm_mat.mean(axis=0) + if graph: + output_dir = OUTPUT_DIR + # Using pyplot here until update to OG-Core mort rates plotting function + plt.plot( + np.arange(totpers), + imm_rates, + label="Estimated", + ) + plt.xlabel(r"Age $s$") + plt.ylabel(r"Immigration Rates") + plt.legend(loc="upper left") + fontdict = {"fontsize": 9} + plt.text( + -5, + -0.2, + "Source: United Nations Population Prospects.", + **fontdict, + ) + plt.tight_layout(rect=(0, 0.03, 1, 1)) + output_path = os.path.join(output_dir, "imm_rates_w_un_data.png") + plt.savefig(output_path) + plt.close() + return imm_rates @@ -339,7 +416,7 @@ def immsolve(imm_rates, *args): difference in two consecutive periods stationary population distributions. This vector of differences is the zero-function objective used to solve for the immigration rates vector, similar to - the original immigration rates vector from get_imm_resid(), that + the original immigration rates vector from get_imm_rates(), that sets the steady-state population distribution by age equal to the population distribution in period int(1.5*S) @@ -369,7 +446,17 @@ def immsolve(imm_rates, *args): return omega_errs -def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): +def get_pop_objs( + E=20, + S=80, + T=320, + min_age=0, + max_age=100, + data_year=START_YEAR - 1, + model_year=START_YEAR, + country_id=UN_COUNTRY_CODE, + GraphDiag=True, +): """ This function produces the demographics objects to be used in the OG-USA model package. @@ -380,11 +467,12 @@ def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): S (int): number of model periods in which agent is economically active, >= 3 T (int): number of periods to be simulated in TPI, > 2*S - min_yr (int): age in years at which agents are born, >= 0 - max_yr (int): age in years at which agents die with certainty, + min_age (int): age in years at which agents are born, >= 0 + max_age (int): age in years at which agents die with certainty, >= 4 - curr_year (int): current year for which analysis will begin, + model_year (int): current year for which analysis will begin, >= 2016 + country_id (str): country id for UN data GraphDiag (bool): =True if want graphical output and printed diagnostics @@ -404,12 +492,24 @@ def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): path, length T + S """ - assert curr_year >= 2019 - # age_per = np.linspace(min_yr, max_yr, E+S) - fert_rates = get_fert(E + S, min_yr, max_yr, graph=False) - mort_rates, infmort_rate = get_mort(E + S, min_yr, max_yr, graph=False) + print("Model year = ", model_year, " Data year = ", data_year) + assert model_year >= 2011 and model_year <= 2100 + assert data_year >= 2011 and data_year <= 2100 + # need data year to be before model year to get omega_S_preTP + assert data_year < model_year + + # Get fertility, mortality, and immigration rates + # will be used to generate population distribution in future years + fert_rates = get_fert( + E + S, min_age, max_age, country_id, data_year, data_year + ) + mort_rates, infmort_rate = get_mort( + E + S, min_age, max_age, country_id, data_year, data_year + ) mort_rates_S = mort_rates[-S:] - imm_rates_orig = get_imm_resid(E + S, min_yr, max_yr) + imm_rates_orig = get_imm_rates( + E + S, min_age, max_age, country_id, data_year, data_year + ) OMEGA_orig = np.zeros((E + S, E + S)) OMEGA_orig[0, :] = (1 - infmort_rate) * fert_rates + np.hstack( (imm_rates_orig[0], np.zeros(E + S - 1)) @@ -429,56 +529,34 @@ def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): # Generate time path of the nonstationary population distribution omega_path_lev = np.zeros((E + S, T + S)) - pop_data = pd.read_csv( - "https://www2.census.gov/programs-surveys/popest/" - + "technical-documentation/file-layouts/2010-2019/" - + "nc-est2019-agesex-res.csv" + pop_data = get_un_data( + "47", country_id=country_id, start_year=data_year, end_year=data_year ) - pop_data = pop_data[pop_data["SEX"] == 0][ - [ - "AGE", - "POPESTIMATE2016", - "POPESTIMATE2017", - "POPESTIMATE2018", - "POPESTIMATE2019", - ] + # TODO: allow one to read in multiple years of UN forecast then + # extrapolate from the end of that + pop_data_sample = pop_data[ + (pop_data["age"] >= min_age - 1) & (pop_data["age"] <= max_age - 1) ] - pop_data.rename( - columns={ - "AGE": "Age", - "POPESTIMATE2016": "2016", - "POPESTIMATE2017": "2017", - "POPESTIMATE2018": "2018", - "POPESTIMATE2019": "2019", - }, - inplace=True, - ) - pop_data_samp = pop_data[ - (pop_data["Age"] >= min_yr - 1) & (pop_data["Age"] <= max_yr - 1) - ] - pop_2019 = np.array(pop_data_samp["2019"], dtype="f") + pop = pop_data_sample.value.values # Generate the current population distribution given that E+S might - # be less than max_yr-min_yr+1 + # be less than max_age-min_age+1 age_per_EpS = np.arange(1, E + S + 1) - pop_2019_EpS = pop_rebin(pop_2019, E + S) - pop_2019_pct = pop_2019_EpS / pop_2019_EpS.sum() - # Age most recent population data to the current year of analysis - pop_curr = pop_2019_EpS.copy() - data_year = 2019 + pop_EpS = pop_rebin(pop, E + S) + pop_pct = pop_EpS / pop_EpS.sum() + # Age the data to the model year + pop_curr = pop_EpS.copy() pop_next = np.dot(OMEGA_orig, pop_curr) g_n_curr = (pop_next[-S:].sum() - pop_curr[-S:].sum()) / pop_curr[ -S: - ].sum() # g_n in 2019 - pop_past = pop_curr # assume 2018-2019 pop - # Age the data to the current year - for per in range(curr_year - data_year): + ].sum() # g_n in data year + pop_past = pop_curr + for per in range(model_year - data_year): pop_next = np.dot(OMEGA_orig, pop_curr) g_n_curr = (pop_next[-S:].sum() - pop_curr[-S:].sum()) / pop_curr[ -S: ].sum() pop_past = pop_curr pop_curr = pop_next - # Generate time path of the population distribution omega_path_lev[:, 0] = pop_curr.copy() for per in range(1, T + S): @@ -560,7 +638,7 @@ def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): omegaSSvTmaxdiff = np.absolute(omega_SS_orig - omega_SSfx).max() if omegaSSvTmaxdiff > 0.0003: print( - "POP. WARNING: The maximimum absolute difference " + "POP. WARNING: The maximum absolute difference " + "between any two corresponding points in the original" + " and adjusted steady-state population " + "distributions is" @@ -666,10 +744,10 @@ def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): ) pp.plot_population_path( age_per_EpS, - pop_2019_pct, + pop_pct, omega_path_lev, omega_SSfx, - curr_year, + model_year, E, S, output_dir=OUTPUT_DIR, @@ -679,10 +757,9 @@ def get_pop_objs(E, S, T, min_yr, max_yr, curr_year, GraphDiag=False): # mort_rates_S, and g_n_path pop_dict = { "omega": omega_path_S.T, - "g_n_SS": g_n_SS, + "g_n_ss": g_n_SS, "omega_SS": omega_SSfx[-S:] / omega_SSfx[-S:].sum(), - "surv_rate": 1 - mort_rates_S, - "rho": mort_rates_S, + "rho": [mort_rates_S], "g_n": g_n_path, "imm_rates": imm_rates_mat.T, "omega_S_preTP": omega_S_preTP, diff --git a/ogusa/deterministic_profiles.py b/ogusa/deterministic_profiles.py index 1960f854..572cdfe7 100644 --- a/ogusa/deterministic_profiles.py +++ b/ogusa/deterministic_profiles.py @@ -8,7 +8,7 @@ # Create directory if output directory does not already exist cur_path = os.path.split(os.path.abspath(__file__))[0] output_fldr = "csv_output_files" -output_dir = os.path.join(cur_path, output_fldr) +output_dir = os.path.join(cur_path, "..", "data", output_fldr) if not os.access(output_dir, os.F_OK): os.makedirs(output_dir) @@ -28,7 +28,9 @@ def estimate_profiles(graphs=False): """ # Read in dataframe of PSID data df = ogcore.utils.safe_read_pickle( - os.path.join(cur_path, "data", "PSID", "psid_lifetime_income.pkl") + os.path.join( + cur_path, "..", "data", "PSID", "psid_lifetime_income.pkl" + ) ) model_results = { diff --git a/ogusa/estimate_beta_j.py b/ogusa/estimate_beta_j.py index 58263784..e542fb42 100644 --- a/ogusa/estimate_beta_j.py +++ b/ogusa/estimate_beta_j.py @@ -16,6 +16,8 @@ # Don't print output along the way of SS solution SS.VERBOSE = False +CUR_PATH = os.path.split(os.path.abspath(__file__))[0] + def beta_estimate( beta_initial_guesses, og_spec={}, two_step=False, client=None @@ -40,10 +42,8 @@ def beta_estimate( # initialize parameters object tax_func_path = os.path.join( + CUR_PATH, "..", - "..", - "dynamic", - "ogusa", "data", "tax_functions", "TxFuncEst_baseline_PUF.pkl", diff --git a/ogusa/macro_params.py b/ogusa/macro_params.py index 248611fc..6ee1b4d7 100644 --- a/ogusa/macro_params.py +++ b/ogusa/macro_params.py @@ -20,7 +20,7 @@ def get_macro_params(): # format is year (1940),month (1),day (1) start = datetime.datetime(1947, 1, 1) end = datetime.date.today() # go through today - baseline_date = datetime.datetime(2019, 3, 31) + baseline_date = datetime.datetime(2023, 3, 31) variable_dict = { "GDP Per Capita": "A939RX0Q048SBEA", @@ -32,6 +32,7 @@ def get_macro_params(): "Total gov transfer payments": "B087RC1Q027SBEA", "Social Security payments": "W823RC1", "Gov expenditures": "FGEXPND", + "Gov investment": "A782RC1Q027SBEA", "Gov interest payments": "A091RC1Q027SBEA", "Real GDP": "GDPC1", "Nominal GDP": "GDP", @@ -55,6 +56,7 @@ def get_macro_params(): "Total gov transfer payments", "Social Security payments", "Gov expenditures", + "Gov investment", "Gov interest payments", "GDP Per Capita", ] @@ -89,10 +91,17 @@ def get_macro_params(): fred_data_q["Gov expenditures"] - fred_data_q["Total gov transfer payments"] - fred_data_q["Gov interest payments"] + - fred_data_q["Gov investment"] + - fred_data_q["Social Security payments"] ) / fred_data_q["Nominal GDP"] ).loc[baseline_date] + # find alpha_I + macro_parameters["alpha_I"] = pd.Series( + fred_data_q["Gov investment"] / fred_data_q["Nominal GDP"] + ).loc[baseline_date] + # find gamma macro_parameters["gamma"] = 1 - fred_data_a["Labor share"].mean() @@ -102,18 +111,18 @@ def get_macro_params(): ) # # estimate r_gov_shift and r_gov_scale - rate_data = fred_data_d[ - ["10 year treasury rate", "BAA Corp Bond Rates"] - ].dropna() + rate_data = ( + fred_data_d[["10 year treasury rate", "BAA Corp Bond Rates"]].dropna()[ + -50: + ] + ) / 100 # divide by 100 bc data in percentage points rate_data["constant"] = np.ones(len(rate_data.index)) - # mod = PanelOLS(fred_data['10 year treasury rate'], - # fred_data[['constant', 'BAA Corp Bond Rates']]) mod = sm.OLS( rate_data["10 year treasury rate"], rate_data[["constant", "BAA Corp Bond Rates"]], ) res = mod.fit() - macro_parameters["r_gov_shift"] = res.params["BAA Corp Bond Rates"] - macro_parameters["r_gov_scale"] = res.params["constant"] + macro_parameters["r_gov_scale"] = res.params["BAA Corp Bond Rates"] + macro_parameters["r_gov_shift"] = res.params["constant"] * -1 return macro_parameters diff --git a/ogusa/ogusa_default_parameters.json b/ogusa/ogusa_default_parameters.json index dc557f8d..4b95da89 100644 --- a/ogusa/ogusa_default_parameters.json +++ b/ogusa/ogusa_default_parameters.json @@ -1,128 +1,39 @@ { "frisch": 0.4, - "g_y_annual": 0.03, + "g_y_annual": 0.019734776526678842, "S": 80, - "J": 7, + "J": 10, "T": 320, "lambdas": [ - 0.25, - 0.25, - 0.2, - 0.1, - 0.1, - 0.09, - 0.01 - ], - "e": [[ - 0.40547084393622496, - 0.2716226561254501, - 0.2899049902450634, - 0.3408270212293285, - 0.4240941105541254, - 0.6918316180259083, - 1.7928884714308537 - ], - [ - 0.3989292087449047, - 0.28582607585554826, - 0.3164066582534865, - 0.37660018951787877, - 0.4688091703774397, - 0.7383993769886736, - 1.9453769063354296 - ], - [0.3934839697658682, 0.3005408694211688, 0.34414451450348615, 0.41437038327356684, 0.5159963384642161, 0.7880580380211653, 2.108123995775691], - [0.38904985581571017, 0.31576027411207386, 0.37305123126821094, 0.45404197942332586, 0.5655290922741096, 0.8409356150706266, 2.2813924505473535], - [0.38555157427242864, 0.3314745019446092, 0.40304688271340083, 0.4954957217162605, 0.6172516179572736, 0.8971532721794839, 2.4653855141888776], - [0.38292234706886663, 0.3476705233975517, 0.4340394373563952, 0.5385894969065246, 0.6709800732634063, 0.9568229790339212, 2.6602362655237437], - [0.38110263782211984, 0.36433185776751287, 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0.13840940498417537, 0.4090171806274872, 0.0, 0.06759191478116817, 0.1066928436924087, 0.000708174902030072, 0.0030232433693507853, 0.06759191478116817], [8.941306086533063e-13, 8.756553907454659e-18, 3.388619595801623e-24, 8.463716393533509e-07, 0.13840940498417537, 0.4090171806274872, 0.0, 0.06759191478116817, 0.1066928436924087, 0.000708174902030072, 0.0030232433693507853, 0.06759191478116817], [8.941306086533063e-13, 8.756553907454659e-18, 3.388619595801623e-24, 8.463716393533509e-07, 0.13840940498417537, 0.4090171806274872, 0.0, 0.06759191478116817, 0.1066928436924087, 0.000708174902030072, 0.0030232433693507853, 0.06759191478116817]]], + "mean_income_data": 69793.52147078, + "frac_tax_payroll": [0.46456878, 0.46484191, 0.46361317, 0.43629773, 0.43492326, 0.43414999, 0.43283517, 0.43150988, 0.43057067] } diff --git a/ogusa/psid_data_setup.py b/ogusa/psid_data_setup.py index e49dcb9a..5fb87182 100644 --- a/ogusa/psid_data_setup.py +++ b/ogusa/psid_data_setup.py @@ -33,7 +33,7 @@ def prep_data(data="psid1968to2015.RData"): income groups defined """ # Read data from R into pandas dataframe - r["load"](os.path.join(CURDIR, "data", "PSID", data)) + r["load"](os.path.join(CURDIR, "..", "data", "PSID", data)) raw_df = r("psid_df") # Create unique identifier for each household @@ -443,7 +443,7 @@ def prep_data(data="psid1968to2015.RData"): first_stage_model_results, open( os.path.join( - CURDIR, "data", "PSID", "first_stage_reg_results.pkl" + CURDIR, "..", "data", "PSID", "first_stage_reg_results.pkl" ), "wb", ), @@ -456,7 +456,7 @@ def prep_data(data="psid1968to2015.RData"): panel_li.dropna(axis=0, how="all", inplace=True) print(panel_li.keys()) panel_li.to_csv( - os.path.join(CURDIR, "data", "PSID", "psid_lifetime_income.csv") + os.path.join(CURDIR, "..", "data", "PSID", "psid_lifetime_income.csv") ) return panel_li diff --git a/ogusa/psid_summ_stats.py b/ogusa/psid_summ_stats.py index dda1c1de..40e562f3 100644 --- a/ogusa/psid_summ_stats.py +++ b/ogusa/psid_summ_stats.py @@ -7,7 +7,7 @@ # Create directory if output directory does not already exist cur_path = os.path.split(os.path.abspath(__file__))[0] output_fldr = "csv_output_files" -output_dir = os.path.join(cur_path, output_fldr) +output_dir = os.path.join(cur_path, "..", "data", "PSID", output_fldr) if not os.access(output_dir, os.F_OK): os.makedirs(output_dir) @@ -19,7 +19,9 @@ # Read in dataframe of PSID data df = pickle.load( open( - os.path.join(cur_path, "psid_data_files", "psid_lifetime_income.pkl"), + os.path.join( + cur_path, "..", "data", "PSID", "psid_lifetime_income.pkl" + ), "rb", ) ) diff --git a/ogusa/transfer_distribution.py b/ogusa/transfer_distribution.py index 0ee0663b..20d04884 100644 --- a/ogusa/transfer_distribution.py +++ b/ogusa/transfer_distribution.py @@ -1,83 +1,15 @@ import pandas as pd import numpy as np import matplotlib.pyplot as plt -from scipy.stats import kde import os +from ogusa.utils import MVKDE CURDIR = os.path.split(os.path.abspath(__file__))[0] -# Will need to do some smoothing with a KDE when estimate the matrix... -def MVKDE(S, J, proportion_matrix, filename=None, plot=False, bandwidth=0.25): - """ - Generates a Multivariate Kernel Density Estimator and returns a - matrix representing a probability distribution according to given - age categories, and ability type categories. - - Args: - S (scalar): the number of age groups in the model - J (scalar): the number of ability type groups in the model. - proportion_matrix (Numpy array): SxJ shaped array that - represents the proportions of the total going to each - (s,j) combination - filename (str): the file name to save image to - plot (bool): whether or not to save a plot of the probability - distribution generated by the kde or the proportion matrix - bandwidth (scalar): used in the smoothing of the kernel. Higher - bandwidth creates a smoother kernel. - - Returns: - estimator_scaled (Numpy array): SxJ shaped array that - that represents the smoothed distribution of proportions - going to each (s,j) - - """ - proportion_matrix_income = np.sum(proportion_matrix, axis=0) - proportion_matrix_age = np.sum(proportion_matrix, axis=1) - age_probs = np.random.multinomial(70000, proportion_matrix_age) - income_probs = np.random.multinomial(70000, proportion_matrix_income) - age_frequency = np.array([]) - income_frequency = np.array([]) - age_mesh = complex(str(S) + "j") - income_mesh = complex(str(J) + "j") - j = 18 - """creating a distribution of age values""" - for i in age_probs: - listit = np.ones(i) - listit *= j - age_frequency = np.append(age_frequency, listit) - j += 1 - - k = 1 - """creating a distribution of ability type values""" - for i in income_probs: - listit2 = np.ones(i) - listit2 *= k - income_frequency = np.append(income_frequency, listit2) - k += 1 - - freq_mat = np.vstack((age_frequency, income_frequency)).T - density = kde.gaussian_kde(freq_mat.T, bw_method=bandwidth) - age_min, income_min = freq_mat.min(axis=0) - age_max, income_max = freq_mat.max(axis=0) - agei, incomei = np.mgrid[ - age_min:age_max:age_mesh, income_min:income_max:income_mesh - ] - coords = np.vstack([item.ravel() for item in [agei, incomei]]) - estimator = density(coords).reshape(agei.shape) - estimator_scaled = estimator / float(np.sum(estimator)) - if plot: - fig = plt.figure() - ax = fig.add_subplot(projection="3d") - ax.plot_surface(agei, incomei, estimator_scaled, rstride=5) - ax.set_xlabel("Age") - ax.set_ylabel("Ability Types") - ax.set_zlabel("Received proportion of total transfers") - plt.savefig(filename) - return estimator_scaled - - -def get_transfer_matrix(graphs=False): +def get_transfer_matrix( + J=7, lambdas=np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09, 0.01]), graphs=True +): """ Compute SxJ matrix representing the distribution of aggregate government transfers by age and lifetime income group. @@ -102,7 +34,7 @@ def get_transfer_matrix(graphs=False): # os.path.join(CURDIR, "data", "PSID", "psid_lifetime_income.pkl") # ) df = pd.read_csv( - os.path.join(CURDIR, "data", "PSID", "psid_lifetime_income.csv") + os.path.join(CURDIR, "..", "data", "PSID", "psid_lifetime_income.csv") ) # Do some tabs with data file... @@ -112,8 +44,8 @@ def get_transfer_matrix(graphs=False): ) df["sum_transfers"] = ( - df["other_familyunit_ssi_prior_year"] - + df["head_other_welfare_prior_year"] + # df["other_familyunit_ssi_prior_year"] # don't include SSI since OG-USA models separately + df["head_other_welfare_prior_year"] + df["spouse_other_welfare_prior_year"] + df["other_familyunit_other_welfare_prior_year"] + df["head_unemp_inc_prior_year"] @@ -123,26 +55,29 @@ def get_transfer_matrix(graphs=False): if graphs: # Total total_transfers by year - df.groupby("year_data").mean().plot(y="total_transfers") + df.groupby("year_data").mean(numeric_only=True).plot( + y="total_transfers" + ) plt.savefig(os.path.join(image_dir, "total_transfers_year.png")) - df.groupby("year_data").mean().plot(y="sum_transfers") + df.groupby("year_data").mean(numeric_only=True).plot(y="sum_transfers") plt.savefig(os.path.join(image_dir, "sum_transfers_year.png")) # note that the sum of transfer categories is much lower than the - # tranfers variable. The transfers variable goes more to high income + # "total transfers" variable. The transfers variable goes more to high income # and old, even though it says it excludes social security + # because of this, we'll use the "sum transfers" variable # Fraction of total_transfers in a year by age # line plot - df[df["year_data"] >= 1988].groupby("age").mean().plot( - y="total_transfers" - ) + df[df["year_data"] >= 1988].groupby("age").mean( + numeric_only=True + ).plot(y="total_transfers") plt.savefig(os.path.join(image_dir, "total_transfers_age.png")) # total_transfers by lifetime income group # bar plot - df[df["year_data"] >= 1988].groupby("li_group").mean().plot.bar( - y="total_transfers" - ) + df[df["year_data"] >= 1988].groupby("li_group").mean( + numeric_only=True + ).plot.bar(y="total_transfers") plt.savefig(os.path.join(image_dir, "total_transfers_li.png")) # lifecycle plots with line for each ability type @@ -164,19 +99,17 @@ def get_transfer_matrix(graphs=False): ).plot(legend=True) plt.savefig(os.path.join(image_dir, "sum_transfers_age_li.png")) - # Matrix Fraction of total_transfers in a year by age and lifetime_inc - total_transfers_matrix = pd.pivot_table( + # Matrix Fraction of sum_transfers in a year by age and lifetime_inc + transfers_matrix = pd.pivot_table( df[df["year_data"] >= 1988], - values="total_transfers", + values="sum_transfers", index="age", columns="li_group", aggfunc="sum", ) # replace NaN with zero - total_transfers_matrix.fillna(value=0, inplace=True) - total_transfers_matrix = ( - total_transfers_matrix / total_transfers_matrix.sum().sum() - ) + transfers_matrix.fillna(value=0, inplace=True) + transfers_matrix = transfers_matrix / transfers_matrix.sum().sum() # total_transfers_matrix.to_csv(os.path.join( # output_dir, 'transfer_matrix.csv')) @@ -184,13 +117,26 @@ def get_transfer_matrix(graphs=False): kde_matrix = MVKDE( 80, 7, - total_transfers_matrix.to_numpy(), - filename=os.path.join(image_dir, "total_transfers_kde.png"), + transfers_matrix.to_numpy(), + filename=os.path.join(image_dir, "sum_transfers_kde.png"), plot=True, bandwidth=0.5, ) + + if (J == 10) and np.array_equal( + np.squeeze(lambdas[:6]), np.array([0.25, 0.25, 0.2, 0.1, 0.1, 0.09]) + ): + kde_matrix_new = np.zeros((80, J)) + kde_matrix_new[:, :6] = kde_matrix[:, :6] + kde_matrix_new[:, 6:] = ( + kde_matrix[:, 6:].sum(axis=1).reshape(80, 1) + * np.tile(np.reshape(lambdas[6:], (1, 4)), (80, 1)) + / lambdas[6:].sum() + ) + kde_matrix = kde_matrix_new + np.savetxt( - os.path.join(output_dir, "total_transfers_kde.csv"), + os.path.join(output_dir, "sum_transfers_kde.csv"), kde_matrix, delimiter=",", ) diff --git a/ogusa/utils.py b/ogusa/utils.py index af94b37e..8520e37f 100644 --- a/ogusa/utils.py +++ b/ogusa/utils.py @@ -1,4 +1,10 @@ import pandas as pd +import numpy as np +from scipy.stats import kde +import matplotlib.pyplot as plt +import requests +import urllib3 +import ssl def read_cbo_forecast(): @@ -137,3 +143,109 @@ def read_cbo_forecast(): df_cbo.replace(to_replace="*", value=0.0, inplace=True) return df_cbo + + +# Will need to do some smoothing with a KDE when estimate the matrix... +def MVKDE( + S, + J, + proportion_matrix, + zaxis_label="Received proportion of total transfers", + filename=None, + plot=False, + bandwidth=0.25, +): + """ + Generates a Multivariate Kernel Density Estimator and returns a + matrix representing a probability distribution according to given + age categories, and ability type categories. + + Args: + S (scalar): the number of age groups in the model + J (scalar): the number of ability type groups in the model. + proportion_matrix (Numpy array): SxJ shaped array that + represents the proportions of the total going to each + (s,j) combination + filename (str): the file name to save image to + plot (bool): whether or not to save a plot of the probability + distribution generated by the kde or the proportion matrix + bandwidth (scalar): used in the smoothing of the kernel. Higher + bandwidth creates a smoother kernel. + + Returns: + estimator_scaled (Numpy array): SxJ shaped array that + that represents the smoothed distribution of proportions + going to each (s,j) + + """ + proportion_matrix_income = np.sum(proportion_matrix, axis=0) + proportion_matrix_age = np.sum(proportion_matrix, axis=1) + age_probs = np.random.multinomial(70000, proportion_matrix_age) + income_probs = np.random.multinomial(70000, proportion_matrix_income) + age_frequency = np.array([]) + income_frequency = np.array([]) + age_mesh = complex(str(S) + "j") + income_mesh = complex(str(J) + "j") + j = 18 + """creating a distribution of age values""" + for i in age_probs: + listit = np.ones(i) + listit *= j + age_frequency = np.append(age_frequency, listit) + j += 1 + + k = 1 + """creating a distribution of ability type values""" + for i in income_probs: + listit2 = np.ones(i) + listit2 *= k + income_frequency = np.append(income_frequency, listit2) + k += 1 + + freq_mat = np.vstack((age_frequency, income_frequency)).T + density = kde.gaussian_kde(freq_mat.T, bw_method=bandwidth) + age_min, income_min = freq_mat.min(axis=0) + age_max, income_max = freq_mat.max(axis=0) + agei, incomei = np.mgrid[ + age_min:age_max:age_mesh, income_min:income_max:income_mesh + ] + coords = np.vstack([item.ravel() for item in [agei, incomei]]) + estimator = density(coords).reshape(agei.shape) + estimator_scaled = estimator / float(np.sum(estimator)) + if plot: + fig = plt.figure() + ax = fig.add_subplot(projection="3d") + ax.plot_surface(agei, incomei, estimator_scaled, rstride=5) + ax.set_xlabel("Age") + ax.set_ylabel("Ability Types") + ax.set_zlabel(zaxis_label) + plt.savefig(filename) + return estimator_scaled + + +class CustomHttpAdapter(requests.adapters.HTTPAdapter): + """ + The UN Data Portal server doesn't support "RFC 5746 secure renegotiation". This causes and error when the client is using OpenSSL 3, which enforces that standard by default. + The fix is to create a custom SSL context that allows for legacy connections. This defines a function get_legacy_session() that should be used instead of requests(). + """ + + # "Transport adapter" that allows us to use custom ssl_context. + def __init__(self, ssl_context=None, **kwargs): + self.ssl_context = ssl_context + super().__init__(**kwargs) + + def init_poolmanager(self, connections, maxsize, block=False): + self.poolmanager = urllib3.poolmanager.PoolManager( + num_pools=connections, + maxsize=maxsize, + block=block, + ssl_context=self.ssl_context, + ) + + +def get_legacy_session(): + ctx = ssl.create_default_context(ssl.Purpose.SERVER_AUTH) + ctx.options |= 0x4 # OP_LEGACY_SERVER_CONNECT #in Python 3.12 you will be able to switch from 0x4 to ssl.OP_LEGACY_SERVER_CONNECT. + session = requests.session() + session.mount("https://", CustomHttpAdapter(ctx)) + return session diff --git a/setup.py b/setup.py index 8b86fc7d..4b75f717 100644 --- a/setup.py +++ b/setup.py @@ -19,7 +19,7 @@ packages=["ogusa"], package_data={"ogusa": ["ogusa_default_parameters.json", "data/PSID/*"]}, include_packages=True, - python_requires=">=3.7.7, <3.11", + python_requires=">=3.7.7", install_requires=[ "numpy", "psutil", @@ -51,6 +51,7 @@ "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Topic :: Software Development :: Libraries :: Python Modules", ], tests_require=["pytest"], diff --git a/ogusa/tests/test_calibrate.py b/tests/test_calibrate.py similarity index 100% rename from ogusa/tests/test_calibrate.py rename to tests/test_calibrate.py diff --git a/ogusa/tests/test_demographics.py b/tests/test_demographics.py similarity index 79% rename from ogusa/tests/test_demographics.py rename to tests/test_demographics.py index 3e368eb0..3be871fd 100644 --- a/ogusa/tests/test_demographics.py +++ b/tests/test_demographics.py @@ -12,7 +12,9 @@ def test_get_pop_objs(): T = int(round(4.0 * S)) start_year = 2019 - pop_dict = demographics.get_pop_objs(E, S, T, 1, 100, start_year, False) + pop_dict = demographics.get_pop_objs( + E, S, T, 1, 100, start_year - 1, start_year, GraphDiag=False + ) assert np.allclose(pop_dict["omega_SS"], pop_dict["omega"][-1, :]) @@ -26,7 +28,17 @@ def test_pop_smooth(): T = int(round(4.0 * S)) start_year = 2019 - pop_dict = demographics.get_pop_objs(E, S, T, 1, 100, start_year, False) + pop_dict = demographics.get_pop_objs( + E, + S, + T, + 1, + 100, + start_year - 1, + start_year, + country_id="840", + GraphDiag=False, + ) assert np.any( np.absolute(pop_dict["omega"][:-1, :] - pop_dict["omega"][1:, :]) @@ -46,7 +58,9 @@ def test_imm_smooth(): T = int(round(4.0 * S)) start_year = 2019 - pop_dict = demographics.get_pop_objs(E, S, T, 1, 100, start_year, False) + pop_dict = demographics.get_pop_objs( + E, S, T, 1, 100, start_year - 1, start_year, GraphDiag=False + ) assert np.any( np.absolute( @@ -81,7 +95,7 @@ def test_infant_mort(): mort_rates, infmort_rate = demographics.get_mort(100, 0, 100, graph=False) # check that infant mortality equals rate hardcoded into # demographics.py - assert infmort_rate == 0.00566 + assert infmort_rate == 0.00491958 def test_pop_rebin(): @@ -94,10 +108,10 @@ def test_pop_rebin(): assert rebinned_data.shape[0] == totpers_new -def test_get_imm_resid(): +def test_get_imm_rates(): """ Test of function to solve for immigration rates from population data """ S = 100 - imm_rates = demographics.get_imm_resid(S, 0, 100) + imm_rates = demographics.get_imm_rates(S, 0, 100) assert imm_rates.shape[0] == S diff --git a/ogusa/tests/test_get_micro_data.py b/tests/test_get_micro_data.py similarity index 100% rename from ogusa/tests/test_get_micro_data.py rename to tests/test_get_micro_data.py diff --git a/ogusa/tests/test_income.py b/tests/test_income.py similarity index 100% rename from ogusa/tests/test_income.py rename to tests/test_income.py diff --git a/ogusa/tests/test_io_data/TxFuncEst_policy.pkl b/tests/test_io_data/TxFuncEst_policy.pkl similarity index 100% rename from ogusa/tests/test_io_data/TxFuncEst_policy.pkl rename to tests/test_io_data/TxFuncEst_policy.pkl diff --git a/ogusa/tests/test_io_data/micro_data_dict_for_tests.pkl b/tests/test_io_data/micro_data_dict_for_tests.pkl similarity index 100% rename from ogusa/tests/test_io_data/micro_data_dict_for_tests.pkl rename to tests/test_io_data/micro_data_dict_for_tests.pkl diff --git a/ogusa/tests/test_io_data/mtr_combined_capinc_for_tests.csv b/tests/test_io_data/mtr_combined_capinc_for_tests.csv similarity index 100% rename from ogusa/tests/test_io_data/mtr_combined_capinc_for_tests.csv rename to tests/test_io_data/mtr_combined_capinc_for_tests.csv diff --git a/ogusa/tests/test_io_data/tax_dict_for_tests.pkl b/tests/test_io_data/tax_dict_for_tests.pkl similarity index 100% rename from ogusa/tests/test_io_data/tax_dict_for_tests.pkl rename to tests/test_io_data/tax_dict_for_tests.pkl diff --git a/ogusa/tests/test_psid_data_setup.py b/tests/test_psid_data_setup.py similarity index 100% rename from ogusa/tests/test_psid_data_setup.py rename to tests/test_psid_data_setup.py diff --git a/ogusa/tests/test_run_example.py b/tests/test_run_example.py similarity index 94% rename from ogusa/tests/test_run_example.py rename to tests/test_run_example.py index 100a0fff..e6698f42 100644 --- a/ogusa/tests/test_run_example.py +++ b/tests/test_run_example.py @@ -15,7 +15,7 @@ def call_run_og_usa(): cur_path = os.path.split(os.path.abspath(__file__))[0] path = Path(cur_path) - roe_fldr = os.path.join(path.parent.parent, "examples") + roe_fldr = os.path.join(path, "..", "examples") roe_file_path = os.path.join(roe_fldr, "run_og_usa.py") spec = importlib.util.spec_from_file_location( "run_og_usa.py", roe_file_path diff --git a/ogusa/tests/test_utils.py b/tests/test_utils.py similarity index 100% rename from ogusa/tests/test_utils.py rename to tests/test_utils.py diff --git a/ogusa/tests/test_wealth.py b/tests/test_wealth.py similarity index 100% rename from ogusa/tests/test_wealth.py rename to tests/test_wealth.py