=============================================================== REQUIREMENTS - Please ensure before running: =============================================================== PYTHON: Install Python 3.x from https://www.python.org/downloads/ - Check "Add Python to PATH" during installation - Required packages: pandas, numpy, statsmodels, linearmodels, scipy, matplotlib, rdrobust (install via pip) - pip install pandas numpy statsmodels linearmodels scipy matplotlib rdrobust =============================================================== ## OBJECTIVES Develop a publication-quality empirical finance article suitable for the Journal of Finance with: - **Topic**: Analyse the returns on equities, bonds, and housing, over the long-run - **Statistical Rigor**: Use appropriate statistical techniques to explore the data comprehensively - **Professional Formatting**: Journal of Finance standards per 02-SampleTables.tex --- ## DATA You have access to the following data based on the Rate of Return on Everything (Jorda et al., 2017) dataset: ========================= JSTdatasetR6.dta order name type isnumeric format vallab varlab Variable Label year Year country Country iso ISO 3-letter code ifs IFS 3-number country-code pop Population rgdpmad Real GDP per capita (PPP, 1990 Int$, Maddison) rgdbarro Real GDP per capita (index, 2005=100) rconsbarro Real consumption per capita (index, 2006=100) gdp GDP (nominal, local currency) iy Investment-to-GDP ratio cpi Consumer prices (index, 1990=100) ca Current account (nominal, local currency) imports Imports (nominal, local currency) exports Exports (nominal, local currency) narrowm Narrow money (nominal, local currency) money Broad money (nominal, local currency) stir Short-term interest rate (nominal, percent per year) ltrate Long-term interest rate (nominal, percent per year) hpnom House prices (nominal index, 1990=100) unemp Unemployment rate (percent) wage Wages (index, 1990= 100) debtgdp Public debt-to-GDP ratio revenue Government revenues (nominal, local currency) expenditure Government expenditure (nominal, local currency) xrusd USD exchange rate (local currency/USD) peg Peg dummy peg_strict Strict peg dummy crisisJST Systemic financial crises (0-1 dummy); included since R5 crisisJST_old Systemic financial crises (0-1 dummy); as coded in all prior releases (R1 – R4) JSTtrilemmaIV JST trilemma instrument (raw base rate changes) tloans Total loans to non-financial private sector (nominal, local currency) tmort Mortgage loans to non-financial private sector (nominal, local currency) thh Total loans to households (nominal, local currency) tbus Total loans to business (nominal, local currency) bdebt Corporate debt (nominal, local currency) peg_type Peg type (BASE, PEG, FLOAT) peg_base Peg base (GBR, USA, DEU, HYBRID, NA) eq_tr Equity total return, nominal. r[t] = [[p[t] + d[t]] / p[t-1] ] - 1 housing_tr Housing total return, nominal. r[t] = [[p[t] + d[t]] / p[t-1] ] - 1 bond_tr Government bond total return, nominal. r[t] = [[p[t] + coupon[t]] / p[t-1] ] - 1 bill_rate Bill rate, nominal. r[t] = coupon[t] / p[t-1] rent_ipolated 1 if housing rental yields interpolated e.g. wartime housing_capgain_ipolated 1 if housing capital gains and total returns interpolated e.g. wartime housing_capgain Housing capital gain, nominal. cg[t] = [ p[t] / p[t-1] ] - 1 housing_rent_rtn Housing rental return. dp_rtn[t] = rent[t]/p[t-1] housing_rent_yd Housing rental yield. dp[t] = rent[t]/p[t] eq_capgain Equity capital gain, nominal. cg[t] = [ p[t] / p[t-1] ] - 1 eq_dp Equity dividend yield. dp[t] = dividend[t]/p[t] eq_capgain_interp 1 if equity cap. gain interpolated to cover exchange closure eq_tr_interp 1 if equity total return interpolated to cover exchange closure eq_dp_interp 1 if equity dividend interpolated or assumed zero to cover exchange closure bond_rate Gov. bond rate, rate[t] = coupon[t] / p[t-1], or yield to maturity at t eq_div_rtn Equity dividend return. dp_rtn[t] = dividend[t]/p[t-1] capital_tr Tot. rtn. on wealth, nominal. Wtd. avg. of housing, equity, bonds and bills risky_tr Tot. rtn. on risky assets, nominal. Wtd. avg. of housing and equity safe_tr Tot. rtn. on safe assets, nominal. Equally wtd. avg. of bonds and bills Sample Data: year country iso ifs pop rgdpmad rgdpbarro rconsbarro gdp iy cpi ca imports exports narrowm money stir ltrate hpnom unemp wage debtgdp revenue expenditure xrusd tloans tmort thh tbus bdebt lev ltd noncore crisisJST crisisJST_old peg peg_strict peg_type peg_base JSTtrilemmaIV eq_tr housing_tr bond_tr bill_rate rent_ipolated housing_capgain_ipolated housing_capgain housing_rent_rtn housing_rent_yd eq_capgain eq_dp eq_capgain_interp eq_tr_interp eq_dp_interp bond_rate eq_div_rtn capital_tr risky_tr safe_tr 1870 Australia AUS 193 1775 3273.2394 13.836157 21.449734 208.78 .1092656 2.708333 -6.147594 36 37 23.3 54.3 4.88 4.911817 .49225261 .44811834 .172568 .36694615 54.792 1.68 25.016659 119.83204 32.928768 0 0 1 1 PEG GBR -.0049039 .0488 -.07004543 .07141703 .04911817 .06641459 1871 Australia AUS 193 1675 3298.5075 13.936864 19.930801 211.56 .1045791 2.666667 5.260774 34 46 27.2 59.5 4.6 4.844633 .46987749 .43941701 .191799 .36914645 53.748 1.766 23.983004 107.03788 30.213655 0 0 1 1 PEG GBR -.41999999 .11019297 .046 -.0454563 .04165363 .06546638 .04844633 .06819329 1872 Australia AUS 193 1722 3553.4262 15.044247 21.085006 227.4 .130438 2.541667 7.867636 38 53 36.2 68.5 4.6 4.73735 .48479424 .452469 .15492 .36923881 55.822 1.47 22.445646 94.909546 26.860411 0 0 1 1 PEG GBR 1.27 .1767493 .046 .03174661 .10894547 .06299735 .0473735 .06986062 1873 Australia AUS 193 1769 3823.6292 16.219443 23.25491 266.54 .1249862 2.541667 -11.047833 49 50 38.6 73.7 4.4 4.671958 .46987749 .49162497 .142692 .36240478 65.38 1.364 21.004997 101.7081 28.645016 0 0 1 1 PEG GBR .58999997 .15168582 .044 -.03076977 .08308647 .06448419 .04671958 .06984195 1874 Australia AUS 193 1822 3834.7969 16.268228 23.45805 287.58 .1419599 2.666667 -5.5639588 49 54 37.9 79.3 4.5 4.653317 .56683634 .50902763 .194322 .37222346 71.478 1.434 20.974375 102.07935 28.298048 0 0 1 1 PEG GBR -1.08 .1914814 .045 .20634975 .11938886 .06350296 .04653317 .07108451 1875 Australia AUS 193 1874 4138.207 17.592107 25.669505 300.74 .160564 2.75 -7.9087368 50 52 37.5 88.5 4.6 4.507325 .56683634 .52207962 .233687 .36092562 79.954 1.564 19.60018 101.90935 28.049749 0 0 1 1 PEG GBR -.50999999 .12440787 .046 0 .05705873 .06364298 .04507325 .06727437 1876 Australia AUS 193 1929 4007.2576 17.019033 24.806161 311.26 .1678778 2.791667 -6.9521128 48 51 38.2 95.8 4.6 4.565908 .52208611 .53513161 .158506 .37202171 86.206 1.86 18.56885 100.68678 28.126993 0 0 1 1 PEG GBR -1.01 .11881457 .046 -.078947 .05410355 .06022258 .04565908 .06348084 1877 Australia AUS 193 1995 4036.0902 17.145652 25.217057 313.66 .2099336 2.875 -14.624799 52 51 40.2 105.7 4.5 4.38885 .52208611 .53948227 .176092 .39991068 101.036 2.42 18.7411 106.30669 24.98303 0 0 1 1 PEG GBR .44999999 .13659137 .045 0 .07444197 .05730547 .0438885 .0615714 1878 Australia AUS 193 2062 4277.4006 18.205261 26.560551 324.3 .181141 2.833333 -16.335923 52 50 38.7 106.2 4.8 4.4428 .58921146 .53078094 .172852 .41257288 106.648 2.754 19.204308 112.32253 27.424212 0 0 1 1 PEG GBR .88999999 -.07893736 .048 .12857077 -.13273652 .06448047 .044428 .05592155 1879 Australia AUS 193 2127 4204.9835 17.887247 26.519 334.44 .1615086 2.75 -12.332673 48 44 37.2 108.3 4.9 4.60335 .60412821 .53513161 .208934 .41508826 102.726 2.9 19.33445 105.52451 26.231398 0 0 1 1 PEG GBR -1.47 .12413586 .049 .02531691 .0600705 .06041679 .0460335 .06404606 1880 Australia AUS 193 2197 4285.3892 18.240585 25.669505 344.32 .1764724 2.666667 .7857892 46 59 44.6 114.8 4.7 4.409408 .57429472 .55688492 .222212 .41252254 94.008 2.996 18.614983 90.800911 28.28406 0 0 1 1 PEG GBR .56 .11423738 .047 -.04938192 .0541498 .05684576 .04409408 .05992395 1881 Australia AUS 193 2269 4454.8259 18.985419 27.788625 362.82 .2123455 2.625 -20.848847 58 54 57.3 134 4.7 3.7815 .57429472 .56123559 .232202 .41587488 115.8 3.834 16.564224 93.845734 27.510811 0 0 1 1 PEG GBR .54000002 .07269453 .047 0 .02000195 .05190045 .037815 .05293856 1882 Australia AUS 193 2348 4062.6065 17.255672 26.851411 382.32 .1805267 2.666667 -30.311825 72 57 55.6 149.3 4.5 3.8220916 .59890735 .57863824 .23255 .41243653 144.204 4.328 15.996553 103.79765 25.786325 0 0 1 1 PEG GBR .51999998 .09030421 .045 .04285602 .03700671 .05078601 .03822092 .05266543 1967 Belgium BEL 124 9606 9071.8359 40.788116 41.2361 969700 .22512117 28.440496 10100 364300 354100 332600 5.22 6.7 24.414519 2.2122932 15.471753 .54081391 222951 254513 49.6275 537100 159700 219766.45 317333.53 400032.34 6.3348532 96.334114 34.914825 0 0 1 1 PEG USA -.55624998 .24981649 .08204313 .0522 1 .15043893 .19813448 .0431354 .067 .05168201 .06712157 1968 Belgium BEL 124 9632 9415.5248 42.358033 43.2863 1037500 .21262651 29.223068 1700 419800 408600 355300 4.01 6.54 22.724283 2.5970398 16.286314 .54523614 239524 285100 50.14 604300 176900 243435.73 360864.25 454907.41 6.2740617 97.671921 36.855572 0 0 1 1 PEG USA 1.08 .16184509 .06199441 .0401 1 -.06922935 .11660447 .04051625 .0654 .04524062 .05104721 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667519.56 841478.75 3.5900712 98.788536 50.653625 0 0 1 1 PEG DEU -.98775005 -.24737221 -.02541651 .1021 1 .15020175 -.28481653 .05235625 .0868 .03744432 .03834175 1975 Belgium BEL 124 9813 12440.785 56.08535 58.107 2313100 .22139121 47.109419 25000 1130900 1056900 657400 6.99 8.54 39.720544 4.0398397 42.41115 .3983973 581265 729000 39.5275 1331700 409700 563796.63 767903.38 968023.06 3.3098736 102.60217 49.191998 0 0 1 1 PEG DEU -3.3464999 .23179574 .12322815 .0699 1 .15446098 .16684663 .05566208 .0854 .06494911 .09656407 1976 Belgium BEL 124 9823 13122.312 59.164105 60.9664 2632800 .21448648 51.43119 -12000 1369000 1266500 713900 9.77 9.05 49.249486 5.386453 47.126772 .4006854 633000 836000 35.9825 1519100 474300 652694 866406 1092196 3.2087691 100.65649 47.507 0 0 1 1 PEG DEU -.38924998 -.03501074 .31864876 .02161541 .0977 .2399013 .07874747 .06351107 -.08252499 .05178805 .0905 .04751424 .26247537 .30024433 .0596577 1977 Belgium BEL 124 9837 13189.945 59.46827 62.5022 2846800 .21276521 55.083192 -49000 1448000 1344700 774900 7.08 8.8 58.577777 6.0597596 51.425299 .4293782 748000 951000 32.94 1732800 552500 760306.63 972493.38 1225930.4 3.2390079 105.78738 50.405674 0 0 1 1 PEG DEU .19050001 .02051396 .2527895 .16404474 .0708 .18940903 .06338045 .05328735 -.03859418 .06148094 .088 .05910814 .22290519 .242926 .11742237 1978 Belgium BEL 124 9842 13553.923 61.109822 64.0115 3057600 .211146 57.543711 -31000 1526000 1410300 787258.75 7.14 8.93 66.279092 6.5406929 55.027032 .4596645 877000 1126000 28.8 1957600 646600 889799.56 1067800.4 1346074.9 3.0244026 107.96291 53.070774 0 0 1 1 PEG DEU -.58424997 .13515538 .18861225 .0638748 .0714 .13147131 .05714095 .05050146 .06903896 .06184658 .0893 .06611641 .1669306 .18633254 .0676374 1979 Belgium BEL 124 9855 13860.651 62.497014 67.2507 3264700 .20461298 60.117033 -100000 1784400 1661200 799617.5 2376020 10.76 9.69 72.584512 6.8292529 59.29121 .4943217 941000 1212000 28.048 2247000 743100 1022595.3 1224404.8 1543491.1 2.7628992 117.96228 56.075142 0 0 1 1 PEG DEU 1.8840001 .10138564 .14865325 -.08388116 .1076 .09513406 .05351919 .04886999 .04104425 .05796237 .0969 .06034139 .12325969 .14662158 .01185942 1980 Belgium BEL 124 9863 14467.441 65.232967 66.5851 3605378.6 .23244645 64.114495 -141889.71 2100800 1890400 800706.69 2437619.3 14.08 11.9 73.179649 7.4122379 64.783493 1004000 1332000 31.523 2651662.8 822000 1131171.1 1520491.5 1916739.8 2.4224041 128.64719 61.253139 0 0 1 1 PEG DEU 2.3917499 -.11453817 .06076451 -.01581264 .1408 .00819872 .05256579 .05213833 -.17446366 .07258976 .119 .05992548 .0561661 .0541357 .06249368 1999 Canada CAN 156 30821 21563.041 88.880229 84.1194 1007.927 .20714119 118.49688 1.2200953 319.00834 354.10756 199.95 477.142 4.71975 5.6908333 112.29013 7.5831 125.68251 .91369151 184.815 175.875 1.4433 683.58725 341.492 464.042 246.367 1199.454 4.7397528 105.15114 60.845898 0 0 1 0 PEG USA -.38 2000 Canada CAN 156 31100 22487.708 92.587548 86.6304 1106.071 .2015497 121.74003 27.745336 354.72789 410.99351 225.19 510.32 5.4898333 5.8858333 113.74285 6.8287 132.39358 .82126871 203.952 183.625 1.5002 708.474 350.241 476.45 263.13 1298.026 4.6911159 101.81964 59.808964 0 0 1 0 PEG USA 1.27 2001 Canada CAN 156 31377 22686.646 93.284019 87.7231 1144.543 .20825963 124.79301 25.095911 343.31066 402.17161 253.75 540.224 3.7685833 5.7825 115.92731 7.2186 134.59881 .82659145 201.102 187.964 1.5926 743.24425 372.854 507.041 243.946 1392.3051 4.6309743 103.11837 61.638817 0 0 1 0 PEG USA -2.3599999 2002 Canada CAN 156 31641 23155.137 95.214092 90.0439 1193.694 .20644461 127.63284 19.765396 348.19805 396.01967 268.5 570.306 2.58575 5.6616667 124.95347 7.6648 136.08641 .80553992 197.095 187.879 1.5796 775.79275 405.953 554.109 232.105 1469.874 4.5186553 103.99126 60.593334 0 0 0 0 FLOAT USA 0 2003 Canada CAN 156 31889 23406.98 96.188134 91.8679 1254.747 .20673456 131.13253 13.483535 334.33069 381.6545 283.95 605.479 2.8700833 5.2783333 137.60116 7.5738 138.45102 .76561832 203.136 198.741 1.2924 808.0475 431.247 599.057 222.502 1499.51 4.6886978 100.32903 57.553204 0 0 0 0 FLOAT USA 0 2004 Canada CAN 156 32135 23952.909 97.894258 94.0405 1335.731 .21610854 133.54607 27.966306 354.85914 395.8971 311.74 644.388 2.2199167 5.0808333 152.71455 7.1853 143.64055 .72600716 212.09 201.667 1.2036 875.8405 462.098 653.533 239.215 1601.936 4.4298105 101.31181 56.321529 0 0 0 0 FLOAT USA 0 2005 Canada CAN 156 32386 24484.345 100 96.8255 1421.59 .22692569 136.52502 25.541092 380.8583 436.3507 340.19 683.068 2.7260833 4.3858333 167.37794 6.7581 150.20276 .71608151 224.217 223.834 1.1645 949.87725 498.658 714.13 262.554 1590.238 4.4347992 101.01932 56.533783 0 0 0 0 FLOAT USA 0 2006 Canada CAN 156 32657 24967.167 103.11869 100 1496.604 .23811046 139.27933 20.964023 397.0439 440.3651 376.251 746.542 4.0341667 4.2991667 186.50049 6.3203 156.72838 .70255274 235.315 223.505 1.1653 1029.3663 536.492 771.67 299.357 1727.1639 4.5004411 102.40002 59.102619 0 0 0 0 FLOAT USA 0 2007 Canada CAN 156 32386 25300.084 109.13649 102.452 1577.661 .23906414 142.2468 10.919305 407.3009 450.3208 402.643 797.896 4.1516667 4.335 207.06475 6.0361 163.7833 .66518261 249.091 234.368 .9881 1141.7925 586.254 844.511 350.722 2005.632 4.2496085 102.66705 58.142483 0 0 0 0 FLOAT USA 0 2008 Canada CAN 156 33213 25262.071 109.04886 104.445 1657.041 .24120888 145.63928 3.8917114 443.592 483.4881 451.954 908.547 2.39 4.04 223.04684 6.1371 169.07086 .71114083 244.023 244.447 1.2246 1242.4158 620.283 917.525 386.518 2255.3269 4.4556847 98.585358 63.104958 0 0 0 0 FLOAT USA 0 ## SUBSET FOR DEVELOPMENT AND DEBUGGING When generating the code restrict the datasets to a smaller subset of just the first 100 observations for each .dta file so that the code runs quickly for testing. However, when designing the code include consideration that the full sample will be used. Use a single constant (e.g., `MAX_OBS_PER_FILE = 100`) at the top of `01_setup.py` that controls the subset size. Setting it to `None` must load the full dataset. **IMPORTANT — Two-pass workflow:** 1. **First pass (development):** Run all stages (4–10) with the subset (`MAX_OBS_PER_FILE = 100`) to debug code, verify table formatting, and confirm PDF compilation. 2. **Second pass (production):** Once the first pass completes successfully with no errors, you MUST automatically change `MAX_OBS_PER_FILE` to `None` in `01_setup.py`, then re-run ALL analysis scripts (`01_setup.py`, `02_descriptive.py`, `03_crosstabs.py`, `04_regressions.py`) and recompile the PDF. The final delivered PDF must be based on the full dataset, not the development subset. See Stage 10.5 below. ## WORKFLOW STAGES ### **STAGE 1: Design Exploratory Analysis ** - Develop ideas on what trends and cross-sectional patterns to look for - Create 10 professional table outlines (minimum 5 columns each) - Use 02-SampleTables.tex for inspiration on what types of tables to include and formatting standards - Include subgroup analyses and variable subsets - Ensure tables show comprehensive empirical coverage ### **STAGE 2: Design Cross-Tabulations ** - Develop ideas on potentially interesting interactions between variables - Create 10 professional table outlines (minimum 5 columns each) - Use 02-SampleTables.tex for inspiration on what types of tables to include and formatting standards - Include subgroup analyses and variable subsets - Ensure tables show comprehensive empirical coverage ### **STAGE 3: Design Regression Analysis ** **Theoretical contribution**: Choose interesting variables as the main focus, and control for standard variables. **Empirical strategy**: Begin with standard and appropriate regression analysis, but where possible try to use more sophisticated research designs such as: - Regression Discontinuity Design (RDD) - Difference-in-Differences (DiD) - Natural experiment exploitation - IV/2SLS with credible instruments - ML/LASSO for variable selection - Event study methodology - Fama-Macbeth regressions - Fama-French regressions Specify for each method: - The precise identification assumption - Why it's credible with THIS dataset - What the treatment/instrument/discontinuity is - What the falsification test would be - Create 10 table professional table outlines - Minimum 5 columns which may include: - Baseline specification - Additional controls - Subsample splits - Robustness checks - Placebo/falsification tests - Avoid creating tables which have just one variable in the entire table. Ensure that you include and show results for different sets of control variables in different columns. - Ensure that the standard errors for coefficients are reported in parentheses - Ensure that the constant is reported in the table - Titles of each table should include details on what empirical technique is used e.g. Difference-in-Differences - Apply Journal of Finance formatting --- ### **STAGE 4: Setup Code Development** **Task 4A - Write `01_setup.py`** - Import all .dta files using `pd.read_stata()` - Create derived DataFrames for all 30 tables - Use `pd.qcut()` or percentile approach for quantile binning - Number all sections for debugging - Check column existence before creation - Apply consistent naming conventions - Winsorize variables at 0.5% and 99.5% percentiles using `scipy.stats.mstats.winsorize` or manual clip **Task 4B - Validate `01_setup.py`** Check for: - ✓ Column duplication (skip if identical definition, rename if different) - ✓ Reference to non-existent columns - ✓ Proper sort/groupby before lag operations - ✓ String vs numeric dtype mismatches **Task 4C - Execute `01_setup.py`** - Run via `run_python.py` - Document all errors - Iteratively fix until clean execution - Verify output DataFrames --- ### **STAGE 5: Descriptive Analysis** **Task 5A - Write `02_descriptive.py`** - Generate all 10 descriptive tables sequentially - Minimum 5 columns per table with subgroup analysis - Export to: (1) screen, (2) log file, (3) results.tex (append mode) - Include complete LaTeX preamble in results.tex **Common Errors to Prevent:** ```python # Check before creating if 'varname' in df.columns: # Column exists - handle appropriately pass # Ensure proper sort before lags df = df.sort_values(['panelvar', 'timevar']) df['lag_var'] = df.groupby('panelvar')['var'].shift(1) ``` **Task 5B - Validate `02_descriptive.py`** - Same checks as 4B - Verify LaTeX syntax (braces, column specs) - Confirm decimal places ≤ 3 **Task 5C - Execute `02_descriptive.py`** - Run via `run_python.py` - Check LaTeX compilation compatibility - Iteratively debug --- ### **STAGE 6: Cross-Tabulations ** **Task 6A - Write `03_crosstabs.py`** - Generate all 10 cross-tabulation tables sequentially - Minimum 5 columns per table with subgroup analysis - Export to: (1) screen, (2) log file, (3) results.tex (append mode) - Include complete LaTeX preamble in results.tex **Common Errors to Prevent:** ```python # Check before creating if 'varname' in df.columns: # Column exists - handle appropriately pass # Ensure proper sort before lags df = df.sort_values(['panelvar', 'timevar']) df['lag_var'] = df.groupby('panelvar')['var'].shift(1) ``` **Task 6B - Validate `03_crosstabs.py`** - Same checks as 4B - Verify LaTeX syntax (braces, column specs) - Confirm decimal places ≤ 3 **Task 6C - Execute `03_crosstabs.py`** - Run via `run_python.py` - Check LaTeX compilation compatibility - Iteratively debug --- ### **STAGE 7: Regression Analysis ** **Task 7A - Write `04_regressions.py`** - Implement all 10 regression tables - Use advanced methods (DiD, RDD, etc.) where appropriate - Include fixed effects, clustered SEs - Report standard errors for ALL coefficients in ALL columns (not just selective columns) - Export to screen + log + results.tex (append) - Add LaTeX document closing commands at end **CRITICAL: Standard Error Validation Requirements:** 1. **Every coefficient cell MUST have a corresponding SE cell in parentheses directly below it.** Never write a coefficient without its standard error. If a coefficient is `{---}` (variable omitted from that specification), the SE row may be blank, but if a numeric coefficient is shown, the SE MUST appear. 2. **Silent failure detection**: After fitting any regression model, immediately check that the results object contains valid coefficients and standard errors before writing to LaTeX. Example: ```python # After fitting model: if results.params is None or len(results.params) == 0: logging.warning(f"Table {table_num}, Col {col}: Model returned no coefficients") # Write fallback: populate cells with {---} or re-estimate with simpler method for var in variables: coef = results.params.get(var, None) se = results.std_errors.get(var, None) if coef is not None and (se is None or np.isnan(se)): logging.error(f"Coefficient for {var} has no valid SE") ``` 3. **Fama-MacBeth regressions**: The custom implementation (year-by-year cross-sectional OLS) frequently fails when there are too few cross-sectional units per period or when variables have insufficient within-period variation. Implement these safeguards: - Skip years with fewer than 3 valid observations in the cross-section - After computing time-series means of coefficients, verify each coefficient has a valid Fama-MacBeth SE (time-series std error of coefficient estimates / sqrt(T)) - If the method produces NaN/empty results, fall back to pooled OLS with year fixed effects and clustered SEs, and note this in the table footnote - Log how many periods were used in the time-series aggregation 4. **Subsample regressions**: Before running a regression on a subsample (e.g., by era), verify the subsample has enough observations. If n < 2*k (where k = number of regressors including constant), skip that column and write `{---}` for all cells with a footnote explaining insufficient observations, rather than running a regression that produces unreliable SEs. 5. **Post-write LaTeX verification**: After writing each table to results.tex, read back the table and verify: - No coefficient row is followed by a blank SE row (unless the coefficient is `{---}`) - The number of `&` separators is consistent across all rows - No row consists entirely of blank cells (indicating a failed regression) **Critical Code Patterns:** ```python import statsmodels.api as sm from linearmodels.panel import PanelOLS from rdrobust import rdrobust # DiD with clustered SEs and fixed effects mod = PanelOLS.from_formula( 'outcome ~ treat * post + controls + EntityEffects + TimeEffects', data=df.set_index(['firm', 'time']) ) res = mod.fit(cov_type='clustered', cluster_entity=True) # RDD with optimal bandwidth rd = rdrobust(Y=df['outcome'], X=df['running_var'], c=cutoff) # Fama-MacBeth from linearmodels.asset_pricing import FamaMacBeth fmb = FamaMacBeth.from_formula('outcome ~ predictors', data=df) res = fmb.fit(cov_type='kernel', bandwidth=4) ``` **Task 7B - Validate `04_regressions.py`** - Same checks as 4B - Verify advanced method syntax - Check clustering dimensions exist - Confirm FE variables populated - Validate `table-format` specs for S columns - **Verify every regression helper/writer function pairs each coefficient with its SE in parentheses** - **Check that no table can produce entirely blank coefficient+SE rows (indicates silent model failure)** - **Confirm column sub-headers use descriptive labels (e.g., "{Baseline}", "{+Controls}"), not duplicates of column numbers like "{(1)}"** **Task 7C - Execute `04_regressions.py`** - Run via `run_python.py` - Verify statistical significance reporting - Check convergence of iterative estimators - Iteratively debug **Task 7D - Post-execution SE Audit** - After 04_regressions.py completes, scan the generated LaTeX in results.tex for Tables 21-30 - For each table, count the number of numeric coefficient cells and the number of parenthesized SE cells - If any table has fewer SE cells than coefficient cells, flag it and fix the generating code - Specifically check for: - Tables where ALL cells in a column are blank (model failed silently) - Tables where coefficients appear but SE rows contain only whitespace or empty cells - Tables where column sub-headers duplicate the column numbers instead of having descriptive labels - If any issues are found, fix the code and re-run before proceeding to Stage 8 --- ### **STAGE 8: Evaluate Methodology ** - Rigorously analyse the Python scripts for methodological errors, and provide a report - Adjust the Python scripts to correct any methodological errors - Re-run the Python scripts and iteratively debug - Check the output of the results.tex file to ensure the methodological errors have been corrected --- ### **STAGE 9: Evaluate Formatting ** - Rigorously analyse the results.tex file for formatting errors, and ensure consistency with the formatting standards outlined in 02-SampleTables.tex, and 03-CompilePDF.py and provide a report - Adjust the Python scripts to correct any formatting errors - Re-run the Python scripts and iteratively debug - Check the output of the results.tex file to ensure the formatting errors have been corrected --- ### **STAGE 10: Create PDF ** - use 03-CompilePDF.py to compile results.tex and create a PDF - correct any errors to ensure it compiles --- ### **STAGE 11: Full-Dataset Production Run** This stage is MANDATORY. Do not skip it. 1. Open `01_setup.py` and change `MAX_OBS_PER_FILE = 100` to `MAX_OBS_PER_FILE = None` 2. Re-run all four analysis scripts in order: - `python 01_setup.py` - `python 02_descriptive.py` - `python 03_crosstabs.py` - `python 04_regressions.py` 3. If any script fails on the full dataset (e.g., due to missing data, insufficient variation, or memory issues), debug and fix the script, then re-run from that script onward 4. Verify results.tex contains 30 tables with no blank coefficient columns 5. Recompile the PDF using `03-CompilePDF.py` 6. Confirm the PDF compiles successfully — this is the final deliverable The development-subset PDF from Stage 10 is for debugging only. The production PDF from this stage is what gets delivered. --- ### **STAGE 12: Deployment Instructions** - Explain how to open PDF - Ask for any changes that the user requires - Explain how to edit and run the code manually in Python if required ``` **Common LaTeX Fixes:** ```latex % Ensure preamble includes: \usepackage{booktabs, threeparttable, siunitx} % Fix S column text: {---} % Not --- % Fix table-format mismatches: S[table-format=-2.3] % For -12.345 ``` --- ## FORMATTING CHECKLIST - **Decimal Precision**: Maximum 3 decimal places for all results - **Standard Error Reporting**: For all tables involving significance tests, all standard errors must be reported using parentheses. EVERY numeric coefficient must have a standard error in parentheses on the line directly below it. A table must NEVER contain a coefficient without its corresponding SE. If a regression method fails to produce standard errors, fall back to a simpler method (e.g., pooled OLS with robust SEs) rather than leaving SE cells blank. - **Empty Cell Detection**: After generating each regression table, programmatically verify that no column consists entirely of blank/empty cells. If a method (e.g., Fama-MacBeth, subsample regression) produces no results due to insufficient data, either (a) drop that column and note why, or (b) use an alternative estimation method and disclose it in the table notes. - **Significance Reporting**: For all tables involving significance tests, the significance must be reported using stars - **Constants Reporting**: For all tables involving regressions, the constant in each regression (with its standard error and significance) must also be reported, at the bottom of the table **Every Table Must Have:** - [ ] `\toprule`, `\midrule`, `\bottomrule` (no `\hline`) - [ ] Decimal-aligned numbers via `S` columns - [ ] `{braces}` around text in S columns - [ ] `\caption{}` and `\label{}` - [ ] `\begin{tablenotes}...\end{tablenotes}` - [ ] Significance stars via `\sym{*}` macro - [ ] Clustered SE note specifying cluster dimension - [ ] ≤3 decimal places - [ ] Column headers in `\textbf{}` - [ ] Panel headers in `\textit{}` - [ ] Proper `\addlinespace` (not blank lines) **Regression Tables Must Include:** - [ ] Model numbers (1), (2), (3)... - [ ] Variable labels (not raw column names) - [ ] Standard errors in parentheses below coefficients - [ ] Fixed effects indicator rows - [ ] R² and N observations - [ ] Note explaining specification and clustering --- ## VARIABLE MANAGEMENT PROTOCOL **Before Creating Any Column:** ```python # 1. Check existence if 'newvar' not in df.columns: # 2. Check if similar column exists similar = [c for c in df.columns if 'newvar' in c] # 3. Create with unique name if needed df['newvar_v2'] = ... ``` **Panel Data Operations:** ```python # ALWAYS sort and groupby before lags/leads/differences df = df.sort_values(['firm_id', 'date_var']) df['lag_roa'] = df.groupby('firm_id')['roa'].shift(1) df['lead_ret'] = df.groupby('firm_id')['ret'].shift(-1) df['diff_size'] = df.groupby('firm_id')['log_assets'].diff() ``` --- ## OUTPUT REQUIREMENTS **Each `.py` file must produce:** 1. **Screen output**: Summary stats, regression tables 2. **Log file**: Use Python `logging` module to write to `filename.log` 3. **LaTeX file**: - `02_descriptive.py` → Preamble + Tables 1-10 - `03_crosstabs.py` → Tables 11-20 + `\end{document}` **LaTeX Export Template:** ```python with open('results.tex', 'a', encoding='utf-8') as f: f.write('\\begin{table}[H]\n') f.write('\\centering\n') # ... table content ... f.write('\\end{table}\n') ``` --- ## SUCCESS CRITERIA - ✅ All 30 tables compile without errors - ✅ Statistical methods appropriate for JoF - ✅ Formatting indistinguishable from published JoF articles - ✅ Code runs without manual intervention - ✅ Results support clear narrative - ✅ Robustness checks address endogeneity concerns