This document describes Hanan Naser's research estimating and forecasting Bahrain's quarterly GDP using simple regression and factor models. It finds that a simple regression model augmented with intercept correction provided the most reliable estimates. Several models were able to shorten the lag in official GDP estimates by one week. The research adapts forecasting techniques used for developed countries to the case of Bahrain as an oil-producing developing country.
Slides presented at the Productivity User Group meeting on 16th March 2018. Slides presented on Labour productivity methodological changes, Regional labour productivity, Review of international best practice in the production of productivity statistics and Multi-factor productivity
Why stress on estimating quantum, in studies concerning black money in the co...D Murali ☆
Why stress on estimating quantum, in studies concerning black money in the country? - T.N. Pandey - Article published in Business Advisor, dated August 10, 2014 http://www.magzter.com/IN/Shrinikethan/Business-Advisor/Business/
Slides presented at the Productivity User Group meeting on 16th March 2018. Slides presented on Labour productivity methodological changes, Regional labour productivity, Review of international best practice in the production of productivity statistics and Multi-factor productivity
Why stress on estimating quantum, in studies concerning black money in the co...D Murali ☆
Why stress on estimating quantum, in studies concerning black money in the country? - T.N. Pandey - Article published in Business Advisor, dated August 10, 2014 http://www.magzter.com/IN/Shrinikethan/Business-Advisor/Business/
Financial Management & Budgeting for Vacation Rental Companies by Ben Edwards, President Weatherby Consulting. Includes info about cash flow, income statements, reporting, revenue projections, and accounting.
Financial Management & Budgeting for Vacation Rental Companies by Ben Edwards, President Weatherby Consulting. Includes info about cash flow, income statements, reporting, revenue projections, and accounting.
ChapterTool KitChapter 1212912Corporate Valuation and Financial .docxmccormicknadine86
ChapterTool KitChapter 1212/9/12Corporate Valuation and Financial Planning12-2 Financial Planning at MicroDrive, Inc.The process used by MicroDrive to forecast the free cash flows from its operating plan is described in the sections below.Setting Up the Model to Forecast OperationsWe begin with MicroDrive's most recent financial statements and selected additional data.Figure 12-1 MicroDrive’s Most Recent Financial Statements (Millions, Except for Per Share Data)INCOME STATEMENTSBALANCE SHEETS20122013Assets20122013Net sales$ 4,760$ 5,000Cash$ 60$ 50COGS (excl. depr.)3,5603,800ST Investments40-Depreciation170200Accounts receivable380500Other operating expenses480500Inventories8201,000EBIT$ 550$ 500Total CA$ 1,300$ 1,550Interest expense100120Net PP&E1,7002,000Pre-tax earnings$ 450$ 380Total assets$ 3,000$ 3,550Taxes (40%)180152NI before pref. div.$ 270$ 228Liabilities and equityPreferred div.88Accounts payable$ 190$ 200Net income$ 262$ 220Accruals280300Notes payable130280Other DataTotal CL$ 600$ 780Common dividends$48$50Long-term bonds1,0001,200Addition to RE$214$170Total liabilities$ 1,600$ 1,980Tax rate40%40%Preferred stock100100Shares of common stock5050Common stock500500Earnings per share$5.24$4.40Retained earnings800970Dividends per share$0.96$1.00Total common equity$ 1,300$ 1,470Price per share$40.00$27.00Total liabs. & equity$ 3,000$ 3,550The figure below shows all the inputs required to project the financial statements for the scenario that has been selected with the Scenario Manager: Data, What-If Analysis, Scenario Manager. There are two scenarios. The first is named Status Quo because all operating ratios except the sales growth rate are assumed to remain unchanged. The initial sales growth rate was chosen by MicroDrive's managers based on the existing product lines. The growth rate declines over time until it eventually levels off at a sustainable rate. The other scenario is named Final because it is the set of inputs chosen by MicroDrive's management team.Section 1 shows the inputs required to estimate the items in an operating plan. For each of these inputs, Section 1 shows the industry averages, the actual values for the past two years for MicroDrive, and the forecasted values for the next five years. The managers assumed the inputs for future years (except the sales growth rate) would be equal to the inputs in the first projected year.MicroDrive's managers assume that sales will eventually level off at a sustaniable constant rate.Sections 2 and 3 show the data required to estimate the weighted average cost of capital. Section 4 shows the forecasted growth rate in dividends.Note: These inputs are linked throughout the model. If you want to change an input, do it here and not other places in the model.Figure 12-2MicroDrive's Forecast: Inputs for the Selected ScenarioStatus QuoIndustryMicroDriveMicroDriveInputsActualActualForecast1. Operating Ratios2013201220132014201520162017201 ...
San Donato Milanese, 29 July 2015 – The Snam Board of Directors, chaired by Lorenzo Bini Smaghi, yesterday approved the consolidated half-year report to 30 June 2015 (subjected to a limited audit) and the consolidated results for the second quarter of 2015 (unaudited).
Financial highlights
Total revenue: €1,837 million (+3.1%)
EBITDA: €1,434 million (+0.4%)
Net profit: €612 million (+9.1%)
Technical investments: €487 million
Free cash flow: €587 million
Operating highlights
Gas injected into the transportation network: 32.77 billion cubic metres, in line with the figure for the first half of 2014
Number of active meters: 6.518 million (5.911 million at 30 June 2014)
Available storage capacity: 11.4 billion cubic metres (unchanged compared with 30 June 2014)
Significant events
Completed on 9 July 2015 the activities relating to the revocation of the judicial administration order imposed on the subsidiary Italgas by the Court of Palermo on 11 July 2014
Approved on 22 June 2015 by the Snam Board of Directors the renewal of the Euro Medium Term Notes (EMTN) programme for the issuance of bonds worth a total of €12 billion, unchanged from the previous renewal of the programme
Thailand UNDP-GIZ workshop on CBA - Enhancing resilience in Thailand through ...UNDP Climate
Thailand, 27-28 November 2017 - UNDP and GIZ partnered with the Thailand Office of Agriculture Economics (OAE) to launch a workshop designed to connect vital stakeholders to build an effective National Adaptation Plan.
The two-day workshop at the Rama Garden Hotel had 20 participants from each department under the Ministry of Agriculture and Cooperatives (MOAC). The workshop was designed to build capacity of planning officers to formulate better projects and budget submissions as well as potential climate finance proposal using cost-benefit analysis and ecosystem-based analysis appraisal tools.
4. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Introduction
Motivation
Simple Regression Approach :
(Trehan (1992,1996), Parigi & Schlitzer (1995), Bovi et al (2000). Camba-
Mendez et al (2001), Irac & Sedillot (2002) and Mourougma & Roma (2002))
Factor Based Model:
(Stock and Watson (1998,2002a, 2002b), Forni and Reichlin (1998), Forni,
Lippi, Hallin and Reichlin (2001a)).
Size and the composition of the data and its impact on factor
estimates Boivin and Ng (2006).
To date, the majority of empirical studies on early estimates of
GDP have focused on developed countries such as UK, USA and
Euro area.
4 / 19
6. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Model
Simple Regression Model
∆yt = c + Σp
i=1αi∆yt−i + Σp
i=0Σk
j=1βixt−i, j + ut (1)
yt ⇒ log of Bahrain GDP
xi,j ⇒ j-th indicator variable (j=1,2,.....k) in logs
c ⇒ intercept
p ⇒ number of lags
∆ ⇒ 1st difference operator
ut ⇒ disturbance ∼ N(0, σ2)
All possible combinations of the q = k(p + 1) + p indicators are used as possible models.
This leads to the constructions of s
i=1
q!
(q−i)!i!
possible models which is 1159 models in
our case
6 / 19
14. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Empirical Work and Results
Evaluating Forecast Performance
Evaluating point forecast using root mean square forecast
error RMSFE, residual standard deviation (RSD) and
Pesaran and Timmerman (1992) test.
Utilize the corrected Diebold Mariano (1995) test of Harvey
et al (1997), to evaluate weather two different forecast
models are significantly different from each other or not
using models loss function.
Evaluation of density forecasts using Diebold et al (1998)
test based on probability integral transform (PIT). Testing
standard normality of the cumulative density function (CDF)
using Doornik and Hansen (1994) (DH) test as suggested by
Clements and Smith (2000) and examin independence in the
PIT using Ljung- Box test .
14 / 19
18. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Conclusion
Conclusion
The most reliable estimates achieved using simple regression
estimates augmented with intercept correction mode
(3IV/IC). However, it can be considered only if the forecaster
concern about the point forecast.
3IV and SIV/IC are good choices and pass point and density
forecasts.
We can shorten the lag of the official estimates by one week.
Our results support Boivin and Ng (2006) argument, which
says that more information does not always help to produce
more accurate results.
18 / 19