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Getting started with knbsdata7 days ago
What is and is not in this version | Five ideas worth knowing | 1. Everything carries its provenance | 2. Citations come from the data, not from you | 3. Access conditions are data, not decoration | 4. Failure is explicit | 5. Everything is a tibble that speaks tidyverse | A first session | Configuration | Caching | Where to next
County codes and joins18 days ago
The reference | The two classic corruptions | A worked join | Gross County Product | Health by county: the KDHS | Matching county names across sources | What is deliberately absent
Source status and limitations18 days ago
Registered sources | What each stability level means | What works | What does not, and why | The archive's microdata cannot be downloaded | Documentation files are not listed | The newest Gross County Product years are still documents | Geography: one vintage, no boundaries, no classifications | Variable documentation is uneven | How you will know if something changes | Reporting a change | Disclaimer
Working with the survey catalogue19 days ago
What is supported | Finding a survey | Reading a study's documentation | Searching variables | Files, and what you may do with them | Downloading microdata | Open microdata: the FinAccess surveys | Handling errors | Citing what you used | Caching
The Statistical Abstract as tidy tables21 days ago
Curated, not guessed | Listing and fetching | Reading the columns | County tables join everything else | Units, footnotes and sources | Into the tidyverse | Provenance travels | What this is not
Indicators: census tables and consumer prices21 days ago
The Data Portal (SDMX) | Open Data for Africa (the republisher) | Consumer prices | CPI geography is not census geography | Retail prices | Census tables | What the census source is, and is not | Finding a table | Getting observations | Filtering geography | A worked example | Provenance and citation | Honesty notes
A forecast in ten minutes: the canonical QPM in qpmR27 days ago
The canonical model | Solving and checking | Monetary transmission | History and forecast | Specification checks | Adapting the model to your country | Real data: Czechia | Policy analysis: conditions, judgment, rounds | Closing the round: audit and report | Where to go next
Estimating qpmR models: priors, posteriors, identification27 days ago
Priors | A laboratory: estimating an AR(1) | Identification: ask before you sample | Marginal likelihood and Bayes factors | On real data
Introduction to the dsge Package1 months ago
Overview | A Simple AR(1) Model | Simulate Data | Specify the Model | Solve at Known Parameters | Estimate the Model | Postestimation | Impulse-Response Functions | Forecasting | A Three-Equation New Keynesian Model | Model Specification | Simulate and Estimate | Nonlinear DSGE Models | A Simple RBC Model | Steady State | Solve and Inspect | IRFs | Bayesian Estimation | Specifying Priors | Running the Sampler | Posterior Diagnostics and IRFs | What This Package Currently Supports | Notes
Introduction to the dcce Package: DCCE Estimation for Panel Data6 months ago
Introduction | The Problem: Cross-Sectional Dependence | What is cross-sectional dependence? | Consequences of ignoring CSD | Testing for CSD: the pcd_test() function | The Model: Heterogeneous Panels with Common Factors | The general DGP | The CCE approach | Dynamic extension (DCCE) | Estimators | Mean Group (MG) | CCE Mean Group (CCE-MG) | Dynamic CCE (DCCE) | Verify that DCCE removes CSD | Regularized CCE (rCCE) | Long-Run Estimators: CS-DL, CS-ARDL, and PMG | CS-DL (Cross-Sectionally Augmented Distributed Lag) | CS-ARDL (Cross-Sectionally Augmented ARDL) | PMG (Pooled Mean Group) | Comparing All Estimators | The CD Test Suite | Bootstrap Inference | Worked Example: Ditzen (2018) Growth Regression | Step 1: Detect CSD in naive residuals | Step 2: Mean Group (no CSD correction) | Step 3: Dynamic CCE with 3 CSA lags (Ditzen 2018 Example 7.3) | Step 4: Summary table | Step 5: Unit-level heterogeneity | Auxiliary Diagnostics | Exponent of cross-sectional dependence | Information criterion for CSA lag selection | Rank condition | Panel unit root test: Pesaran CIPS | Slope heterogeneity: Swamy and Pesaran-Yamagata | Hausman test: MG vs pooled | broom compatibility | Confidence intervals | Visualising heterogeneity | Additional Estimators (v0.2.0+) | Augmented Mean Group (AMG) | Interactive Fixed Effects (IFE, Bai 2009) | Pooled CCE (CCEP) | Additional Diagnostics (v0.3.0+) | Panel Granger causality | IPS and LLC panel unit root tests | Pedroni cointegration test | Structural break test | Impulse Response Functions (v0.4.0) | The dcce_workflow() Pipeline | Quick-Start Guide | References