Profile
Dr Qian Li is a Lecturer in Digital Finance in the Institute for Digital Technologies at Loughborough University London. Her research lies at the intersection of macroeconomics, housing and labour markets, financial markets, and artificial intelligence. She develops structural macroeconomic models, including dynamic stochastic general equilibrium and overlapping-generations models, and combines them with Bayesian estimation, econometric methods, machine learning, natural language processing, and reinforcement learning.
Her current research examines the effects of artificial intelligence on labour-market dynamics; monetary policy and housing wealth; credit reallocation during housing booms; and the transmission of energy-price shocks to housing and financial markets. She is also interested in financial forecasting, commodity and foreign-exchange markets, and the macroeconomic implications of the green-energy transition.
Qian teaches across economics, finance, and digital technology programmes and works with industry partners on applied interdisciplinary research. Before joining Loughborough University London, she taught macroeconomics as a Postgraduate Teaching Associate at the University of Exeter and gained experience in the financial sector.
Academic Background
- PhD in Economics, University of Exeter, 2023
- MSc in Financial Economics with Distinction, Cardiff University, 2017
- BSc in Public Finance, Zhongnan University of Economics and Law, 2015
Teaching
- LLP104 Statistical Methods in Finance, 2023 to Present
- LLP252 Data Analytics Tools in the Digital Economy, 2023 to Present
- LLP233 Advanced Programming and Data Visualisation, 2023 to 2024
- LLP108 Collaborative Project, 2023 to Present
- LLP249 Digital Strategy, 2023 to 2024
Research interests
- Macroeconomics, housing markets, labour markets, and macro-finance
- Artificial intelligence, technological change, and labour-market dynamics
- DSGE and overlapping-generations models
- Bayesian estimation and structural econometrics
- Machine learning, natural language processing, and reinforcement learning in economics and finance
- Monetary policy, credit conditions, and macroprudential policy
- Energy-price shocks, commodity markets, and the green-energy transition
Working papers and research in progress
Oil Price Shocks and US House Prices: The Role of Credit Conditions (2026), joint with Ernil Sabaj. Online Appendix.
We study how oil-price shocks transmit to US housing markets. In a structural vector autoregression identified using oil-supply news shocks, a 10 percent oil-price increase lowers US house prices by 1 to 1.2 percent. State-dependent local projections show that credit conditions govern the persistence of the decline rather than its initial impact. Under loose credit conditions, house prices fall by 1.9 percent and recover within 18 months. Under elevated spreads, the trough is three times shallower and the decline does not reverse. A two-sector New Keynesian DSGE model with oil as an intermediate input rationalises these findings. Oil shocks raise non-housing prices by more than house prices, reducing the relative price of housing, while bank balance-sheet and borrower-collateral frictions shape the recovery. Tighter loan-to-value ratios shift the strain to borrower loan pricing, whereas tighter bank leverage shifts it to capital spreads, leaving the house-price response unchanged. Macroprudential policy therefore reallocates rather than eliminates the financial effects of energy shocks.
Targeted Refinancing and Credit Reallocation during Housing Booms in China (2026), joint with Ernil Sabaj. Working paper and Online Appendix.
The accompanying Online Appendix derives the household, production, price-setting, capital-accumulation, intermediary, and policy blocks; states the complete nonlinear equilibrium closure; maps the equations to the canonical Dynare source; documents the deterministic steady state and first-order solution; and reports calibration, robustness, welfare, empirical, and native-replication details.
Monetary Policy and the Housing Wealth Effect: Evidence from an OLG Structure (2026). Working paper.
This paper develops a DSGE model in which housing and financial wealth directly affect consumption through an overlapping-generations structure. It examines how monetary policy affects the housing market through this wealth channel. With flexible prices, monetary policy has real effects through housing wealth, and the effect is stronger in the housing sector than in the non-housing sector. Price stickiness magnifies this transmission channel. The model is then estimated using Bayesian methods. The estimates indicate that monetary-policy shocks have only a small effect on house prices, while housing and non-housing technology shocks explain most housing-market fluctuations. Stock-price shocks account for approximately 10 percent of the volatility in both real house prices and housing investment.
Artificial Intelligence and Labour Market Dynamics: A DSGE Approach (work in progress), joint with Chowool Kim, University of Warwick.
This project develops a DSGE framework to study how advances in artificial intelligence affect labour demand, worker reallocation, skill acquisition, job matching, wages, and aggregate economic dynamics.
Conference presentations
2025
- 18th Annual Meeting of The Portuguese Economic Journal, July 2025, Lisbon, Portugal
- XXVII Applied Economics Meeting, June 2025, Murcia, Spain
2024
- XXV Conference on International Economics and XII Meeting on International Economics, Chair and Speaker, Spain
- The Socially Just Planning Doctoral Network Seminar Series, Panel Speaker, University College London
- 2024 RCEA International Conference in Economics, Econometrics, and Finance, London
- 24th Scottish Economic Society Annual Conference
2023
- 17th South-Eastern European Economic Research Workshop of the Bank of Albania
- 29th International Conference on Computing in Economics and Finance
Current PhD and research supervision interests
Qian welcomes enquiries from prospective PhD students whose proposed research falls within the following areas:
- Machine-learning and natural-language-processing applications in housing markets, labour markets, and macro-finance
- Hybrid models combining machine learning with DSGE, overlapping-generations, or econometric frameworks
- Artificial intelligence, technological change, job creation, worker reallocation, and labour-market frictions
- Dynamic optimisation in macroeconomic models using reinforcement learning
- Analysis of structured and unstructured economic and financial data
- Macroeconomic implications of the green-energy transition