Index Symbols | A | B | C | D | E | F | G | H | I | J | K | L | M | N | P | R | S | T | U | V | W Symbols # operator Instructs the search algorthim to restrict itself to groups .current_per, Master time index model instance .set_smpl limiting the display time frame - example .upd() Setting the time period to the entire sample A Accessing WB models Add a group Add-factors Use in simulations Anaconda Installation start session Anaconda/MiniConda Updating model flow B Balance of Payments basedf/lastdf Storage system, [1] Behavioral Equations, [1] Boxes Box 1. Opening the Anaconda/MiniConda prompt under windows Box 2. Time scope of .upd() commands Box 3. World Bank Mnemonics Box 4. The steps performed by the `.fix()` method Box 5. Endogenous (Add-factor) shocks versus temporarily exogenized shocks Box 6. Compilation of a model Box 7. Targeting background Box 8. `ModelFlow` report writing routines Box 9. Where ModelFlow Stores Results Box 10. PDFs and latex outputs C Carbon tax example(s) Complex simulations simple simulation Colors Customized Table visualizations, [1] D Data display - format output Data formatting DataFrame .columns.size - # of cols) .eval() method .loc[] method .loc[] method specific dates, specific series .loc[] set specific cells .loc[] specific range Add a column Color code table Customized Table visualizations, [1] Dated Indexes Format output leads and lags in ModelFlow Limit decimal places List columns mfcalc() ModelFlow extension for variable transformations ModelFlow extensions ModelFlow naming conventions ModelFlow specific features ModelFlow time index ModelFlow use Revise existing series or column Set one or more cells to a specific value Slicing Style property to generate fancy tables upd() ModelFlow extension for easy updating of variables Variable descriptions - set or change Dated Indexes Decimal places Limit using DataFrame styler decimal points Dependencies .show method, display RHS variables and values from .based and .lastdf Downloading WB models from github E ECMs lambda - the speed of adjustment parameter in ECMs The Error Correction specification Equation dependencies Equations .eqdelete() Deleting model equations .equpdate() Revising model equations, [1] .show method The Eviews representation of an equation(prior to normalization) The normalized representation of an equation Error Correction models - ECMs Exogenous variables F Fiscal Accounts Flow of Funds Formatting data display from package import function G gdppct in reports Only World Bank conventions Goal Seeking Defining Instruments Defining Targets solve for instruments Government Accounts Groups Add a group of variables List variables in group The # operator instructs search to restrict itself to groups H Help Initialize a ModelFlow session Modeflow I Impact Decomposition examples Find all exogenous shocks .exodif() method Single equation Single equation - impact over time Single equation, .get_attr() more output transformations Single equation, charts Single equation, charts , .dekomp_plot() Single equation, cutout threshold Single equation, impact accumulated across lags Single equation, output as "growth/pct/Level" Single equation, time dimension Single equation, Trace preceding variables Single equation, Waterfall graphs Whole model - accumulated effects Whole model - grouping variables Whole model - interactive widget Whole model - last year Whole model - waterfall Whole model .totdif() method Whole model as charts importing packages, libraries and modules Index indices for quarterly models indices for quarterly models Index Information about model variables model instance Information on equations Install ModelFlow package Installation Anaconda MiniConda Python J Jupyter Notebook Cell Execution cell modes Change cell type code cells common markdown commands Create a New Notebook Delete, Add, Move cells Executing python code Introduction JN cells markdown - Display code markdown cells Markdown cells Markdown rendering mathematics multi-line equations Startup Supress output with semicolon ';' Tables in markdown K Keep The Keep option to retain scenario results keep_solutions keep_variables, [1] Storage system, [1] keep_variables keep_solutions, [1] Kinds of simulation L Lags in ModelFlow Lambda the speed of adjustment in ECMs Leads in ModelFlow limiting the display time frame - example .set_smpl list List variables in group ljit option M Markdown Display code multi-line equations rendering mathematics Markdown commands Markdown tables Mathplotlib mfcalc() Create series Give variable a specific growth rate Multiple equations showeq option Specify timeframe for transformations The diff operator to initialize shocked dataframe MFMod Model coverage MiniConda Choosing betwen Anaconda and MiniConda Installation Model Adjacency matrix Model equations Revise equations with .equpdate() Model Information Model Name Model Structure Number of endogenous variables Number of equations Number of exogenous variables Number of variables model instance .current_per, Master time index .endogene property .substitution, Defining deferred substitution .var_descriptions, a dictionary of variable descriptions .var_groups, a dictionary of variable groups [] To select and visualize variables Add a group compilation Groups Groups - list variables in group Index operator [] information about equations Information about model variables test if mnemonic is endogenous Wildcard search on variable descriptions the ! operator wildcard selection of data - return plot model instance() Model simulation model instance.<variable> .frml - the normalized representation of an equation .tracedep() Causal tree at the variable level .tracedep() down option displays the causal tree of variables that are impacted by the referenced variable .tracepre() fokus2 option adds a table of impacts to the causal flow graph .tracepre() method - trace influence of causal variables .tracepre(); filter option restricts output to variables with a large impact tracepre() up option extends the causal tree beyond the initial set of RHS variables model instance[] .base Access basedf .df Return a dataframe .dif - difference in level .dif operator .difgrowth/.difpct - difference in growth rate .difpctlevel - difference in level as a pct of baseline .difpctlevel operator .eviews - The Eviews representation of an equation(prior to normalization) .frml .growth/.dif Growth rates .names Variable names .qoq_ar - Annualized quarterly growth rate .rename() Rename variables to description .yoy_ar - Growth over 4 periods rplot rtable Transformations Model Name Model Structure, [1], [2] .tracedep() Causal tree at the variable level .tracedep() method .tracepre() method Model Adjacency matrix Modeldash method - an interactive display of linkages within a model. Recursive Block Recursive equation block Simultaneous block Simultaneous equation block Modeldash - an interactive display of linkages within a model. ModelFlow .equpdate() - revising model equations .set_smpl(begin,end) method .upd() used to initialize shock dataframe Allowed column names Dataframe - variable descriptions DataFrame naming conventions Load model from file ModelFlow environment time index types of simulation ModelFlow - wildcard selection of data - return dataframe ModelFlow extensions DataFrame ModelFlow Help Retrieve descriptions of function options The ? operator ModelFlow Prepare your workspace, [1] ModelFlow use DataFrame ModelFlow versions ModelFlow_book vs ModelFlow_stable N National Income Accounts Number of endogenous variables in model Number of equations in model Number of exogenous variables in model Number of variables in model P Pandas, [1] DataFrame Display Options Series PDF routines under Google Colab Plot data from wildcard search of data Plotting scenario results Preparing your workspace, [1] Python classes from package import function importing packages, libraries and modules libraries Named colors packages start session start session with anaconda R Recursive block Recursive equation block Report plots from selection Report table from selection Report writing Reports gdppct - only World Bank conventions joining tables, plots and text by the + operator Plots Plots from selection Plots, by_var= , plots by scenario or by variable Plots, datatype= transformations of results Plots, Joing plots with `|` Plots, options Plots, Special scenario: 'base_last' Table from selection Tables Tables .display(table) method - as html ::: Tables .show method - as text Tables Jupyter Notebook output ::: Tables, A complex table using | Tables, joining tables with `|` Tables; datatype= transformations of results Tables; Display settings Text Vertical tables Restrict the time period of displayed output Restricting the amount of data displayed Return normalized formula of equations Revising model equations, [1] .equpdate() S Saving results .basedf,.lastdf first and last simulation keep= saves to .keep_solutions keep_variables= select variables to keep Scenario set up use .upd() to initialize shock dataframe Scenarios .fix() Exogenizing an endogenous variable A shock to an exogenous variable Changing equations Exogenizing an endogenous variable Exogenous shock over a limited time period Exogenous shocks, [1] Impact Decomposition examples print results Print scenario results Report writing Results - display results as percent change from baseline results - plots Results - shock-control display Simulating a shock to an exogenous variable Simulation execution Solve the model temporarily exogenize endogenous variable Temporarily exognization of a behavioral equation the Keep option use .mfcalc() to initialize shocked dataframe using .upd() to create an input DataFrame for a simulation Using the upd() KG (KEEP_GROWTH) in an exogenous simulation Series Create Series from dictionary Declare with specific index Setting the time period to the entire sample .upd() Shock-control display of results Simulation types Add-factor based endogenous simulation Exogenizing an endogenous variable Exogenous shocks temporarily exogenize endogenous variable Simulations .fix() method .tracedep() Causal tree at the variable level A shock to an exogenous variable Changing equations Endogenous simulation Exogenous shock over a limited time period Exogenous shocks Report writing Results - display results as percent change from baseline Results - shock-control display Simulating a shock to an exogenous variable Solve the model Temporarily exognization of a behavioral equation the keep option use .mfcalc() to initialize shocked dataframe using .upd() to create an input DataFrame for a simulation Using the upd() KG (KEEP_GROWTH) in an exogenous simulation Simultaneous equation block Slicing DataFrame Solve ljit option Solve the model Speed of adjustment in ECMs Storage system basedf/lastdf, [1] keep_solutions, [1] strings multi-line strings Style property of Pandas to customize DataFrame outputs T Targeting Convergence Impulse Max iterations Nonlinearity Tuning Targeting a result Defining Instruments Defining Targets solve for Instruments Test if mnemonic is endogenous tracepre() method trace influnce of causal variables Tutorials Anaconda Jupyter Notebook Mathplotlib Pandas Python U upd() % operator + operator +GROWTH operator = operator =diff operator =GROWTH operator Create new variable Example the Keep_Growth option Examples keep_growth option lprint option Options scale option Time scope of command Update several variables simultaneously Update ModelFlow Updating ModelFlow Anaconda/MiniConda V Variable dependencies, [1] variable names Wildcard Variable selection ! Variable descriptions, [1], [2] # Variable groups, [1] #ENDO all endogenous variables Deferred substitution Use of {cty} Variable names with wildcards Wildcards W Wildcard variable names Wildcard searches| The ! operator: search on variable descriptions Wildcard selection of data with .set_smpl() Restrict the time period with clause .set_smpl - local time scope .set_smpl(begin,end) method to temporarily alter the active rows in the model object data display format