Panel ARDL Explained: Why It's the Best Choice for Your Panel Data

🔒 More advanced videos with Datasets (Excel) + Stata Do-files available as Members-Only - Join P.E.R.S here:    / @cruncheconometrix   P.E.R.S Full List (92 videos, 15 Courses):    • Members-only videos   Basics of Panel ARDL Modeling - Panel Data and ARDL Tutorial for MSc Dissertation | UK, US, Canada Are you writing your MSc Economics, Finance, Banking or PhD dissertation using panel ARDL - ARDL #paneldata #pedronitest #panelardl - panel data and ARDL? You are in the right place. WHAT YOU WILL LEARN: ✓ Why Panel ARDL matters - Reasons for conducting panel data analysis - Such as: (1) main interest is group and not individual units in group - which means very little information is lost by taking panel perspective - e.g., interest in SSA countries as group not individual country - panel perspective sufficient, (2) use of panel rather than time series - increases sample size - N*T larger - more observations - more power - e.g., 50 countries * 20 years = 1000 observations vs 20 observations for one country time series - more degrees of freedom, (3) Panel controls for heterogeneity - unobserved individual effects - e.g., country fixed effects - culture, geography, (4) Panel ARDL allows I(0)/I(1) mix - unlike traditional panel cointegration which requires I(1) - Panel ARDL more flexible - suitable for MSc with mix of stationary and non-stationary, (5) Panel ARDL estimates both long-run and short-run - error correction model - ECT - speed of adjustment - long-run causality ✓ How Panel ARDL works - Panel ARDL re-parameterised as error correction model - ECM: ΔY_it = phi_i (Y_{i,t-1} - theta' X_{i,t}) + sum lagged ΔY + sum lagged ΔX + error - phi_i = ECT coefficient - speed of adjustment - should be negative and significant and between -1 and 0 - e.g., -0.4 means 40% disequilibrium corrected each period, theta = long-run coefficients - long-run relationship, short-run coefficients on differenced terms - short-run dynamics - Panel ARDL steps - Steps 1-4: Specify re-parameterised ARDL ECM, describe data, correlation, panel unit roots IPS, LLC, etc. I(0)/I(1) mix no I(2), Steps 5-7: Lag selection per unit per variable via information criteria, most common lag per variable, Pedroni or Westerlund cointegration, Step 8: Hausman test PMG vs MG vs DFE - choose estimator, Steps 9-10: Causality - Granger, Wald, Weak Exogeneity - and policy implications - optional ✓ Common mistakes MSc students make: Not testing panel unit roots before Panel ARDL - need IPS, LLC etc. - I(0)/I(1) mix allowed no I(2) - if I(2) cannot use Panel ARDL - need to test, Not doing lag selection per unit per variable - need to choose most common lag via information criteria - AIC, SC - per group per variable, Not testing Pedroni cointegration before estimation - need cointegration via Pedroni or Westerlund - if no cointegration long-run not valid, Not doing Hausman test PMG vs MG vs DFE - need Hausman to choose estimator. WHO THIS IS FOR: MSc, MBA, PhD Economics, Finance, Banking, Development Economics, Energy Economics students in UK (Warwick, Manchester, Leeds, Birmingham, Glasgow, Edinburgh, LSE), US, Canada, Australia, EU using Panel ARDL - panel data and ARDL - I(0)/I(1) mix. OFFICIAL LINKS & COMMUNITY: 🌐 Website: https://cruncheconometrix.com 🛒 Data Shop: https://cruncheconometrix.com/view/da... 🎓 Members-Only:    • Members-only videos   ⭐ Join Membership:    / @cruncheconometrix   👥 Facebook:   / cruncheconometrix   💼 LinkedIn:   / cruncheconometrix   🔔 Subscribe: https://www.youtube.com/c/CrunchEcono... If this helped, LIKE, COMMENT your Panel ARDL variables and country, SHARE. FAQ: Q: What is Panel ARDL? A: Panel Autoregressive Distributed Lag - combines panel data and ARDL - allows I(0)/I(1) mix no I(2) - allows long-run homogeneous short-run heterogeneous - PMG, MG, DFE - estimates long-run and short-run via ECM - ECT negative significant indicates long-run relationship and speed of adjustment. Q: Why use panel data rather than time series? A: Reasons: (1) main interest is group not individual units - little information lost taking panel perspective, (2) panel increases sample size N*T more observations more power degrees freedom, (3) panel controls heterogeneity unobserved individual effects, (4) Panel ARDL allows I(0)/I(1) mix, (5) estimates long-run and short-run. #PanelARDL #PanelData #ARDL #PedroniTest #PanelARDLBasics #PMG #MG #DFE #PanelDataAnalysis #Econometrics #Stata #MScDissertation #UKUniversities #PanelARDLTutorial #ErrorCorrectionModel #PhDResearch #DissertationHelp #PanelARDLReasons #PooledMeanGroup #MeanGroup #CrunchEconometrix