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Macro by Mark

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OverviewThe flagship learning arc.ConceptsCore measures, terms, and mechanisms.PolicyFiscal, monetary, and transmission routes.

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SchoolsCompeting macro traditions.CompareLine up schools and assumptions.HistoryHow the field evolved.

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Macro by Mark
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Macro
Overview
OverviewThe flagship learning arc.ConceptsCore measures, terms, and mechanisms.PolicyFiscal, monetary, and transmission routes.
Debate and context
SchoolsCompeting macro traditions.CompareLine up schools and assumptions.HistoryHow the field evolved.
Work with it
ModelsEmpirical, structural, and theoretical routes.GlossaryFast definitions while you learn.
News
Calendar
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All libraryThe full tracked working set.GrowthOpen this indicator lane.Prices & InflationOpen this indicator lane.Labor MarketOpen this indicator lane.Monetary & Financial ConditionsOpen this indicator lane.Nowcasting & Leading IndicatorsOpen this indicator lane.
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Data-Driven Models

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Macro by Mark

U.S. macro data with release timing, boards, and macro context.

Public U.S. data from agencies and market feeds.

MarkJayson.com

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© 2026 Mark Jayson Martinez Farol

Data-Driven Models

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Data-Driven Models

Review dataset

Workflow step

Review the aligned dataset, choose the harmonization rule, and lock the transform assumptions before modeling.

Modeling frame

0 connected series staged

Harmonization: none
Transform and harmonization controls
Why use log transform?Use this when the series stays positive and moves proportionally with its level. It makes large-scale moves easier to compare with earlier ones.
When should I use a growth transform?Use a growth transform when the question is about pace rather than level. It helps compare series that live on very different scales.
Why detrend a series?Detrend when long-run drift hides the cycle you want to inspect. Leave it off if the level itself is the thing you are trying to explain.
When should I adjust seasonality?Use this when within-year seasonal patterns are obscuring the underlying signal. If the source is already adjusted, keep the current setting.
How should I handle missing values?Warn preserves the raw series, drop keeps only complete observations, and interpolate fills smaller holes. Choose the least aggressive option that still leaves you with usable data.
When do I need frequency harmonization?Use this when your inputs arrive at different frequencies. Aggregate moves faster data up to a slower cadence; interpolate smooths slower data down.

Diagnostics preview

Choose at least one connected series to open the raw-vs-analysis preview.

Readiness

  • Add at least one connected series or an uploaded file.
Continue to modelOpen sandbox