ragul n

recent posts

resources

podcasts

1. amit verma’s seen and the unseen 🔗
2. struthi rajagopalan’s ideas of india 🔗
3. amit verma and ajay shah’s everything is everything 🔗
4. macro musing by david beckworth 🔗
5. capitalism and freedom in 21st century by jon hartley 🔗

blogs

1. economics
   1. paul krugman wonks out 🔗
   2. noahpinion 🔗
   3. marginal revolution 🔗
   4. apricitas economics 🔗
   5. Liberty Street Economics 🔗
   6. best of econtwitter 🔗
   7. greg mankiw’s blog 🔗
   8. Economics and Finacne at Project Syndicate 🔗
   9. the grumpy economist by john cochrane 🔗
   10. the great gender divergence 🔗
   11. the central bank watcher 🔗
2. news
   1. ft’s Alphaville 🔗
   2. Currency Througts 🔗
   3. ft’s newsletters 🔗
   4. bloomberg’s newsletters 🔗
3. india
   1. anticipating the unintended 🔗
   2. urbanomics 🔗
   3. ideas for india 🔗
   4. mostly economics 🔗
   5. ft’s india business briefing 🔗
   6. the leap blog 🔗

macroeconomics

1. overview articles:

   1. what do we know about macroeconomics that fisher and wicksell did not?, blanchard (2000) 🔗
   2. macroeconomist as scientist and engineers, mankiw (2006) 🔗
   3. revolution and evolution in twentieth-century macroeconomics, woodford (1999) 🔗
   4. macroeconomics is still in its infancy, noha smith 🔗
   5. what i learned in econ grad school, noha smith [pt. 1 🔗; pt. 2 🔗 ]
   6. bob lucas and his papers, john cochrane 🔗
   7. macroeconomics: a reading list 🔗
   8. macroeconomics after lucus, sargent (2024) 🔗
2. core textbooks: phdmacrobook 🔗, acemoglue 🔗, stocky & lucus 🔗, sargent & ljungqvist 🔗

3. new keynsian: gali 🔗, woodford 🔗

4. other lecture notes:
   1. chris carroll’s notes 🔗
   2. vv chari’s notes 🔗
   3. jesús fernández-villaverde’s lecture slides 🔗

measure theory

1. terry tao’s notes 🔗
2. measure, integration & real analysis by axler 🔗
3. measure theory and probability theory by krishna athreya and soumendra lahiri 🔗
4. krishna jagannathan’s probability foundations for electrical engineers 🔗 \

functional anlaysis

1. notes on functional analysis by rajendra bhatia 🔗
2. measure, integration & real analysis by axler 🔗
3. functional analysis for probability and stochastic processes by adam bobrowski 🔗

measure-theoritic probability & stochastic processes

1. probability and stochastic by erhan cinlar 🔗
2. measure, probability and functional analysis by hannah geiss & stefan geiss 🔗
3. probability with martingales by david williams 🔗
4. measure theory, probability, and stochastic processes by jean-françois le gall 🔗

stochastic calculus

1. stochastic integration and differential equations by philip protter 🔗
2. introduction to stocahstic calculus with application by fima klebaner 🔗
3. Brownian Motion and stochastic calculus by karatzas & shreve
4. levy processes and stochastic calculus b david applebaum 🔗
5. intro to stochastic integration by hui-hsiung kuo 🔗
6. stochastic calculus and financial applications by michael steele

differential equations

1. mit opencourseware differential equations course🔗
2. steve brunton’s youtube course 🔗
3. non-linear dynamics and chaos by steven strogatz 🔗
4. love affiers and differential equations by steven strogatz

time series

1. john cochrane’s time series for macroeconomists
2. introduction to difference equations by saber elaydi
3. applied time series by enders
4. time series by hamilton

deep learning

1. dive into deep learning book 🔗
2. theory of deep learning book 🔗
3. theoretical foundations of deep learning, ankur moitra [🔗]
4. deep learning theory, matus telgarsky 🔗
5. deep learning architectures: a mathematical approach, ovidiu calin 🔗

reinforcement learning

1. neuro-dynamic programming, bertsekas 🔗
2. foundations of rl, chi jin 🔗
3. optimal control and rl at cmu 🔗
4. intro to reinforcement learning by barto & sutton

machine learning

1. andrew ng’s lecture notes

youtube

1. math lectures
   1. steve brunton 🔗
   2. jason bramburger 🔗
   3. the bright side of mathematics 🔗
   4. mike, the mathematician 🔗
2. casual
   1. vertasium
   2. 3blue1brown