A Frequency Domain Test for a Spatial Unit Root Process

2012 ◽  
Author(s):  
Andres Ramirez Hassan
Keyword(s):  
Frequency Domain ◽  
Unit Root ◽  
Spatial Unit ◽  
Unit Root Process

Fathoming the theta method for a unit root process

2012 ◽  
Vol 25 (1) ◽  
pp. 105-124 ◽  
Author(s):  
D. Thomakos ◽  
K. Nikolopoulos
Keyword(s):  
Unit Root ◽  
Unit Root Process ◽  
Theta Method

scholarly journals Convolution Based Unit Root Processes: a Simulation Approach

2016 ◽  
Vol 5 (6) ◽  
pp. 22
Author(s):  
Fabio Gobbi
Keyword(s):  
Negative Correlation ◽  
Unit Root ◽  
Unit Roots ◽  
Copula Function ◽  
The State ◽  
Dependence Structure ◽  
Simulation Approach ◽  
State Variable ◽  
Unit Root Process

We propose a convolution based approach to the simulation of a modified version of a unit root process where the state variable $Y_{t-1}$ is dependent on the innovation $\varepsilon_t$. The dependence structure is given by a copula function $C$. We study by simulation the effect of a negative correlation on the properties of unit roots. We call this process C-UR(1).

THE EFFICIENT MARKET HYPOTHESIS REVISITED: EVIDENCE FROM THE FIVE SMALL OPEN ASEAN STOCK MARKETS

2012 ◽  
Vol 57 (03) ◽  
pp. 1250021 ◽  
Author(s):  
QAISER MUNIR ◽  
KOK SOOK CHING ◽  
FUMITAKA FUROUKA ◽  
KASIM MANSUR
Keyword(s):  
Stock Returns ◽  
Unit Root ◽  
Stock Prices ◽  
Stock Price ◽  
Efficient Market Hypothesis ◽  
Linear Series ◽  
Efficient Market ◽  
Threshold Autoregressive ◽  
Non Linear ◽  
Unit Root Process

The efficient market hypothesis (EMH), which suggests that returns of a stock market are unpredictable from historical price changes, is satisfied when stock prices are characterized by a random walk (unit root) process. A finding of unit root implies that stock returns cannot be predicted. This paper investigates the stock prices behavior of five ASEAN (Association of Southeast Asian Nations) countries i.e., Indonesia, Malaysia, Philippines, Singapore and Thailand, for the period from 1990:1 to 2009:1 using a two-regime threshold autoregressive (TAR) approach which allows testing nonlinearity and non-stationarity simultaneously. Among the main findings, our results indicate that stock prices of Malaysia and Thailand are a non-linear series and are characterized by a unit root process, consistent with the EMH. Furthermore, we find that stock prices of Indonesia, Philippines and Singapore follow a non-linear series, however, stock price indices are stationary processes that are inconsistent with the EMH.

scholarly journals Threshold effects and unit roots of real commodity prices since the mid-nineteenth century

2020 ◽  
Vol 9 (4) ◽  
pp. 342-349
Author(s):  
Pedro Clavijo ◽  
Jacobo Campo ◽  
Henry Mendoza
Keyword(s):  
Unit Root ◽  
Autoregressive Model ◽  
Commodity Prices ◽  
Unit Roots ◽  
Terms Of Trade ◽  
Threshold Effects ◽  
Threshold Autoregressive ◽  
Threshold Autoregressive Model ◽  
Primary Goods ◽  
Unit Root Process

This paper investigates whether a unit root process and nonlinearities can characterize real commodity prices for six major primary goods. An unconstrained two-regime threshold autoregressive model is used with an autoregressive unit root. Among the main results, it is found that terms of trade for agricultural, mineral, non-tropical, and non-oil goods are nonlinear processes that are characterized by a unit root process. The finding of nonlinearities explains why the deterioration of the terms of trade has been episodic. Additionally, we found there is no statistical evidence supporting the Prebisch-Singer hypothesis for agricultural, mineral, non-tropical, and non-oil goods.

Are US real house prices stationary? New evidence from univariate and panel data

2016 ◽  
Vol 20 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Jing Zhang ◽  
Robert de Jong ◽  
Donald Haurin
Keyword(s):  
Panel Data ◽  
Error Correction ◽  
Unit Root ◽  
House Prices ◽  
House Price ◽  
State Level ◽  
Data Sets ◽  
Modeling And Forecasting ◽  
Unit Root Process ◽  
Cointegration Relationship

AbstractMany papers in the housing literature treat the intertemporal evolution of the logarithm of US real house prices as a unit root process. They also study the cointegration relationship among the logarithm of real house prices and fundamental economic variables such as income and they apply an error correction specification for modeling and forecasting real house prices. This paper argues that the logarithm of US real house price is not a unit root process. Instead, the evidence from a 120-year national dataset and metro area level and state level panel data sets supports the notion that US house prices are trend stationary. One result of this conclusion is that the validity of analyses of US house prices based on cointegration and error correction models needs to be reconsidered.

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