scholarly journals Large mixed-frequency VARs with a parsimonious time-varying parameter structure

2021 ◽  
Author(s):  
Thomas B Götz ◽  
Klemens Hauzenberger
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Computational Complexity ◽  
Common Factor ◽  
Time Varying ◽  
Frequency Vector ◽  
Mixed Frequency ◽  
Forecasting Models ◽  
Time Varying Parameter ◽  
Joint Dynamics

Summary In order to simultaneously consider mixed-frequency time series, their joint dynamics, and possible structural change, we introduce a time-varying parameter mixed-frequency vector autoregression (VAR). Time variation enters in a parsimonious way: only the intercepts and a common factor in the error variances can vary. Computational complexity therefore remains in a range that still allows us to estimate moderately large VARs in a reasonable amount of time. This makes our model an appealing addition to any suite of forecasting models. For eleven U.S. variables, we show the competitiveness compared to a commonly used constant-coefficient mixed-frequency VAR and other related model classes. Our model also accurately captures the drop in the gross domestic product during the COVID-19 pandemic.

Online Bayesian Time-varying Parameter Estimation of HIV-1 Time-series*

2012 ◽  
Vol 45 (16) ◽  
pp. 1294-1299 ◽  
Author(s):  
András Hartmann ◽  
Susana Vinga ◽  
João M. Lemos
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Time Varying ◽  
Time Varying Parameter ◽  
Hiv 1

scholarly journals Structural Breaks dan Ketidakstabilan Permintaan Uang di Indonesia

2017 ◽  
Vol 17 (2) ◽  
pp. 169-183
Author(s):  
Deviyantini Deviyantini ◽  
Iman Sugema ◽  
Tony Irawan
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Interest Rate ◽  
Money Demand ◽  
Structural Breaks ◽  
Demand Function ◽  
Series Data ◽  
Time Varying ◽  
Money Demand Function ◽  
Time Varying Parameter

Structural Breaks and Instability of Money Demand in IndonesiaThis research aims to identify the sources of instability of the money demand function (M1 and M2) due to structural changes that occur as a result of economic shocks. These shocks, are technically shown by the presence of structural breaks in the data and can lead the parameters non-constancy. The instability of the money demand function was analyzed using the Gregory and Hansen test. The source of instability of the money demand was identified using time varying parameter model. This research used quarterly time series data from 1993Q1 to 2013Q4. The result of Gregory and Hansen test indicates there is no long term equilibrium between variables (money demand, income, domestic interest rate, foreign interest rate, exchange rate, and inflation) in the model, neither M1 nor M2 model. On the other word, money demand function is unstable. The source of the instability is exchange rate variable.Keywords: Stability Money Demand; Structural Breaks; Time Varying Parameter ModelAbstrakPenelitian ini bertujuan untuk mengidentifikasi sumber-sumber ketidakstabilan fungsi permintaan uang (M1 dan M2) akibat dari perubahan struktural yang terjadi karena adanya guncangan ekonomi. Guncangan tersebut, yang secara teknis ditunjukkan oleh keberadaan structural breaks di dalam data, dapat menyebabkan parameter menjadi tidak konstan. Ketidakstabilan fungsi permintaan uang dianalisis dengan menggunakan Gregory and Hansen test. Sumber ketidakstabilan dari permintaan uang diidentifikasi dengan menggunakan time varying parameter model. Penelitian ini menggunakan data time series dalam bentuk kuartalan dari 1993Q1 sampai 2013Q4. Hasil Gregory and Hansen test menunjukkan bahwa tidak ada keseimbangan jangka panjang di antara variabel-variabel (permintaan uang, pendapatan, suku bunga domestik, suku bunga luar negeri, nilai tukar, dan inflasi) di dalam model, baik pada model M1 maupun M2. Dengan kata lain, fungsi permintaan uang tidak stabil. Sumber ketidakstabilan tersebut berasal dari variabel nilai tukar.

Structural change and economic growth in India: a comparative study of Punjab

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anirban Sanyal ◽  
Nirvikar Singh
Keyword(s):  
Economic Growth ◽  
Structural Change ◽  
Structural Changes ◽  
Green Revolution ◽  
State Level ◽  
Productivity Change ◽  
Time Varying ◽  
Content Type ◽  
Panel Regressions ◽  
Time Varying Parameter

Purpose The Green Revolution transformed agriculture in the Indian State of Punjab, with positive spillovers to the rest of India, but recently the state’s economy has fallen dramatically in rankings of per capita state output. Understanding the trajectory of Punjab’s economy has important lessons for all of India. Economic development is typically associated with changes in economic structure, but Punjab has remained relatively reliant on agriculture rather than shifting economic activity to manufacturing and services, where productivity growth might be greater. Design/methodology/approach The authors empirically examine structural change in the Punjab economy in the context of structural change and economic growth across the States of India. The authors calculate structural change indices and map their pattern over time. The authors estimate panel regressions and time-varying parameter regressions, as well as performing productivity change decompositions into within-sector and structural changes. Findings Panel regressions and time-varying-coefficient regressions suggest a significant positive influence of structural change on state-level growth. In addition, growth positively affected structural change across India’s states. The relative lack of structural change in Punjab’s economy is implicated in its relatively poor recent growth performance. Comparisons with a handful of other states reinforce this conclusion: Punjab’s lack of economic diversification is a plausible explanation for its lagging economic performance. Originality/value This paper performs a novel empirical analysis of structural change and growth, simultaneously using three different approaches: panel regressions, time-varying parameter regressions and productivity decompositions. To the best of the authors’ knowledge, it is the only paper we are aware of that combines these three approaches.

scholarly journals Estimation of Market Risk Premium for Japan

2009 ◽  
Vol 1 (1) ◽  
Author(s):  
Seoungpil AHN ◽  
Keshab SHRESTHA
Keyword(s):  
Time Series ◽  
Risk Aversion ◽  
Kalman Filtering ◽  
Risk Premium ◽  
Time Varying ◽  
Random Walk Process ◽  
Market Risk Premium ◽  
Time Varying Parameter ◽  
Variance Risk ◽  
Over Time

In this paper, the time series of risk aversion parameter is estimated for the Japanese stock market using weekly return data covering 2/7/1973 to 12/27/2000. The time series of risk aversion parameter is estimated with the Time Varying Parameter (EVP) GARCH-M model proposed by Chou, Engle and Kane (1992), which allows for the risk aversion parameter to change over time by modeling the risk aversion parameter to follow a random walk process. The risk aversion parameter is found to range between 3.5 to 2.2. We also find that the risk aversion parameter has not significantly changed over time. This implies that most of the variation in excess return can be explained by the variation in the market (variance) risk. Keywords: GARCH-M, Kalman Filtering, risk aversion, time-varying parameter, volatility.

scholarly journals A Modeling Study on the Estimation of COVID-19 Daily and Weekly Cases and Reproduction Number Using the Adaptive Kalman Filter: The Example of Ziraat Bank, Turkey

2021 ◽  
pp. 15-27
Author(s):  
İlker Met ◽  
Levent Özbek ◽  
Himmet Aksoy ◽  
Ayfer Erkoç
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Reproduction Number ◽  
Jel Classification ◽  
Model Parameters ◽  
Time Varying ◽  
Adaptive Kalman Filter ◽  
Human Race ◽  
Time Varying Parameter ◽  
Case Number

Abstract Since the beginning of 2020, the world has been struggling with a viral epidemic (COVID-19), which poses a serious threat to the collective health of the human race. Mathematical modeling of epidemics is critical for developing such policies, especially during these uncertain times. In this study, the reproduction number and model parameters were predicted using AR(1) (autoregressive time-series model of order 1) and the adaptive Kalman filter (AKF). The data sample used in the study consists of the weekly and daily number of cases amongst the Ziraat Bank personnel between March 11, 2020, and April 19, 2021. This sample was modeled in the state space, and the AKF was used to estimate the number of cases per day. It is quite simple to model the daily and weekly case number time series with the time-varying parameter AR(1) stochastic process and to estimate the time-varying parameter with online AKF. Overall, we found that the weekly case number prediction was more accurate than the daily case number (R2 = 0.97), especially in regions with a low number of cases. We suggest that the simplest method for reproduction number estimation can be obtained by modeling the daily cases using an AR(1) model. JEL classification numbers: C02, C22, C32. Keywords: COVID-19, Modeling, Reproduction number estimation, AR(1), Kalman filter.

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