arXiv subjects

Sudiksha Joshi

Publications and source records attributed to Sudiksha Joshi.

4 recordsLinked to original sources

arXiv2021

Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs

Using a state-space system, I forecasted the US Treasury yields by employing frequentist and Bayesian methods after first decomposing the yields of varying maturities into its unobserved term structure factors. Then, I exploited the structure of the state-space model to forecast the Treasury yields and compared the forecast performance of each model using mean squared forecast error. Among the frequentist methods, I applied the two-step Diebold-Li, two-step principal components, and one-step Kalman filter approaches. Likewise, I imposed the five different priors in Bayesian VARs: Diffuse, Minnesota, natural conjugate, the independent normal inverse: Wishart, and the stochastic search variable selection priors. After forecasting the Treasury yields for 9 different forecast horizons, I found that the BVAR with Minnesota prior generally minimizes the loss function. I augmented the above BVARs by including macroeconomic variables and constructed impulse response functions with a recursive ordering identification scheme. Finally, I fitted a sign-restricted BVAR with dummy observations.

econ.EM↗

arXiv2020

Forecasting the Leading Indicator of a Recession: The 10-Year minus 3-Month Treasury Yield Spread

In this research paper, I have applied various econometric time series and two machine learning models to forecast the daily data on the yield spread. First, I decomposed the yield curve into its principal components, then simulated various paths of the yield spread using the Vasicek model. After constructing univariate ARIMA models, and multivariate models such as ARIMAX, VAR, and Long Short Term Memory, I calibrated the root mean squared error to measure how far the results deviate from the current values. Through impulse response functions, I measured the impact of various shocks on the difference yield spread. The results indicate that the parsimonious univariate ARIMA model outperforms the richly parameterized VAR method, and the complex LSTM with multivariate data performs equally well as the simple ARIMA model.

q-fin.ST↗

arXiv2020

Reforming the State-Based Forward Guidance through Wage Growth Rate Threshold: Evidence from FRB/US Simulations

I have analyzed the practicality of the Evans Rule in the state based forward guidance and possible ways to reform it. I examined the biases, measurement errors, and other limitations extant in the unemployment and the inflation rate in the Evans Rule. Using time series analysis, I calibrated the thresholds of ECI wage growth and the employment to population ratio and investigated the relationship between other labor utilization variables. Then I imposed various shocks and constructed impulse response functions to contrast the paths of eight macroeconomic variables under three scenarios. The results suggest that under the wage growth rate scenario, the federal funds rate lift off earlier than under the current Evans Rule.

econ.GN↗

arXiv2019

Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model

In this research paper, I have performed time series analysis and forecasted the monthly value of housing starts for the year 2019 using several econometric methods - ARIMA(X), VARX, (G)ARCH and machine learning algorithms - artificial neural networks, ridge regression, K-Nearest Neighbors, and support vector regression, and created an ensemble model. The ensemble model stacks the predictions from various individual models, and gives a weighted average of all predictions. The analyses suggest that the ensemble model has performed the best among all the models as the prediction errors are the lowest, while the econometric models have higher error rates.

econ.EM↗