diff --git a/lectures/_static/lecture_specific/hansen_singleton_1982/README.md b/lectures/_static/lecture_specific/hansen_singleton_1982/README.md new file mode 100644 index 000000000..c3c6a2519 --- /dev/null +++ b/lectures/_static/lecture_specific/hansen_singleton_1982/README.md @@ -0,0 +1,54 @@ +# Data — `hansen_singleton_1982` + +Vendored input data for the **Hansen–Singleton (1982)** lecture +(`lectures/hansen_singleton_1982.md`). + +The lecture reads `hansen_singleton_1982_data.csv` directly from GitHub instead +of querying the data providers while the book builds. This keeps the build +reproducible and removes its dependence on live FRED / Ken French endpoints +(which can be slow, rate-limited, or temporarily unavailable). + +## Sources + +| Quantity | Provider | Code / dataset | +| --- | --- | --- | +| Civilian noninstitutional population, 16+ | FRED | `CNP16OV` | +| Real PCE: nondurable goods (chain-type quantity index) | FRED | `DNDGRA3M086SBEA` | +| PCE: nondurable goods (chain-type price index) | FRED | `DNDGRG3M086SBEA` | +| Monthly market excess return `Mkt-RF` and risk-free rate `RF` | Ken French data library | `F-F_Research_Data_Factors` | + +- FRED — +- Ken French data library — + +## Sample + +Monthly, **1959-02 to 1978-12** (239 observations), matching the Hansen–Singleton +(1982) ND + VWR sample. One extra prior month is pulled to form the first +in-sample growth rate. + +## File: `hansen_singleton_1982_data.csv` + +Month-end `date` index plus: + +| Column | Definition | +| --- | --- | +| `gross_real_return` | gross real market return, `(1 + (Mkt-RF + RF)/100) / gross_inflation_cons` | +| `gross_cons_growth` | gross growth of per-capita real nondurable consumption | + +where `gross_inflation_cons` is the month-over-month gross inflation of the PCE +nondurables price deflator (`DNDGRG3M086SBEA`). It is used to deflate the +nominal return but is not itself a column of this CSV. + +## Regenerating + +```bash +python make_data.py +``` + +`make_data.py` needs only the standard library and pandas (both ship with +Anaconda). + +> **The committed CSV is a frozen snapshot.** FRED revises historical series and +> the Ken French library is updated over time, so regenerating may produce small +> differences from the committed values. Update the CSV deliberately, in its own +> commit, when you intend to refresh the data. diff --git a/lectures/_static/lecture_specific/hansen_singleton_1982/hansen_singleton_1982_data.csv b/lectures/_static/lecture_specific/hansen_singleton_1982/hansen_singleton_1982_data.csv new file mode 100644 index 000000000..9b2837537 --- /dev/null +++ b/lectures/_static/lecture_specific/hansen_singleton_1982/hansen_singleton_1982_data.csv @@ -0,0 +1,240 @@ +date,gross_real_return,gross_cons_growth +1959-02-28,1.0109579062397553,1.0006229224411516 +1959-03-31,1.00571436382525,1.0180902221035215 +1959-04-30,1.037975704918033,0.9817704517570749 +1959-05-31,1.0206154266958425,1.0184807553981456 +1959-06-30,0.997762130888052,0.9958074219092394 +1959-07-31,1.0332284787350057,0.993247983171137 +1959-08-31,0.9862179047619046,1.001525294171654 +1959-09-30,0.9525118058948054,1.00821529850278 +1959-10-31,1.0133797259977257,0.9992750451488477 +1959-11-30,1.02031276849642,0.9946087169562763 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used in the +lecture, and writes them to ``hansen_singleton_1982_data.csv`` next to this +script. + +Usage +----- + python make_data.py + +Requires only the standard library plus pandas (both ship with Anaconda). + +Note +---- +The committed CSV is a *frozen snapshot*. FRED revises historical series and +the Ken French library is updated over time, so re-running this script may +produce small differences from the committed data. See README.md. +""" +import io +import urllib.request +import zipfile +from pathlib import Path + +import pandas as pd + +FRED_CODES = { + "population_16plus": "CNP16OV", + "cons_nd_real_index": "DNDGRA3M086SBEA", + "cons_nd_price_index": "DNDGRG3M086SBEA", +} +START = "1959-02-01" +END = "1978-12-01" +OUTPUT = Path(__file__).with_name("hansen_singleton_1982_data.csv") + + +def read_fred(codes, start, end): + """Download FRED series as a date-indexed DataFrame (columns = codes).""" + base = "https://fred.stlouisfed.org/graph/fredgraph.csv" + columns = [] + for code in codes: + url = f"{base}?id={code}&cosd={start:%Y-%m-%d}&coed={end:%Y-%m-%d}" + columns.append( + pd.read_csv(url, index_col=0, parse_dates=True, na_values=".")) + fred = pd.concat(columns, axis=1).astype("float64") + fred.index.name = "DATE" + return fred + + +def read_famafrench_factors(start, end): + """Download the monthly Fama-French research factors (percent).""" + url = ("https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/" + "F-F_Research_Data_Factors_CSV.zip") + with urllib.request.urlopen(url) as response: + payload = response.read() + with zipfile.ZipFile(io.BytesIO(payload)) as archive: + text = archive.read(archive.namelist()[0]).decode("utf-8") + + # Preamble, then a monthly table (rows keyed by YYYYMM), then an annual + # table (rows keyed by YYYY). Keep the contiguous monthly block. + records = [] + for line in text.splitlines(): + cells = [cell.strip() for cell in line.split(",")] + key = cells[0] + if len(key) == 6 and key.isdigit(): + records.append([key] + [float(x) for x in cells[1:5]]) + elif records: + break + factors = pd.DataFrame( + records, columns=["date", "Mkt-RF", "SMB", "HML", "RF"]) + factors.index = pd.PeriodIndex( + pd.to_datetime(factors["date"], format="%Y%m"), freq="M") + factors = factors.drop(columns="date") + window = ((factors.index >= pd.Period(start, "M")) + & (factors.index <= pd.Period(end, "M"))) + return factors.loc[window] + + +def to_month_end(index): + return pd.PeriodIndex(pd.DatetimeIndex(index), freq="M").to_timestamp("M") + + +def build(start=START, end=END): + start_period = pd.Timestamp(start).to_period("M") + end_period = pd.Timestamp(end).to_period("M") + + # Pull one extra month to build the first in-sample growth rate. + fetch_start = (start_period - 1).to_timestamp(how="start") + fetch_end = end_period.to_timestamp("M") + sample_start = start_period.to_timestamp("M") + sample_end = end_period.to_timestamp("M") + + fred = read_fred(list(FRED_CODES.values()), fetch_start, fetch_end) + fred = fred.rename(columns={v: k for k, v in FRED_CODES.items()}) + fred.index = to_month_end(fred.index) + fred["cons_real_level"] = fred["cons_nd_real_index"] + fred["cons_price_index"] = fred["cons_nd_price_index"] + fred["consumption_per_capita"] = ( + fred["cons_real_level"] / fred["population_16plus"]) + fred["gross_cons_growth"] = ( + fred["consumption_per_capita"] + / fred["consumption_per_capita"].shift(1)) + fred["gross_inflation_cons"] = ( + fred["cons_price_index"] / fred["cons_price_index"].shift(1)) + + ff = read_famafrench_factors(fetch_start, fetch_end).copy() + ff.columns = [str(col).strip() for col in ff.columns] + if ("Mkt-RF" not in ff.columns) or ("RF" not in ff.columns): + raise KeyError( + "Fama-French data missing required columns: 'Mkt-RF' and 'RF'.") + # Mkt-RF and RF are reported in percent per month. + ff["gross_nom_return"] = 1.0 + (ff["Mkt-RF"] + ff["RF"]) / 100.0 + ff.index = ff.index.to_timestamp(how="end") + ff.index = to_month_end(ff.index) + market = ff[["gross_nom_return"]] + + out = fred.join(market, how="inner") + out["gross_real_return"] = ( + out["gross_nom_return"] / out["gross_inflation_cons"]) + out = out.loc[sample_start:sample_end].dropna() + + frame = out[["gross_real_return", "gross_cons_growth"]].copy() + frame.index.name = "date" + return frame + + +if __name__ == "__main__": + frame = build() + frame.to_csv(OUTPUT) + print(f"wrote {OUTPUT.name}: {frame.shape[0]} rows x {frame.shape[1]} cols " + f"({frame.index.min().date()} .. {frame.index.max().date()})") diff --git a/lectures/_static/lecture_specific/hansen_singleton_1983/README.md b/lectures/_static/lecture_specific/hansen_singleton_1983/README.md new file mode 100644 index 000000000..010df47f5 --- /dev/null +++ b/lectures/_static/lecture_specific/hansen_singleton_1983/README.md @@ -0,0 +1,56 @@ +# Data — `hansen_singleton_1983` + +Vendored input data for the **Hansen–Singleton (1983)** lecture +(`lectures/hansen_singleton_1983.md`). + +The lecture reads `hansen_singleton_1983_data.csv` directly from GitHub instead +of querying the data providers while the book builds. This keeps the build +reproducible and removes its dependence on live FRED / Ken French endpoints +(which can be slow, rate-limited, or temporarily unavailable). + +The companion lecture `hansen_singleton_1982` uses the same construction; this +dataset is a superset that additionally carries the T-bill return and the +intermediate consumption/inflation series the 1983 lecture reports. + +## Sources + +| Quantity | Provider | Code / dataset | +| --- | --- | --- | +| Civilian noninstitutional population, 16+ | FRED | `CNP16OV` | +| Real PCE: nondurable goods (chain-type quantity index) | FRED | `DNDGRA3M086SBEA` | +| PCE: nondurable goods (chain-type price index) | FRED | `DNDGRG3M086SBEA` | +| Monthly market excess return `Mkt-RF` and risk-free rate `RF` | Ken French data library | `F-F_Research_Data_Factors` | + +- FRED — +- Ken French data library — + +## Sample + +Monthly, **1959-02 to 1978-12** (239 observations). One extra prior month is +pulled to form the first in-sample growth rate. + +## File: `hansen_singleton_1983_data.csv` + +Month-end `date` index plus: + +| Column | Definition | +| --- | --- | +| `gross_real_return` | gross real market return, `(1 + (Mkt-RF + RF)/100) / gross_inflation_cons` | +| `gross_cons_growth` | gross growth of per-capita real nondurable consumption | +| `gross_inflation_cons` | gross consumption-deflator inflation | +| `consumption_per_capita` | per-capita real nondurable consumption level | +| `gross_real_tbill` | gross real T-bill return, `(1 + RF/100) / gross_inflation_cons` | + +## Regenerating + +```bash +python make_data.py +``` + +`make_data.py` needs only the standard library and pandas (both ship with +Anaconda). + +> **The committed CSV is a frozen snapshot.** FRED revises historical series and +> the Ken French library is updated over time, so regenerating may produce small +> differences from the committed values. Update the CSV deliberately, in its own +> commit, when you intend to refresh the data. diff --git a/lectures/_static/lecture_specific/hansen_singleton_1983/hansen_singleton_1983_data.csv b/lectures/_static/lecture_specific/hansen_singleton_1983/hansen_singleton_1983_data.csv new file mode 100644 index 000000000..491212f49 --- /dev/null +++ b/lectures/_static/lecture_specific/hansen_singleton_1983/hansen_singleton_1983_data.csv @@ -0,0 +1,240 @@ +date,gross_real_return,gross_cons_growth,gross_inflation_cons,consumption_per_capita,gross_real_tbill +1959-02-28,1.0109579062397553,1.0006229224411516,1.0004373018476003,0.0002135783527442639,1.001462058791389 +1959-03-31,1.00571436382525,1.0180902221035215,0.9992896951152881,0.0002174420325819119,1.0029123735578762 +1959-04-30,1.037975704918033,0.9817704517570749,1.0006014544261577,0.00021347816255892023,1.001397704918033 +1959-05-31,1.0206154266958425,1.0184807553981456,0.9989071038251366,0.00021742340026401722,1.003296498905908 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+1978-12-31,1.0113599312297732,1.0089199884598332,1.0050823338076846,0.0002641114214317213,1.00270392394822 diff --git a/lectures/_static/lecture_specific/hansen_singleton_1983/make_data.py b/lectures/_static/lecture_specific/hansen_singleton_1983/make_data.py new file mode 100644 index 000000000..fa4b1ad36 --- /dev/null +++ b/lectures/_static/lecture_specific/hansen_singleton_1983/make_data.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +""" +Build the vendored dataset for the ``hansen_singleton_1983`` lecture. + +Downloads the raw inputs from FRED and the Ken French data library, constructs +the monthly series used in the lecture (gross real market return, gross real +T-bill return, gross consumption growth, gross consumption inflation, and per +capita real consumption), and writes them to +``hansen_singleton_1983_data.csv`` next to this script. + +Usage +----- + python make_data.py + +Requires only the standard library plus pandas (both ship with Anaconda). + +Note +---- +The committed CSV is a *frozen snapshot*. FRED revises historical series and +the Ken French library is updated over time, so re-running this script may +produce small differences from the committed data. See README.md. +""" +import io +import urllib.request +import zipfile +from pathlib import Path + +import pandas as pd + +FRED_CODES = { + "population_16plus": "CNP16OV", + "cons_nd_real_index": "DNDGRA3M086SBEA", + "cons_nd_price_index": "DNDGRG3M086SBEA", +} +START = "1959-02-01" +END = "1978-12-01" +OUTPUT = Path(__file__).with_name("hansen_singleton_1983_data.csv") + + +def read_fred(codes, start, end): + """Download FRED series as a date-indexed DataFrame (columns = codes).""" + base = "https://fred.stlouisfed.org/graph/fredgraph.csv" + columns = [] + for code in codes: + url = f"{base}?id={code}&cosd={start:%Y-%m-%d}&coed={end:%Y-%m-%d}" + columns.append( + pd.read_csv(url, index_col=0, parse_dates=True, na_values=".")) + fred = pd.concat(columns, axis=1).astype("float64") + fred.index.name = "DATE" + return fred + + +def read_famafrench_factors(start, end): + """Download the monthly Fama-French research factors (percent).""" + url = ("https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/" + "F-F_Research_Data_Factors_CSV.zip") + with urllib.request.urlopen(url) as response: + payload = response.read() + with zipfile.ZipFile(io.BytesIO(payload)) as archive: + text = archive.read(archive.namelist()[0]).decode("utf-8") + + # Preamble, then a monthly table (rows keyed by YYYYMM), then an annual + # table (rows keyed by YYYY). Keep the contiguous monthly block. + records = [] + for line in text.splitlines(): + cells = [cell.strip() for cell in line.split(",")] + key = cells[0] + if len(key) == 6 and key.isdigit(): + records.append([key] + [float(x) for x in cells[1:5]]) + elif records: + break + factors = pd.DataFrame( + records, columns=["date", "Mkt-RF", "SMB", "HML", "RF"]) + factors.index = pd.PeriodIndex( + pd.to_datetime(factors["date"], format="%Y%m"), freq="M") + factors = factors.drop(columns="date") + window = ((factors.index >= pd.Period(start, "M")) + & (factors.index <= pd.Period(end, "M"))) + return factors.loc[window] + + +def to_month_end(index): + return pd.PeriodIndex(pd.DatetimeIndex(index), freq="M").to_timestamp("M") + + +def build(start=START, end=END): + start_period = pd.Timestamp(start).to_period("M") + end_period = pd.Timestamp(end).to_period("M") + + # Pull one extra month to build the first in-sample growth rate. + fetch_start = (start_period - 1).to_timestamp(how="start") + fetch_end = end_period.to_timestamp("M") + sample_start = start_period.to_timestamp("M") + sample_end = end_period.to_timestamp("M") + + fred = read_fred(list(FRED_CODES.values()), fetch_start, fetch_end) + fred = fred.rename(columns={v: k for k, v in FRED_CODES.items()}) + fred.index = to_month_end(fred.index) + fred["cons_real_level"] = fred["cons_nd_real_index"] + fred["cons_price_index"] = fred["cons_nd_price_index"] + fred["consumption_per_capita"] = ( + fred["cons_real_level"] / fred["population_16plus"]) + fred["gross_cons_growth"] = ( + fred["consumption_per_capita"] + / fred["consumption_per_capita"].shift(1)) + fred["gross_inflation_cons"] = ( + fred["cons_price_index"] / fred["cons_price_index"].shift(1)) + + ff = read_famafrench_factors(fetch_start, fetch_end).copy() + ff.columns = [str(col).strip() for col in ff.columns] + if ("Mkt-RF" not in ff.columns) or ("RF" not in ff.columns): + raise KeyError( + "Fama-French data missing required columns: 'Mkt-RF' and 'RF'.") + # Mkt-RF and RF are reported in percent per month. + ff["gross_nom_return"] = 1.0 + (ff["Mkt-RF"] + ff["RF"]) / 100.0 + ff["gross_nom_tbill"] = 1.0 + ff["RF"] / 100.0 + ff.index = ff.index.to_timestamp(how="end") + ff.index = to_month_end(ff.index) + market = ff[["gross_nom_return", "gross_nom_tbill"]] + + out = fred.join(market, how="inner") + out["gross_real_return"] = ( + out["gross_nom_return"] / out["gross_inflation_cons"]) + out["gross_real_tbill"] = ( + out["gross_nom_tbill"] / out["gross_inflation_cons"]) + out = out.loc[sample_start:sample_end].dropna() + + frame = out[[ + "gross_real_return", + "gross_cons_growth", + "gross_inflation_cons", + "consumption_per_capita", + "gross_real_tbill", + ]].copy() + frame.index.name = "date" + return frame + + +if __name__ == "__main__": + frame = build() + frame.to_csv(OUTPUT) + print(f"wrote {OUTPUT.name}: {frame.shape[0]} rows x {frame.shape[1]} cols " + f"({frame.index.min().date()} .. {frame.index.max().date()})")