Autocorrelation

reasonable to do so. Moreover, Hausman test choose a more effective model compared to a less efficient as consistent model should presents robust estimates and consistent results owing to the more efficient model. Autocorrelation test Another terms sometimes used for describe Autocorrelation these are “lagged

Course ADVANCED ECONOMETRICS Programme MSc in Finance Site HEC Lausanne Semester Fall 2014 Module Leader Diane Pierret Teaching Assistant Daria Kalyaeva Assessment Type: Empirical Assignment Assessment Title: A Dynamic Model for Switzerland GDP Written by: Group Y (Ariane Kesrewani & Alan Lucero) Additional attachments: Zip Folder containing Matlab code, data and figures

A practical numerical estimation algorithm for the PACF is given by Durbin (V.I.1-29) with (V.I.1-30) The standard error of a partial autocorrelation coefficient for k > p (where p is the order of the autoregressive data generating process; see later) is given by (V.I.1-31) Finally, we define the following polynomial lag-processes (V.I.1-32) where B is the backshift operator (c.q. BiYt

Table: 1, represents the results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R² values in the analysis

LITERATURE REVIEW A study from Ray M. Valadez, “The housing bubble and the GDP: a correlation perspective” in Journal of Case Research in Business and Economics has been done to focus on the relationship between the Real Gross Domestic Product and the situation of Housing Bubble. In this research, the author has concentrated on the time from the beginning of losing trust in government from the financial institution. He emphasizes how much the housing bubble relates to the recession in the economy

Aim of the Project My intention is to create a model in order to forecast the adjusted close price of Paddy Power PLC shares. I will examine some of the different Statistical Modelling techniques and evaluate the merits of each in turn. I will use the Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model if it is found that the variance of the time series is non-constant. My final forecasting model will primarily use the Autoregressive Integrated Moving Average (ARIMA) model to

4.2 ARIMA MODEL This chapter displays the empirical results from the modeling of claim inflation using ARIMA model. Data Description Series=claim inflation Sample 1984-2014 Observations=30 Mean=2.748 Median=2.415 Minimum=1.25 Maximum=7.15 Standard deviation=1.43012 Kurtosis=1.679 Skewness=1.354 4.2.1 Descriptive Statistics for the claim inflation series The data is not stationary since it does not exhibit a certain state of statistical equilibrium showing that the variance changes with

The oceans play an important role in the climate system owing to the interannual and longer timescale variability in sea surface temperature (SST). Hasselmann (1976) proposed that this climate variability could be represented by a stochastic first order auto-regressive process (AR1-process) and should be considered as the null hypothesis for extra-tropical sea surface temperature anomalies (SSTA). According to this concept, SSTAs quickly responds to the atmospheric heat fluxes at short time period

3. Data and Methodology Present paper utilizes the annual data of GDP, Indian FDI, level of Investment and Export in real terms from the period 1989/90 to 2013/14. The concerned variables are transformed into logarithm and hereafter these are denoted by 〖LnGDP〗_t,〖LnFDI〗_t 〖LnI〗_t and 〖LnX〗_t . Fully Modified Ordinary Least Squares (FMOLS) is the main econometric methodology used in this paper to examine the role and impact of Indian FDI on Nepalese economic growth. The FMOLS of economic growth

Referee Report on: Jeffrey L. Callen and Mindy Morel (2001), Linear Accounting Valuation When Abnormal Earnings Are AR (2), “Review of Quantitative Finance and Accounting”, vol. 16 pp 191-203 Introduction In this study, Callen and Morel (Callen & Morel, 2001) compare the linear information dynamics of Ohlson model (Ohlson, 1995) with AR (1) process, which is used in Ohlson’s research, and AR (2) process for earnings, book values and dividends. The purpose of this research is to evaluate the forecasting